Entity Topical Network Analyzer — Assessing Topical Authority Through Entity Reinforcement and Structural Consistency

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The Entity Topical Network Analyzer is a structured analytical system designed to evaluate how effectively a webpage establishes, reinforces, and sustains topical authority through its use of entities across content sections. The project moves beyond surface-level keyword evaluation and instead analyzes the semantic relationships between entities, sections, and target queries to determine thematic clarity and authority signals.

The system processes one or multiple URLs and multiple target queries in a single pipeline. Each page is decomposed into meaningful content sections, from which named entities are extracted, normalized, and semantically evaluated. These entities are then filtered, aligned with section and query semantics, classified into functional roles, and scored based on their reinforcement behavior across the page. The outcome is a structured understanding of how entities collectively form a topical network that supports or weakens the page’s thematic focus.

At its core, the project transforms raw content into an explainable entity-driven representation of topical authority. It identifies which entities act as core thematic anchors, which ones reinforce the topic contextually, and which contribute little or introduce noise. By constructing a pruned topical network from these entities, the system provides a clear view of thematic coherence, reinforcement depth, and structural consistency across sections.

The final outputs are designed to be interpretable and actionable. Section-level diagnostics, page-level reinforcement metrics, and a simplified entity network collectively highlight strengths, gaps, and inconsistencies in topical coverage. This enables informed decisions around content refinement, entity enrichment, and structural optimization, all grounded in measurable semantic signals rather than intuition or isolated keyword metrics.

This project functions as a standalone analytical framework and is suitable for real-world content evaluation workflows where clarity, explainability, and practical insight into topical authority are essential.

Project Purpose

The purpose of the Entity Topical Network Analyzer — Assessing Topical Authority Through Entity Reinforcement and Structural Consistency is to provide a reliable and explainable method for evaluating how well a webpage communicates topical authority through its underlying entity structure. Rather than relying on isolated keywords or surface-level content signals, the project focuses on how entities are distributed, reinforced, and interconnected across sections to support a coherent thematic narrative.

This project is designed to address a common challenge in content evaluation: understanding whether a page truly covers a topic in depth or merely mentions related terms without meaningful reinforcement. By analyzing entity presence at both section and page levels, the system distinguishes between intentional topical coverage and incidental entity mentions. This allows for a more accurate assessment of thematic strength and structural alignment.

Another core objective is to expose gaps and inconsistencies in topical reinforcement. Pages may appear comprehensive in length but still suffer from weak entity continuity, fragmented coverage, or over-reliance on contextual or low-impact entities. The project surfaces these issues through explicit diagnostics and reinforcement metrics, making it easier to identify where topical authority breaks down within the content structure.

The project also aims to support scalable, real-world analysis. It is built to handle multiple URLs and multiple queries within a single pipeline, ensuring that comparisons and evaluations remain consistent across different pages and topical intents. All intermediate and final outputs are structured, traceable, and interpretable, making the analysis suitable for decision-making rather than experimentation alone.

Ultimately, the project’s purpose is to convert complex semantic signals into clear insights about topical authority, entity relevance, and structural consistency. The results enable targeted content improvements that strengthen thematic clarity, improve semantic reinforcement, and align content structure with the intended topical focus.

Project’s Key Topics: Explanation and Understanding

This section explains the core concepts and analytical foundations that underpin the Entity Topical Network Analyzer. Understanding these topics is essential for interpreting how the system evaluates content and how its outputs should be used.

Entity-Centric Content Analysis

At the heart of the project lies an entity-centric view of content. An entity represents a distinct, identifiable concept such as an organization, product, technology, location, or process that carries semantic meaning beyond individual words. Unlike keywords, entities are context-aware and remain stable across variations in phrasing.

The project extracts named entities from each content section and treats them as primary semantic building blocks. This approach allows the analysis to focus on meaning and topical relevance rather than exact keyword matching. By working at the entity level, the system can evaluate whether a page meaningfully covers a topic or simply mentions related terms without depth.

Topical Authority

Topical authority refers to the extent to which a page demonstrates comprehensive, consistent, and focused coverage of a subject area. In this project, topical authority is not inferred from backlinks or external signals, but from internal content structure and semantic reinforcement.

A page with strong topical authority shows repeated, coherent use of relevant entities across multiple sections. Core entities appear consistently, are reinforced in different contexts, and are closely aligned with the intended queries. Weak topical authority, by contrast, is characterized by fragmented entity usage, shallow mentions, or over-reliance on loosely related concepts.

Entity Reinforcement

Entity reinforcement describes how strongly an entity contributes to the page’s topical narrative. This is evaluated using multiple signals rather than a single metric. Reinforcement depends on how often an entity appears, where it appears, how semantically aligned it is with the surrounding section, and how well it aligns with the target queries.

An entity mentioned repeatedly but confined to a single section may carry less reinforcement value than one that appears meaningfully across multiple sections. The project captures this behavior by measuring section coverage, semantic similarity, and positional consistency, ensuring that reinforcement reflects true topical contribution rather than raw frequency.

Structural Consistency

Structural consistency refers to how evenly and logically topical signals are distributed across the content structure. A well-structured page maintains thematic continuity from introduction to conclusion, with entities reinforcing the topic across sections rather than clustering randomly.

The project explicitly models structural consistency by evaluating entity presence section by section. It identifies sections that lack viable entities, highlights uneven distribution patterns, and flags pages where topical signals are concentrated in isolated areas. This allows structural weaknesses to be detected even when the overall content length appears sufficient.

Query–Entity Semantic Alignment

Queries represent the intended topical focus of the analysis. The project evaluates how closely each extracted entity aligns with the semantic intent of the provided queries using embedding-based similarity. This ensures that entity relevance is measured in meaning space rather than through lexical overlap.

Entities that strongly align with one or more queries are considered higher-value contributors to topical authority. Entities with weak or negative alignment are treated cautiously, even if they appear frequently, as they may dilute or distract from the core topic.

Entity Role Classification

Entities do not contribute equally to topical authority. The project classifies entities into functional roles based on their relative semantic importance within the page. These roles are derived from the distribution of relevance scores rather than fixed thresholds, making the classification adaptive to each page.

Core entities act as primary thematic anchors and define the subject matter. Reinforcing entities support and expand the core topic by adding depth or context. Contextual entities may appear naturally in content but contribute little to topical authority. This role-based view allows for a nuanced understanding of content composition.

Topical Network Representation

To capture relationships between entities, the project constructs a topical network where entities are represented as nodes and meaningful relationships as edges. Edge strength reflects both semantic similarity and co-presence across sections, ensuring that only substantively connected entities are linked.

This network is intentionally pruned to avoid visual and analytical noise. The resulting structure provides a high-level view of how entities interact to form a coherent topic, revealing clusters, gaps, and weak connections that may not be visible through linear analysis.

Together, these key topics form the conceptual foundation of the project. They explain how the system interprets content, evaluates topical authority, and produces structured, explainable outputs that reflect both semantic depth and structural integrity.

Questions and Answers: Understanding Project Value and Importance

What problem does this project solve that traditional content analysis cannot?

Traditional content analysis often relies on keyword frequency, surface-level relevance, or isolated SEO signals. These approaches fail to explain whether a page genuinely communicates topical depth or simply references related terms. This project addresses that gap by evaluating content at the entity level and examining how entities are reinforced across sections. It reveals whether a topic is structurally supported throughout the page or concentrated in isolated areas, providing clarity that keyword-based methods cannot deliver.

Why is an entity-based approach more reliable than keyword-based evaluation?

Keywords are sensitive to phrasing and repetition, which can be manipulated without improving content quality. Entities represent stable concepts that carry meaning across variations in language. By focusing on entities, the project evaluates semantic intent rather than textual coincidence. This leads to a more accurate assessment of relevance, topical coverage, and authority, especially for complex or multi-faceted topics.

How does the project determine whether a page has strong topical authority?

Topical authority is evaluated through multiple interconnected signals. The system examines how core entities are distributed across sections, how consistently they align with the intended queries, and how strongly they reinforce each other semantically. Pages with strong authority exhibit repeated, meaningful entity reinforcement across the content structure, while weaker pages show fragmented or inconsistent entity usage.

What makes the project’s outputs actionable rather than purely analytical?

The project does not stop at scoring or classification. It produces section-level diagnostics, entity role classifications, reinforcement metrics, and a pruned topical network. These outputs explicitly indicate where topical strength exists and where it breaks down. This allows content decisions to be targeted, such as strengthening under-represented sections, consolidating redundant entities, or reinforcing core concepts more consistently.

Why is section-level analysis important for understanding content quality?

Page-level metrics can mask internal inconsistencies. A page may appear authoritative overall while containing sections that are off-topic or weakly supported. Section-level analysis exposes these issues by evaluating entity presence and relevance within each structural unit. This helps identify sections that dilute topical focus or fail to contribute meaningfully to the overall narrative.

What is the practical benefit of classifying entities into roles?

Role classification provides a functional view of content composition. Core entities define the main topic, reinforcing entities add depth and context, and contextual entities represent background noise. Understanding these roles helps distinguish essential content elements from expendable ones, supporting decisions around content consolidation, expansion, or refinement.

How does the topical network enhance understanding beyond lists of entities?

Lists show what entities exist but not how they interact. The topical network reveals relationships based on semantic similarity and co-occurrence across sections. This highlights clusters of related concepts, exposes weak or missing connections, and provides a structural view of thematic coherence. It offers insights that linear representations cannot capture.

Is the analysis explainable and suitable for decision-making?

Yes. All classifications and scores are derived from transparent, interpretable signals such as semantic similarity distributions, section coverage ratios, and reinforcement metrics. There are no opaque black-box decisions. This makes the analysis suitable for informed decision-making rather than exploratory experimentation.

How does this project support scalable, real-world content evaluation?

The system is built as a modular, multi-URL, multi-query pipeline. It applies consistent logic across pages while adapting thresholds dynamically based on content distributions. This allows it to scale across large content sets without sacrificing interpretability or analytical rigor.

Code Explanations

This section explains the libraries used in the project and their role in the overall implementation. Each library is included deliberately to support reliability, scalability, interpretability, and professional-grade execution within a notebook environment.

Standard Python Libraries

The project uses core Python libraries such as time, re, html, hashlib, unicodedata, gc, collections, logging, and typing. These libraries provide fundamental capabilities for string processing, text normalization, hashing, memory management, structured logging, and type safety.

These libraries form the backbone of the pipeline. Text normalization and cleanup rely heavily on regular expressions, HTML unescaping, and Unicode normalization to ensure consistent semantic inputs for downstream models. Hashing is used to generate stable identifiers for sections and entities, which is essential for traceability across phases. Garbage collection is explicitly triggered during multi-URL processing to manage memory usage in long-running notebook executions. Structured logging is implemented throughout the pipeline to provide transparent diagnostics, error handling, and execution traceability, which is critical for real-world usage. Type hints improve code clarity, maintainability, and reduce ambiguity in complex data structures.

Requests

requests is a widely used Python library for making HTTP requests in a simple and reliable manner.

The library is used to fetch webpage HTML content with robust retry logic, timeout handling, and controlled request pacing. This ensures stable content extraction even under imperfect network conditions. The implementation supports headers, redirects, and encoding fallbacks, which is essential when working with real-world web pages that vary widely in structure and server behavior.

BeautifulSoup (bs4)

BeautifulSoup is a Python library for parsing HTML and XML documents and navigating their structure in a flexible way.

BeautifulSoup is used to clean and parse raw HTML content, remove non-informational elements such as scripts and navigation, and extract meaningful textual sections. Its DOM traversal capabilities allow the project to segment content based on headings and structural cues, which is crucial for section-level entity analysis. This ensures that semantic evaluation aligns with how content is actually organized on the page.

NumPy

NumPy is a foundational library for numerical computation in Python, providing efficient array operations and mathematical functions.

NumPy is used for vector-based operations, similarity computations, and numerical aggregations throughout the pipeline. It supports efficient handling of embedding vectors and statistical calculations used in reinforcement scoring, distribution-based role classification, and network construction.

Pandas

Pandas is a data manipulation and analysis library that provides tabular data structures and high-level operations.

Pandas is primarily used in the visualization and diagnostic phases to aggregate results, compute summary statistics, and prepare structured data for plotting. It enables clean transformation of nested result objects into analysis-ready formats without altering the underlying project data structures.

PyTorch

PyTorch is a deep learning framework that provides tensor operations and GPU acceleration.

PyTorch acts as the execution backend for all transformer-based models used in the pipeline. It enables efficient embedding generation and similarity computation, with automatic support for GPU acceleration when available. Its tensor-based operations ensure performance consistency during batch processing.

SentenceTransformers

SentenceTransformers is a library built on top of transformers that provides pretrained models for generating high-quality sentence and text embeddings.

This library is central to the project’s semantic analysis. It is used to generate embeddings for queries, sections, and entities in a consistent vector space. Built-in batching, cosine similarity utilities, and optimized inference make it suitable for large-scale, multi-query analysis without requiring custom tokenizer or model management. Using SentenceTransformer directly ensures cleaner, more maintainable code compared to low-level model handling.

Transformers (Hugging Face)

The transformers library provides access to pretrained transformer models and pipelines for a wide range of NLP tasks.

Transformers are used specifically for Named Entity Recognition through a pretrained NER pipeline. This allows the project to extract semantically meaningful entities from content without fine-tuning or heavy generative models. The pipeline abstraction simplifies inference while maintaining reliability and reproducibility.

Matplotlib

Matplotlib is a plotting library for creating static visualizations in Python.

Matplotlib provides low-level control over visual output, which is necessary for building clear, interpretable plots tailored to professional reporting. It is used as the foundational plotting engine for all visual diagnostics.

Seaborn

Seaborn is a statistical data visualization library built on top of Matplotlib.

Seaborn is used to enhance plot readability and consistency through clean styling and high-level plotting functions. It simplifies the creation of comparative visualizations such as distributions and bar charts while maintaining a professional appearance suitable for reporting and stakeholder interpretation.

Function: fetch_html

Overview

The fetch_html function is responsible for reliably retrieving raw HTML content from a given URL under real-world web conditions. It is designed to handle unstable network behavior, server-side throttling, encoding inconsistencies, and transient request failures, all of which are common when processing client-provided URLs at scale.

Rather than assuming a single successful request, the function implements a controlled retry mechanism with exponential backoff and request pacing. This ensures that temporary failures do not immediately terminate the pipeline while also avoiding aggressive request behavior that could lead to blocking or rate-limiting. The function returns validated HTML content only when a minimum content quality threshold is met, ensuring downstream processing receives meaningful input. If all retries fail, a clear runtime error is raised to allow the pipeline to handle the failure explicitly.

This approach makes the function suitable for production-grade analysis pipelines where robustness and predictability are essential.

Key Code Explanations

Headers configuration

headers = {
    “User-Agent”: (
        “Mozilla/5.0 (Windows NT 10.0; Win64; x64) “
        “AppleWebKit/537.36 (KHTML, like Gecko) “
        “Chrome/120.0 Safari/537.36”
    )
}

A realistic browser user-agent is explicitly defined to reduce the likelihood of request blocking or altered responses. Many servers treat non-browser user agents differently, so this improves consistency when fetching content from diverse websites.

Retry loop with controlled backoff

while attempt <= max_retries:
    if attempt == 0 and delay:
        time.sleep(delay)
    elif attempt > 0:
        time.sleep(backoff_factor ** attempt)

This logic introduces two safeguards. An initial delay helps avoid rapid-fire requests when processing multiple URLs, while exponential backoff progressively increases wait time after failures. This balances resilience with responsible request behavior.

HTTP request and status validation

resp = requests.get(
    url,
    headers=headers,
    timeout=timeout,
    allow_redirects=True
)
resp.raise_for_status()

The request explicitly allows redirects and enforces a timeout to prevent hanging requests. raise_for_status() ensures that HTTP errors are surfaced immediately rather than silently passing invalid responses downstream.

Defensive encoding handling

encodings = [
    getattr(resp, “apparent_encoding”, None),
    “utf-8”,
    “iso-8859-1”
]

Webpages often declare incorrect or inconsistent encodings. By attempting multiple encoding strategies, the function maximizes the chance of recovering readable HTML without manual intervention.

Content quality validation

if html and len(html.strip()) > 100:
    return html

This check prevents empty or near-empty responses from being treated as valid pages. It ensures that downstream parsing and semantic analysis operate on substantive content rather than boilerplate or error pages.

Function: clean_html

Overview

The clean_html function is responsible for parsing raw HTML into a structured format while removing high-noise elements that do not contribute to semantic understanding or topical analysis. Its primary goal is to retain meaningful content structure—such as headings, paragraphs, and list elements—while eliminating scripts, navigation components, advertisements, and other non-informational elements that can distort entity extraction and downstream semantic modeling.

The function is deliberately conservative. It does not flatten the document or aggressively strip structural tags. Instead, it focuses on removing elements that are universally recognized as noise in content analysis workflows. This ensures that section boundaries, hierarchical context, and content flow remain intact, which is essential for accurate entity role classification and topical network construction later in the pipeline.

Key Code Explanations

Robust HTML parsing strategy

try:

    soup = BeautifulSoup(html, “lxml”)

except Exception:

    soup = BeautifulSoup(html, “html.parser”)

The function first attempts to parse HTML using the lxml parser for better performance and structural accuracy. If lxml is unavailable or fails due to malformed markup, it gracefully falls back to Python’s built-in HTML parser. This defensive approach ensures consistent behavior across diverse page structures.

Explicit definition of high-noise tags

remove_tags = [

    “script”, “style”, “noscript”, “iframe”, “svg”, “canvas”,

    “header”, “footer”, “nav”, “form”, “input”,

    “aside”, “advertisement”, “img”, “video”, “audio”, “picture”

]

These tags commonly contain executable code, layout elements, or decorative media that do not represent topical content. Removing them prevents irrelevant text, duplicated navigation labels, and embedded media metadata from contaminating entity detection and semantic embeddings.

Safe decomposition of noisy nodes

for tag in remove_tags:

    for node in soup.find_all(tag):

        try:

            node.decompose()

        except Exception:

            pass

Each unwanted node is removed using decompose, which completely deletes the element and its children from the DOM tree. The operation is wrapped in a try–except block to prevent malformed HTML fragments from interrupting the cleaning process.

HTML comment removal

for c in soup.find_all(string=lambda x: isinstance(x, Comment)):

    try:

        c.extract()

    except Exception:

        pass

HTML comments often contain hidden scripts, tracking identifiers, or developer notes that are not visible to users but can appear in raw text extraction. Removing comments ensures that only user-facing content contributes to semantic analysis.

Function: _md5

Overview

The _md5 utility function generates a deterministic hash for a given text input. In this project, it is used to create stable, reproducible identifiers for sections derived from page content. These identifiers are not intended for interpretation but serve as internal references to uniquely distinguish sections across processing stages.

Because section content and headings can be long and repetitive, hashing provides a compact and consistent way to track sections without storing or exposing verbose text.

Function: normalize_text

Overview

The normalize_text function standardizes raw text extracted from HTML before any semantic processing. Its purpose is to eliminate superficial differences in formatting while preserving the actual linguistic content. This normalization step is critical for ensuring consistent behavior in downstream tasks such as embedding generation, entity extraction, and similarity computation.

The function performs HTML unescaping, Unicode normalization, and whitespace cleanup to produce clean, readable text that reflects what a user would actually read on the page.

Key Code Explanations

HTML entity decoding

text = html_lib.unescape(text)

This converts encoded HTML entities (such as &amp;, &nbsp;, or &lt;) into their readable character equivalents, ensuring that textual meaning is preserved during analysis.

Unicode normalization

text = unicodedata.normalize(“NFKC”, text)

Unicode normalization standardizes visually similar characters into a consistent representation. This is particularly important for avoiding false mismatches caused by different encodings of the same characters, which can otherwise affect entity matching and embedding similarity.

Whitespace consolidation

text = re.sub(r”[\r\n\t]+”, ” “, text)

text = re.sub(r”\s+”, ” “, text)

These lines collapse excessive whitespace, line breaks, and tabs into single spaces. This produces clean, continuous text suitable for semantic modeling without altering the original meaning.

Function: extract_sections

Overview

The extract_sections function segments a cleaned HTML document into logical content sections using heading-based boundaries. Each section represents a coherent topical unit consisting of a heading and its associated body text. This structural segmentation is foundational to the project, as entity extraction, topical reinforcement scoring, and network construction all operate at the section level.

When headings are not present or insufficient, the function gracefully falls back to paragraph-based grouping to ensure that meaningful sections are still created.

Key Code Explanations

Configurable heading detection

if heading_tags is None:

    heading_tags = [“h2”, “h3”, “h4”]

This allows flexible control over which heading levels define section boundaries. Mid-level headings are used by default because they typically represent meaningful topical divisions within articles.

Iterative DOM traversal

for node in body.descendants:

Traversing the DOM in document order ensures that section text is accumulated in the same sequence as it appears to readers, preserving contextual flow and positional relevance.

Section boundary handling

if name in heading_tags:

    if current and len(current[“raw_text”]) >= min_section_chars:

        …

When a new heading is encountered, the current section is finalized if it meets the minimum length requirement. This prevents short or shallow fragments from being treated as standalone sections.

Deterministic section identification

src = f”{current[‘heading’]}_{current[‘position’]}_{current[‘raw_text’][:80]}”

current[“section_id”] = _md5(src)

The section identifier is generated from a combination of heading text, position, and content prefix. This approach ensures stability across runs while remaining resilient to minor formatting changes.

Minimum content enforcement

if current and len(current[“raw_text”]) >= min_section_chars:

Sections must meet a minimum character threshold to be included. This avoids noise from short, low-information blocks and ensures that each section has sufficient semantic depth for reliable analysis.

Function: is_boilerplate

Overview

The is_boilerplate function identifies low-value or non-topical text segments that should be excluded from semantic and entity-based analysis. These typically include legal notices, navigation prompts, subscription prompts, and other repeated structural elements that do not contribute to topical authority or thematic clarity.

Filtering such boilerplate content at an early stage is essential to prevent noise from influencing entity extraction, similarity scoring, and downstream network construction.

Key Code Explanations

Boilerplate keyword screening

boilerplate_terms = [

    “privacy policy”, “terms of service”, “cookie policy”,

    “all rights reserved”, “©”, “contact us”, “subscribe”,

    “newsletter”, “powered by”

]

This list captures common phrases that frequently appear in footers, headers, or site-wide components. Their presence is a strong indicator that the text is not part of the core topical narrative.

Short-text exclusion

if len(lower.split()) < 6:

    return True

Very short text fragments rarely carry meaningful topical information. This check eliminates fragments such as single-line labels or UI remnants that could otherwise be mistakenly treated as content sections.

Context-aware boilerplate detection

if bp in lower and len(lower.split()) < max_words:

    return True

This condition ensures that even if boilerplate terms appear, the text is only excluded when it is relatively short. This avoids accidentally filtering long, legitimate discussions that may reference such terms in a meaningful context.

Function: build_page_data

Overview

The build_page_data function orchestrates the complete page ingestion pipeline, transforming a raw URL into a structured, analysis-ready representation. It performs HTML retrieval, content cleaning, section extraction, normalization, and boilerplate filtering in a single, cohesive workflow.

The resulting output serves as the foundational input for all subsequent phases, including entity extraction, topical reinforcement scoring, and network construction.

Key Code Explanations

End-to-end HTML acquisition

html = fetch_html(url,

                  request_timeout,

                  fetch_delay,

                  max_retries,

                  backoff_factor)

This line invokes the robust HTML fetching mechanism with retry logic and backoff control, ensuring reliable content acquisition even under unstable network conditions.

HTML cleaning and parsing

soup = clean_html(html)

Cleaning the HTML before extraction removes high-noise elements and preserves only meaningful content structure, enabling accurate section segmentation and text extraction.

Resilient title extraction

if soup.title:

    title = normalize_text(soup.title.get_text())

…

h1 = soup.find(“h1”)

The function attempts to extract the page title from multiple sources. If the <title> tag is unavailable or empty, it falls back to the primary heading. This ensures that every page has a usable title for reporting and diagnostics.

Section extraction and validation

raw_sections = extract_sections(

    soup,

    min_section_chars=min_section_chars

)

Sections are extracted using heading boundaries with a minimum length constraint. This enforces semantic depth and avoids fragmentary sections that would weaken entity-level analysis.

Boilerplate-aware section filtering

if not cleaned or is_boilerplate(cleaned):

    continue

Each section undergoes normalization and boilerplate screening. Only sections that are both meaningful and topical are retained, significantly improving the quality of downstream entity signals.

Structured page output

return {

    “url”: url,

    “title”: title or “Untitled Page”,

    “sections”: sections,

    “note”: None if sections else “no_valid_sections”

}

The function returns a clean, structured representation of the page, including metadata and validated sections. The note field provides a clear diagnostic signal when no usable sections are detected, enabling transparent reporting and interpretation.

Function: load_ner_pipeline

Overview

The load_ner_pipeline function is responsible for initializing and safely loading the Named Entity Recognition (NER) model used throughout the project. Named Entity Recognition is a foundational capability for this analysis, as it enables the identification of real-world entities such as organizations, products, technologies, and concepts that form the basis of topical networks.

This function is designed with production reliability in mind. It includes automatic device selection, logging for transparency, and graceful failure handling to ensure that the overall pipeline does not crash silently when model loading issues occur.

Key Code Explanations

Automatic device selection

device = device if device is not None else (0 if torch.cuda.is_available() else -1)

This line ensures that the model automatically utilizes GPU acceleration when available, falling back to CPU execution otherwise. This design allows the same codebase to run efficiently across different environments, from local machines to cloud notebooks, without manual reconfiguration.

NER pipeline initialization

ner = pipeline(

    task=”ner”,

    model=model_name,

    tokenizer=model_name,

    aggregation_strategy=”simple”,

    device=device

)

The Hugging Face pipeline abstraction is used to load the NER model and tokenizer together in a unified interface. The aggregation_strategy=”simple” parameter merges sub-token predictions into coherent entity spans, producing clean, human-readable entities rather than fragmented token-level outputs. This is essential for downstream steps such as entity normalization, frequency calculation, and semantic similarity analysis.

Function: is_entity_valid

Overview

The is_entity_valid function performs conservative validation on extracted entity candidates to remove obvious noise before further processing. Named Entity Recognition models often produce borderline or low-value outputs, especially in technical or instructional content. This function acts as a first quality gate, ensuring that only structurally meaningful entities are retained.

The validation logic is intentionally strict to prioritize precision over recall at this early stage, reducing downstream noise in entity salience filtering, role classification, and network construction.

Function: normalize_entity_text

Overview

The normalize_entity_text function standardizes entity surface forms into a consistent representation suitable for aggregation and comparison. Entity mentions can appear with inconsistent casing or spacing across sections, and normalization ensures that semantically identical entities are treated as the same node during frequency counting and reinforcement analysis.

This function applies minimal, conservative normalization to avoid altering semantic meaning while still improving consistency.

Function: extract_entities_from_section

Overview

The extract_entities_from_section function applies the NER model to a single content section and converts raw model predictions into a structured entity representation. This function is a core component of the entity extraction pipeline, bridging unstructured section text and structured entity data used throughout later phases.

The function is designed to be fault-tolerant, efficient, and aligned with real-world content variability, ensuring that extraction failures in one section do not interrupt overall page processing.

Key Code Explanations

Running NER inference safely

predictions = ner_pipeline(text)

This line executes the named entity recognition model on the section text. The inference is wrapped in a try–except block to ensure that unexpected model errors, text encoding issues, or length-related failures do not halt the pipeline. In case of failure, the function logs the issue and returns an empty result for the section.

Entity-level validation

if not is_entity_valid(entity_text, label):

    continue

Each raw prediction is passed through the validation function before being accepted. This step removes short tokens, numeric-only strings, and unsupported entity types early, preventing noisy entities from propagating into salience scoring and semantic similarity calculations.

Entity normalization for aggregation

normalized = normalize_entity_text(entity_text)

Normalization ensures that multiple mentions of the same entity are grouped consistently across sections. This normalized form is later used as the basis for frequency counting, section coverage measurement, and reinforcement scoring.

Structured entity output

entities.append({

    “text”: entity_text,

    “normalized_text”: normalized,

    “label”: label,

    “start”: pred.get(“start”),

    “end”: pred.get(“end”)

})

Each valid entity is stored with both its original surface form and normalized representation, along with positional metadata. Retaining character offsets allows for future extensions such as highlighting, positional consistency analysis, or section-level diagnostics without reprocessing the raw text.

Function: generate_entity_id

Overview

The generate_entity_id function creates a deterministic identifier for each unique entity based on its normalized text and entity label. This identifier ensures that the same real-world entity is consistently referenced across sections, reinforcement scoring, and network construction.

Using deterministic hashing rather than random IDs guarantees reproducibility and stability across runs, which is essential for professional analytical reporting.

Key Code Explanations

Deterministic hashing

key = f”{normalized_text}::{label}”

return hashlib.md5(key.encode(“utf-8”)).hexdigest()

By combining normalized entity text with its semantic label and hashing the result, the function produces a compact, stable identifier that uniquely represents the entity while remaining independent of section position or extraction order.

Function: aggregate_section_entities

Overview

The aggregate_section_entities function consolidates raw entity mentions within a single section into frequency-based entity summaries. Rather than treating every mention independently, this function builds a section-level view of entity presence and prominence.

Aggregation is a crucial step for enabling salience filtering, reinforcement scoring, and network analysis, all of which rely on frequency and distribution signals rather than raw mention counts alone.

Key Code Explanations

Bucket-based aggregation

bucket: Dict[Tuple[str, str], Dict[str, Any]] = defaultdict(

    lambda: {“frequency”: 0, “spans”: []}

)

This structure groups entity mentions by normalized text and label, allowing efficient accumulation of frequency and character span information during iteration.

Frequency and span accumulation

bucket[key][“frequency”] += 1

bucket[key][“spans”].append((ent[“start”], ent[“end”]))

Each occurrence of an entity contributes to its overall frequency and positional metadata. These signals later inform entity salience, section coverage, and positional consistency metrics.

Aggregated entity construction

aggregated.append({

    “entity_id”: generate_entity_id(norm_text, label),

    “normalized_text”: norm_text,

    “label”: label,

    “frequency”: data[“frequency”],

    “char_spans”: data[“spans”]

})

The final aggregated structure produces a clean, section-level entity representation that includes identity, frequency, and positional context. This structured output becomes the foundation for all subsequent phases, including semantic alignment, role classification, and topical network construction.

Function enrich_page_with_entities

Overview

This function processes all sections of a page and enriches each section with extracted named entities. The entities are obtained using a pre-loaded Named Entity Recognition (NER) pipeline. Each section’s entities field is populated with structured information about the entities, including their normalized form, label, frequency, and character spans. The function ensures that even if some sections are missing, the page data remains intact.

The function iterates through each section, applies the extract_entities_from_section function, aggregates entity mentions using aggregate_section_entities, and updates the section dictionary with a comprehensive list of entities.

Key Code Explanations

raw = extract_entities_from_section(text, ner_pipeline)

section[“entities”] = aggregate_section_entities(raw)

• extract_entities_from_section(text, ner_pipeline): Uses the NER pipeline to detect entity mentions in the section text. Each entity is validated and normalized before inclusion.
• aggregate_section_entities(raw): Combines multiple mentions of the same entity within the section into a single structured entity record, computing frequency and collecting all character span positions. This ensures consistent representation of entities across the section.

Function load_embedding_model

Overview

This function loads a SentenceTransformer model, which is used to generate embeddings for text such as sections, entities, and queries. SentenceTransformer models are optimized for semantic similarity and can process text in batches, making them suitable for large-scale content analysis. The function automatically detects whether a GPU is available and loads the model on the appropriate device, ensuring efficient computation.

It provides a safe wrapper around the model loading process with logging for monitoring progress and exception handling to prevent pipeline failures if the model cannot be loaded.

Key Code Explanations

device = device or (“cuda” if torch.cuda.is_available() else “cpu”)
model = SentenceTransformer(model_name, device=device)

• Device selection: Automatically chooses GPU if available, otherwise CPU, ensuring optimal performance without manual configuration.
• SentenceTransformer(model_name, device=device): Instantiates the embedding model on the chosen device, ready for computing semantic embeddings for sections, entities, and queries in downstream processing.

Function: embed_texts

Overview

The embed_texts function generates dense vector embeddings for a list of input texts using a SentenceTransformer model. Embeddings represent the semantic content of each text in a high-dimensional space, allowing similarity comparisons between queries, sections, or entities. Batch processing is used to optimize GPU/CPU utilization and prevent memory issues when encoding a large number of texts.

This function is critical for downstream tasks such as query alignment, entity salience filtering, reinforcement scoring, and network construction, as all semantic similarity computations rely on these embeddings.

Key Code Explanations

Batch size adjustment

batch_size = min(batch_size, len(texts))

• Ensures the batch size does not exceed the number of texts, preventing unnecessary overhead and errors during encoding.

Embedding generation

embeddings = model.encode(
    texts,
    batch_size=batch_size,
    convert_to_tensor=True,
    normalize_embeddings=True,
    show_progress_bar=False
)

• convert_to_tensor=True returns a PyTorch tensor, which allows efficient similarity computation using matrix operations.
• normalize_embeddings=True scales each embedding to unit length, enabling cosine similarity to be computed via simple dot product.
• show_progress_bar=False suppresses verbose output for clean logging in production pipelines.

Function: compute_entity_query_similarity

Overview

The compute_entity_query_similarity function calculates cosine similarity between entity embeddings and query embeddings for multiple queries. Each entity receives a similarity score per query, indicating how semantically aligned it is with each query. These scores are essential for entity role classification, query alignment filtering, and downstream reinforcement scoring.

The function outputs a list of dictionaries where each dictionary represents an entity and maps queries to their corresponding similarity scores. This structure facilitates easy integration with section-level and page-level entity analysis.

Key Code Explanations

Cosine similarity computation

similarity_matrix = util.cos_sim(entity_embeddings, query_embeddings) # E x Q

• entity_embeddings is a tensor of shape E x D (entities × embedding dimension).
• query_embeddings is a tensor of shape Q x D (queries × embedding dimension).
• The resulting similarity_matrix has shape E x Q, where each row contains cosine similarity scores between a single entity and all queries.

Mapping scores to queries

per_query_scores = {
    queries[i]: float(row[i].item())
    for i in range(len(queries))
}

• Converts tensor values to Python floats for readability and downstream usage.
• Associates each similarity score with its corresponding query string.

The function returns a list of per-entity similarity dictionaries, providing a direct mapping between each entity and its alignment with all queries.

Function: annotate_section_entities_with_query_alignment

Overview

The annotate_section_entities_with_query_alignment function attaches query-level alignment scores to each entity within a section. By computing the semantic similarity between entities and queries, this function enriches the entity metadata with quantitative alignment information, which is later used for entity role classification and salience filtering.

This ensures that each entity has a clear measure of relevance to the queries of interest, supporting transparent, data-driven insights into which entities reinforce the page’s topical focus.

Key Code Explanations

Embedding section entities

entity_embeddings = embed_texts(entities_texts, embedding_model)

• Converts normalized entity texts into vector embeddings using the SentenceTransformer model.
• These embeddings represent the semantic meaning of entities in a high-dimensional space.

Computing per-query similarity

per_entity_scores = compute_entity_query_similarity(
    entity_embeddings=entity_embeddings,
    query_embeddings=query_embeddings,
    queries=queries
)

• Produces a list of dictionaries mapping each query to its similarity score for a given entity.
• Provides the foundation for role assignment based on alignment strength.

Annotating entities with scores

for entity, scores in zip(entities, per_entity_scores):
    entity[“query_alignment”] = scores

• Updates each entity dictionary in-place with a query_alignment field.
• Enables downstream functions to filter or classify entities based on query relevance.

Function: enrich_page_entities_with_query_alignment

Overview

The enrich_page_entities_with_query_alignment function applies per-query semantic alignment scoring across all sections of a page. For each entity in every section, it attaches a query alignment dictionary that quantifies how closely the entity relates to each target query.

This function acts as a wrapper over the section-level alignment, ensuring that all entities on the page are consistently annotated with relevance scores, which is essential for subsequent salience filtering, role classification, and reinforcement scoring.

Key Code Explanations

Iterating through sections

for section in page_data.get(“sections”, []):
    annotate_section_entities_with_query_alignment(
        section=section,
        queries=queries,
        query_embeddings=query_embeddings,
        embedding_model=embedding_model
    )

• Processes each section independently, applying query alignment at a granular level.
• Ensures that entities in all sections have consistent relevance metrics.

Function: filter_section_entities_by_salience

Overview

The filter_section_entities_by_salience function applies multi-criteria filtering to entities within a single section. Its goal is to retain only high-value entities that meaningfully contribute to the page’s topical analysis.

Filtering is based on three main criteria:

1. Frequency threshold – Entities must appear sufficiently often in the section.
2. Section semantic similarity – Entities must be relevant to the section’s content based on embeddings.
3. Query relevance – Entities must align with at least one of the target queries.

The filtered entities are sorted by strongest query alignment and capped to a configurable maximum per section. The function also computes a viability flag for the section, indicating whether it contains enough meaningful entities for analysis.

Key Code Explanations

Embedding entities and computing similarity

entity_embeddings = embed_texts(entities_texts, embedding_model)
section_similarity = compute_entity_section_similarity(ent_emb, section_embedding)

• Converts entity texts into embeddings to capture semantic meaning.
• Computes cosine similarity between each entity and the section to quantify relevance beyond simple string matching.

Multi-threshold filtering

if not passes_frequency_threshold(entity, min_frequency):
    continue
if section_similarity < min_section_similarity:
    continue
if not passes_query_relevance_threshold(entity, min_query_similarity):
    continue

• Ensures only entities that satisfy all three salience checks are retained.
• This multi-stage filtering prevents noisy or irrelevant entities from affecting downstream analysis.

Sorting and capping entities

retained_entities.sort(key=lambda e: max(e[“query_alignment”].values()), reverse=True)
section[“filtered_entities”] = retained_entities[:max_entities_per_section]

• Prioritizes entities with strongest alignment to queries.
• Caps the number per section to prevent overrepresentation, ensuring clarity in thematic analysis.

Section-level viability flag

section[“entity_viability”] = {
    “raw_entity_count”: len(section[“entities”]),
    “filtered_entity_count”: len(section[“filtered_entities”]),
    “viable”: len(section[“filtered_entities”]) >= MIN_ENTITIES_PER_SECTION
}

• Provides a diagnostic metric to indicate whether a section has enough salient entities for reliable analysis.
• Supports reporting and visualization decisions downstream.

Function: apply_entity_salience_filtering

Overview

The apply_entity_salience_filtering function executes section-level entity filtering across an entire page. It ensures that only highly relevant and semantically meaningful entities are retained for subsequent analysis, including role classification, reinforcement scoring, and network construction.

Key responsibilities of the function include:

• Embedding all sections to capture semantic meaning.
• Filtering entities per section using multi-criteria salience rules (frequency, section similarity, query alignment).
• Computing coverage diagnostics to assess how many sections contain viable entities.
• Flagging pages with low entity density, which may limit the reliability of entity-based topical analysis.

This function centralizes the salience logic and provides both the filtered entities and diagnostic metrics, making it critical for downstream analysis and reporting.

Key Code Explanations

Embedding sections for semantic filtering

section_embeddings = embed_sections(sections, embedding_model)

• Converts each section’s text into embeddings using a SentenceTransformer model.
• Enables semantic comparison between entities and sections, rather than relying solely on string matching.

Per-section entity filtering

for section, sec_emb in zip(sections, section_embeddings):
    filter_section_entities_by_salience(
        section=section,
        section_embedding=sec_emb,
        embedding_model=embedding_model,
        …
    )

• Iterates over each section and applies the multi-threshold filtering defined in filter_section_entities_by_salience.
• Retains entities that satisfy frequency, section similarity, and query alignment criteria.

Page-level coverage diagnostics

page_data[“entity_coverage_diagnostics”] = compute_page_entity_coverage(sections)

• Summarizes entity distribution across sections.
• Provides coverage_ratio and the number of sections with viable entities, giving a quantitative measure of page entity density.

Low-density page note

if page_data.get(“entity_coverage_diagnostics”, {}).get(“coverage_ratio”, 0) < 0.3:
    page_data[“analysis_note”] = “Low named-entity density detected…”

• Automatically flags pages with insufficient entity representation.
• Supports interpretation and visualization, alerting users to potential limitations in entity-based analysis.

Function: collect_unique_entities

Overview

The collect_unique_entities function aggregates all filtered entities from every section of a page into a unique set. Its primary purpose is to provide a consolidated view of the entities present in a page, eliminating duplicates across sections.

This is a preparatory step for role classification, reinforcement scoring, and network construction, where a single representation per entity is required. By focusing only on filtered entities, the function ensures that downstream processes operate on high-quality, relevant entities rather than all extracted mentions.

Key Code Explanations

Iterating through sections and entities

for section in page_data.get(“sections”, []):
    for entity in section.get(“filtered_entities”, []):

• Loops over all sections of the page.
• Only considers filtered_entities, ensuring irrelevant or low-salience entities are excluded.

Collecting unique entities

eid = entity.get(“entity_id”)
if eid not in entities and entity.get(“normalized_text”) is not None:
    entities[eid] = entity.get(“normalized_text”)

• Uses the deterministic entity ID (entity_id) as the key to avoid duplicates.
• Maps the ID to the normalized entity text, providing a readable reference for downstream scoring and network visualization.
• Returns a dictionary of unique entity IDs and their normalized text, simplifying entity-level analyses across the page.

Function: compute_entity_embeddings

Overview

The compute_entity_embeddings function generates vector representations (embeddings) for each unique entity in a page. Embeddings provide a numerical, semantic representation of entities, enabling similarity calculations, reinforcement scoring, and network construction.

By using a pre-trained SentenceTransformer model, this function ensures that entities are embedded in a high-dimensional semantic space, allowing meaningful comparisons across sections and queries. The function returns a dictionary mapping entity IDs to their embeddings, which can be directly used in downstream computations.

Key Code Explanations

Preparing input texts and generating embeddings

entity_ids = list(entity_text_map.keys())
texts = [entity_text_map[eid] for eid in entity_ids]
embeddings = embed_texts(texts, model, batch_size)

• Converts the entity_text_map into a list of texts to feed into the embedding model.
• Calls the embed_texts function to compute embeddings efficiently in batches, which is essential for handling pages with many entities without exhausting memory.

Mapping embeddings back to entity IDs

return {
    eid: embeddings[idx]
    for idx, eid in enumerate(entity_ids)
}

• Creates a dictionary linking each entity ID to its embedding vector, preserving entity identity for semantic comparisons and further analysis.
• Ensures a one-to-one mapping between entity and embedding for consistent downstream use.

Function: build_entity_similarity_matrix

Overview

The build_entity_similarity_matrix function constructs a pairwise semantic similarity map between all entities on a page. Using cosine similarity between embeddings, it identifies semantically related entities that can reinforce each other in the context of topical authority and network analysis.

Only significant relationships exceeding a min_similarity threshold are retained, resulting in a sparse, symmetric matrix that prevents noise and unnecessary computational overhead. This matrix is essential for entity role reinforcement scoring and network construction.

Key Code Explanations

Stack embeddings and compute pairwise similarity

embedding_tensor = torch.stack(
    [entity_embeddings[eid] for eid in entity_ids]
)
similarity_matrix = util.cos_sim(embedding_tensor, embedding_tensor)

• Converts the entity embeddings dictionary into a single tensor for efficient computation.
• Uses util.cos_sim from sentence-transformers to calculate all pairwise cosine similarities, producing an NxN matrix where N is the number of entities.

Filter and store only significant relationships

if score >= min_similarity:
    matrix.setdefault(eid_a, {})[eid_b] = score
    matrix.setdefault(eid_b, {})[eid_a] = score

• Only retains pairs of entities with similarity above the specified threshold, keeping the matrix sparse.
• Ensures the matrix is symmetric, reflecting the bidirectional nature of semantic relatedness.
• Using setdefault efficiently initializes nested dictionaries for storing scores without overwriting existing values.

Return the sparse similarity matrix

• Returns a dictionary of dictionaries, mapping each entity ID to other entity IDs with which it shares meaningful similarity.
• Provides the foundational data structure for reinforcement scoring and topical network construction.

Function: get_entity_similarity_matrix

Overview

The get_entity_similarity_matrix function serves as a wrapper that orchestrates the complete workflow for generating the entity–entity similarity matrix for a page. It consolidates the processes of collecting unique entities, computing embeddings, and building the similarity matrix into a single, reusable function.

This function simplifies downstream usage, allowing other phases (like entity role classification or topical network construction) to obtain the similarity matrix without manually managing intermediate steps.

Key Code Explanations

Collect unique entities from the page

unique_entities = collect_unique_entities(page_data)

• Extracts all filtered entities across sections, ensuring each entity is represented once.
• Provides a clean mapping of entity IDs to their normalized text, which serves as the input for embedding computation.

Compute embeddings for entities

entity_embeddings = compute_entity_embeddings(unique_entities, model, batch_size)

• Generates vector representations for all unique entities using the preloaded sentence-transformer model.
• Embeddings capture semantic meaning, enabling cosine similarity comparisons.

Build the entity similarity matrix

entity_similarity_matrix = build_entity_similarity_matrix(
                                        entity_embeddings,
                                        min_similarity
                                    )

• Constructs a sparse, symmetric cosine similarity matrix from the embeddings.
• Only retains meaningful relationships above the min_similarity threshold, which is critical for reliable reinforcement and network analysis.

Return the similarity matrix

• Returns the ready-to-use matrix in dictionary form for downstream phases, streamlining the integration with entity role scoring and topical network construction.

Function: collect_page_entity_relevance_scores

Overview

The collect_page_entity_relevance_scores function is responsible for calculating and aggregating the query relevance scores of all filtered entities on a page. It leverages the per-entity query alignment data to determine how strongly each entity aligns with any of the provided queries.

By computing the maximum query similarity per entity, the function provides a quantitative measure of entity importance in relation to the target queries, which is later used for entity role classification and reinforcement scoring.

Key Code Explanations

Compute strongest query alignment per entity

score = compute_entity_query_relevance(entity)
entity[“max_query_similarity”] = score

• Uses the compute_entity_query_relevance helper to find the highest similarity between the entity and any query.
• Annotates each entity with max_query_similarity, making this score accessible for downstream analysis, e.g., sorting entities by query relevance.

Aggregate scores across sections

for section in page_data.get(“sections”, []):
    for entity in section.get(“filtered_entities”, []):
        …
        scores.append(score)

• Iterates through all sections and filtered entities, ensuring only entities that passed salience filtering contribute to relevance scoring.
• Produces a flat list of relevance scores representing the entire page.

Sort scores descending

return sorted(scores, reverse=True)

• Returns descending sorted scores, which facilitates percentile-based role assignment in subsequent phases.
• Enables identification of core versus reinforcing entities based on their query alignment strength.

Function: compute_role_cutoffs

Overview

The compute_role_cutoffs function determines adaptive thresholds for classifying entities into different semantic roles (core or reinforcing) based on their query relevance distribution across the page.

By using percentiles, it accounts for page-specific variation in entity relevance, ensuring that the role classification is relative and robust, rather than relying on arbitrary fixed thresholds.

Key Code Explanations

Handle empty score list

if not relevance_scores:
    return 1.0, 1.0

• Provides a safe fallback when no entities have relevance scores.
• Ensures downstream functions have valid cutoffs, preventing runtime errors.

Compute percentile-based cutoffs

core_cutoff = max(
    float(np.percentile(relevance_scores, core_percentile)),
    relevance_floor
)

reinforcing_cutoff = max(
    float(np.percentile(relevance_scores, reinforcing_percentile)),
    relevance_floor
)

• np.percentile computes the core and reinforcing thresholds based on the distribution of entity relevance scores.
• max(…, relevance_floor) ensures that extremely low or zero values do not result in meaningless role assignments.

Return adaptive cutoffs

return core_cutoff, reinforcing_cutoff

• Provides two numeric thresholds for downstream entity role assignment:
  • core_cutoff: Entities above this are considered core entities.
  • reinforcing_cutoff: Entities above this but below the core cutoff are reinforcing entities.
• These cutoffs dynamically reflect page-specific semantic importance, which is critical for accurate entity network analysis.

Function: build_entity_section_coverage_index

Overview

The build_entity_section_coverage_index function creates a mapping between each filtered entity and the set of section positions where the entity occurs. This mapping provides a concise view of entity distribution across the page, which is essential for evaluating coverage, positional consistency, and thematic reinforcement. By understanding where entities appear, the function supports downstream analyses such as role assignment, reinforcement scoring, and network construction.

The function iterates through all sections of the page, considers only filtered entities, and accumulates the positions into a dictionary with sets to ensure uniqueness. This approach enables efficient retrieval of section coverage for any given entity without redundancy.

Function: assign_entity_role

Overview

The assign_entity_role function determines the semantic role of a filtered entity on a page based on its query relevance score and section coverage. The function uses a distribution-aware logic that considers both the strength of an entity’s alignment with the target queries and how widely it is represented across the page sections. Roles are assigned as core, reinforcing, or contextual, reflecting the entity’s importance in establishing topical authority and thematic clarity.

An entity is classified as core if its maximum query similarity exceeds the core_cutoff and it appears in at least min_core_sections, indicating it is central to the page’s theme. Entities with scores above the reinforcing_cutoff but not meeting core criteria are labeled reinforcing, contributing to thematic reinforcement. All other entities are considered contextual, providing supplementary context without major thematic influence.

Function: enforce_section_contextual_limit

Overview

The enforce_section_contextual_limit function ensures that contextual entities do not dominate a section’s content representation. Contextual entities are supplementary and should not overshadow core or reinforcing entities. The function calculates the ratio of contextual entities to the total number of filtered entities in the section and trims them if the ratio exceeds the defined max_contextual_ratio. Only the strongest contextual entities, measured by their maximum query similarity, are retained, while all core and reinforcing entities are preserved. This guarantees that section-level thematic clarity and relevance are maintained.

Key Code Explanations

Filtering contextual entities

contextual = [e for e in entities if e[“role”] == “contextual”]

This line extracts all entities labeled as “contextual” to evaluate their proportion in the section.

Ratio check and early return

if len(contextual) / len(entities) <= max_contextual_ratio:
    return

If the proportion of contextual entities is within the acceptable limit, no changes are made, and the function exits early.

Sorting by query relevance

contextual.sort(key=lambda e: e[“max_query_similarity”])

Contextual entities are sorted in ascending order of their maximum query similarity score. This ensures that the least relevant contextual entities are considered first for removal.

Determining allowed contextual entities

allowed = int(len(entities) * max_contextual_ratio)

Calculates the maximum number of contextual entities permitted in the section based on the total number of filtered entities and the predefined ratio.

Final reconstruction of filtered entities

section[“filtered_entities”] = (
    [e for e in entities if e[“role”] != “contextual”]
    + contextual[-allowed:]
)

• Core and reinforcing entities are preserved without modification.
• Only the top allowed contextual entities (based on query relevance) are included in the section.
• This maintains a balanced representation of entities in the section while enforcing the contextual limit.

Function: classify_page_entity_roles

Overview

The classify_page_entity_roles function assigns semantic roles—core, reinforcing, or contextual—to entities across all sections of a page. These roles are determined using distribution-aware logic based on entity query relevance scores and section coverage. Core entities represent the primary thematic elements strongly aligned with queries, reinforcing entities support the main theme, and contextual entities provide additional but supplementary information. The function also enforces a limit on the proportion of contextual entities in each section, ensuring that the most relevant entities dominate the content representation. This classification helps in understanding page topical authority, entity importance, and their contribution to thematic clarity.

Key Code Explanations

Collecting entity relevance scores

relevance_scores = collect_page_entity_relevance_scores(page_data)

This line gathers the maximum query-alignment scores of all filtered entities across the page. These scores form the basis for determining thresholds that separate core, reinforcing, and contextual roles.

Computing adaptive role cutoffs

core_cutoff, reinforcing_cutoff = compute_role_cutoffs(

    relevance_scores,

    core_percentile,

    reinforcing_percentile,

    relevance_floor

)

Role cutoffs are calculated using page-level distributions. Entities with relevance above the core cutoff and sufficient section coverage are classified as core, while entities above the reinforcing cutoff are labeled reinforcing. The relevance_floor ensures that thresholds never fall below a minimum semantic relevance level.

Building entity-section coverage index

coverage_index = build_entity_section_coverage_index(page_data)

This constructs a mapping from each entity to the set of section positions in which it appears. It is used to ensure that core entities appear across multiple sections, reinforcing their thematic importance.

Assigning roles to entities in sections

entity[“role”] = assign_entity_role(

    entity=entity,

    section_coverage_count=len(coverage_index[entity[“entity_id”]]),

    core_cutoff=core_cutoff,

    reinforcing_cutoff=reinforcing_cutoff,

    min_core_sections=min_core_sections

)

Each filtered entity is assigned a role based on its query relevance score and the number of sections it covers. This ensures that core entities are both highly relevant and widely distributed.

Enforcing contextual entity limit per section

enforce_section_contextual_limit(

    section,

    max_contextual_ratio

)

After role assignment, this line trims contextual entities in sections to prevent them from exceeding the allowed proportion (max_contextual_ratio). The highest relevance contextual entities are retained, while the least relevant ones are removed.

Function: compute_entity_reinforcement_score

Overview

The compute_entity_reinforcement_score function calculates a comprehensive reinforcement score for a given entity on a page. This score reflects how strongly an entity supports the page’s thematic structure, based on three core dimensions: coverage, semantic reinforcement, and positional consistency, with an additional weight applied according to the entity’s semantic role (core, reinforcing, or contextual).

The function integrates multiple supporting utility functions, each handling a specific aspect of the score calculation:

• compute_section_coverage_ratio — computes the fraction of sections in which the entity appears relative to the total number of sections.
• compute_average_semantic_reinforcement — calculates the mean semantic similarity of the entity to the sections it appears in.
• compute_positional_consistency — measures how consistently the entity is distributed across sections using normalized variance.

These helper functions are straightforward and self-contained, providing modularity and clarity to the overall calculation. By combining their outputs with the role weight, the function produces a single reinforcement score that quantifies the entity’s importance in reinforcing the page’s topical authority.

Key Code Explanations

Role weighting and final score calculation

role_weight = ROLE_WEIGHTS.get(entity_role, 0.3)

reinforcement_score = (
    coverage
    * semantic_strength
    * positional_consistency
    * role_weight
)

The final reinforcement score multiplies the three core metrics — coverage, semantic strength, and positional consistency — by the role-specific weight. This ensures that entities classified as “core” have a stronger contribution than “reinforcing” or “contextual” entities, while still accounting for their actual presence and influence across the page.

This structure allows a transparent, interpretable, and quantifiable assessment of how each entity contributes to the page’s thematic clarity and authority.

Function: score_page_entities

Overview

The score_page_entities function calculates reinforcement scores for all filtered entities across a page. It leverages the previously defined compute_entity_reinforcement_score function to generate a detailed set of diagnostics for each entity, including section coverage ratio, average semantic reinforcement, positional consistency, and the overall reinforcement score.

The function first builds a section coverage index to track where each entity appears, which is then used along with the total number of sections to compute coverage metrics. Each entity is processed only once to avoid redundant calculations, ensuring efficiency. The resulting diagnostics are directly attached to each entity in the page data, providing a comprehensive view of the entity’s contribution to the page’s topical authority.

Key Code Explanations

Tracking processed entities and avoiding redundant computation

if eid in entity_seen:

    continue

This snippet ensures that each entity is scored only once even if it appears in multiple sections. The entity_seen dictionary acts as a record of entities that have already been processed, which improves efficiency and prevents overwriting or duplicating results.

Updating entity diagnostics

entity.update(diagnostics)

entity_seen[eid] = diagnostics

Here, the diagnostics computed for each entity are attached directly to the entity object within the page data. This allows downstream modules to access all reinforcement metrics alongside the original entity attributes, providing a complete and interpretable dataset for analysis or visualization.

Function: compute_page_reinforcement_metrics

Overview

The compute_page_reinforcement_metrics function calculates high-level, page-wide diagnostics that summarize the topical reinforcement contributed by entities of different roles. It aggregates reinforcement scores across core, reinforcing, and contextual entities to provide metrics such as core_entity_stability, reinforcing_entity_depth, and contextual_noise_ratio.

These metrics allow a quick, interpretable assessment of how well the page’s content is thematically structured, highlighting the dominance of core entities, the depth contributed by reinforcing entities, and the potential “noise” from contextual entities. By summarizing at the page level, this function supports high-level decision-making for content optimization and entity-based thematic analysis.

Key Code Explanations

Categorizing entities by role

if entity[“role”] == “core”:

    core_scores.append(score)

elif entity[“role”] == “reinforcing”:

    reinforcing_scores.append(score)

else:

    contextual_scores.append(score)

This section classifies each entity’s reinforcement score into lists based on its assigned role. This classification is crucial for deriving role-specific diagnostics that reflect the entity’s impact on the page’s topical structure.

Computing the contextual noise ratio

“contextual_noise_ratio”: round(

    len(contextual_scores) / max(len(core_scores) + len(reinforcing_scores), 1),

    4

)

The contextual noise ratio measures the relative proportion of contextual entities compared to core and reinforcing entities. Using max(…, 1) prevents division by zero for pages with no core or reinforcing entities. This metric indicates potential dilution of thematic focus and informs content refinement decisions.

Function: run_entity_reinforcement_scoring

Overview

The run_entity_reinforcement_scoring function serves as the orchestration layer for the entity reinforcement analysis workflow. It sequentially executes the reinforcement scoring of individual entities and computes page-level reinforcement diagnostics. By centralizing these operations, it ensures that all reinforcement-related metrics are consistently calculated and attached to the page data in a single pass.

This function enables a streamlined, client-ready output where both section-level and page-level reinforcement insights are available for interpretation, visualization, and decision-making. It simplifies downstream processes by encapsulating multiple steps into a single callable routine, making the workflow robust and reproducible.

Key Code Explanations

Orchestrating entity scoring and page-level metrics

page_data = score_page_entities(page_data)

page_data[“page_reinforcement_metrics”] = compute_page_reinforcement_metrics(page_data)

These two lines sequentially compute detailed reinforcement scores for all filtered entities and then aggregate those scores into high-level page metrics. This design ensures that each entity’s role, coverage, and semantic reinforcement contribute directly to interpretable page-level diagnostics, providing a holistic view of the content’s thematic reinforcement.

Function: build_section_entity_index

Overview

The build_section_entity_index function creates a mapping from each section identifier (section_id) to the list of filtered entities present in that section. This index provides a convenient structure for later computations, such as assessing entity co-occurrence across sections or building entity relationships for network analysis. It simplifies access to section-level entity data, making downstream network and reinforcement calculations more efficient.

Function: compute_entity_copresence_ratio

Overview

The compute_entity_copresence_ratio function calculates how often two entities appear together across the sections of a page. It measures the ratio of shared sections to total sections, providing a normalized value between 0 and 1. This metric is useful for understanding the contextual relationship between entities, which contributes to network edge strength and semantic reinforcement analyses.

Function: compute_reinforcement_alignment

Overview

The compute_reinforcement_alignment function computes the average reinforcement score between two entities. By averaging the individual reinforcement scores, it captures the combined importance and influence of both entities within the page context. This alignment score is later used to weight edges in the entity network, reflecting the overall semantic strength of connections.

Function: compute_edge_strength

Overview

The compute_edge_strength function calculates a weighted combination of three key factors—semantic similarity, copresence ratio, and reinforcement alignment—to produce a single edge strength metric. These weights are configurable and allow the network to emphasize different aspects of entity relationships. The function ensures that edges in the network reflect both semantic and structural relevance between entities.

Function: generate_candidate_edges

Overview

The generate_candidate_edges function constructs a set of candidate edges between entities for building the entity network. It evaluates pairwise entity similarity, co-occurrence across sections, and reinforcement alignment. Contextual entities are filtered to avoid weak connections dominating the network, and edges failing threshold criteria are discarded. The resulting edges are annotated with source and target labels, similarity measures, and computed edge strength, ready for network visualization and analysis.

Key Code Explanations

Role-based filtering of edges

if ent_a.get(“role”) == “contextual” and ent_b.get(“role”) == “contextual”:

    continue

This line prevents edges connecting two purely contextual entities, which are considered less semantically significant. It ensures that the network emphasizes stronger, more meaningful relationships.

Copresence threshold enforcement

if copresence * total_sections < MIN_COPRESENCE_SECTIONS:

    continue

Edges are discarded if the entities do not appear together in at least the minimum number of sections. This step filters out spurious connections that are unlikely to contribute to thematic reinforcement.

Weighted edge strength computation

strength = compute_edge_strength(sim, copresence, alignment)

Here, the function combines semantic similarity, copresence ratio, and reinforcement alignment using pre-defined weights. This produces a single numeric score that quantifies the overall strength of the connection between the two entities, guiding network construction and visualization.

Function: prune_edges_per_node

Overview

The prune_edges_per_node function limits the number of edges connected to each entity node in the network to avoid overly dense graphs. By retaining only the strongest edges (based on edge strength) for each node, it ensures that the network highlights the most meaningful relationships between entities. This pruning improves network interpretability and focuses analysis on the most important entity connections, while preventing clutter from weaker or redundant links.

Key Code Explanations

Sorting and keeping top edges per node

sorted_edges = sorted(

    e_list,

    key=lambda x: x[“edge_strength”],

    reverse=True

)[:max_edges_per_node]

This line sorts all edges connected to a given node in descending order of their computed edge strength and selects only the top max_edges_per_node. This ensures that only the most significant connections are retained for each node, reducing noise in the network visualization.

Edge deduplication across source and target

kept.add((e[“source”], e[“target”]))

…

if (e[“source”], e[“target”]) in kept

or (e[“target”], e[“source”]) in kept

Edges are tracked in a set to prevent duplication when nodes appear in either the source or target position. This guarantees that each significant connection is represented only once in the pruned edge list.

Function: build_topical_entity_network

Overview

The build_topical_entity_network function orchestrates the creation of the final topical entity network for a page. It integrates multiple prior steps, including candidate edge generation and edge pruning, to produce a clear and interpretable network of entities. Each node in the network represents a filtered entity, enriched with its role and reinforcement score, while edges capture the strongest semantic, copresence, and reinforcement-based relationships between entities. The resulting network provides a structured view of how key entities interact and reinforce the page’s main topics, enabling clients to quickly understand thematic clarity and content authority.

This function consolidates the network data into a nodes and edges structure, which can be directly visualized or used for further analysis, ensuring that only the most meaningful relationships are highlighted and minor, redundant connections are removed.

Key Code Explanations

Node construction with entity attributes

nodes[eid] = {

    “entity_id”: eid,

    “label”: entity[“normalized_text”],

    “role”: entity[“role”],

    “reinforcement_score”: entity[“reinforcement_score”]

}

This code ensures that each entity in the network is represented once as a node, capturing its identifier, display label, semantic role, and reinforcement score. Including these attributes allows downstream analysis and visualization to distinguish core, reinforcing, and contextual entities while reflecting their contribution to topical reinforcement.

Integration of pruned edges into the network

edges = prune_edges_per_node(edges)

After generating candidate edges, this line applies the per-node pruning logic to maintain only the strongest connections for each entity. This step reduces network complexity, enhances clarity, and ensures the visualization highlights meaningful relationships rather than being overwhelmed by weak or redundant links.

Function: display_results

Overview

The display_results function provides a comprehensive, user-friendly summary of the Entity Topical Network Analyzer outputs. It focuses on interpretability and actionable insights rather than raw technical details, making the results accessible to users who may not have a technical background.

For each analyzed page, the function presents high-level diagnostics, including the number of sections analyzed, the proportion of sections with meaningful entities, and page-level reinforcement metrics such as core entity stability, reinforcing depth, and contextual noise ratio. It also interprets these metrics with human-readable notes when available.

At the query level, the function summarizes how well the content supports each target query, categorizing support strength as “Strongly Supported,” “Partially Supported,” or “Weak / Unclear Support,” based on average entity alignment scores. Section-level contribution is highlighted by showing sections with meaningful entity signals and the number of entities retained, providing insight into which content segments drive topical authority.

Additionally, entity roles are summarized, distinguishing core and reinforcing entities, with the top concepts listed for quick understanding. The function also provides an overview of the topical network, including the total number of nodes, semantic links, and the strongest entity-to-entity relationships. This structured presentation ensures users can quickly interpret entity relationships, topical coverage, and content alignment without needing to dive into technical outputs.

Result Analysis and Explanation

The analysis of the page “Handling Different Document URLs Using HTTP Headers Guide” provides a detailed look at the content’s topical structure, entity coverage, query alignment, and semantic reinforcement. Each subsection below breaks down the results, interprets what they mean, and outlines actionable insights for the client.

1. Page-Level Topical Diagnostic

The page contains 38 sections, but only 4 sections contribute meaningful entities, resulting in a topical coverage ratio of 0.11. This indicates that just over 10% of the page’s content contains named entities that are relevant to the overall topical analysis.

Interpretation:

• The extremely low coverage suggests that the page has sparse named-entity presence. For entity-driven topical mapping, this limits the ability to extract strong semantic signals, reinforcement relationships, or a dense topical network.
• Core entity stability and reinforcing depth score are both 0.00, highlighting that no entities consistently drive the topic across multiple sections.
• Contextual noise ratio is also 0.00, which reflects the absence of widespread contextual entities. While this avoids noise, it further emphasizes the page’s low entity density.

Action Suggestions:

• Consider adding more technical terms, concepts, and industry-relevant entities across multiple sections to strengthen the page’s topical representation.
• Include recurring key phrases, tool names, or technical markers (e.g., HTTP header types, canonical URL examples) that can serve as reinforcing or core entities for better semantic reinforcement.
• Ensure that each section provides concrete, entity-rich content rather than purely descriptive text to improve automated topical analysis outcomes.

2. Query-Level Support Assessment

The page was evaluated against two primary client queries:

1. How to handle different document URLs – Partially Supported (avg entity alignment: 0.29)
2. Using HTTP headers for PDFs and images – Strongly Supported (avg entity alignment: 0.37)

Interpretation:

• A partially supported query indicates that while the page contains some relevant content, the semantic alignment with the query is weak. There may be scattered mentions or a lack of structured guidance for the query.
• Strongly supported queries show good alignment between content and user intent. In this case, the page provides sufficient technical details about HTTP headers for PDFs and images.

Action Suggestions:

• For queries with partial support, enrich the content with explicit explanations, step-by-step guides, or examples that directly address the query.
• Ensure query terms are naturally integrated into headings and subheadings to improve both semantic and entity alignment for search and topical analysis.
• Use entity reinforcement strategies, such as linking key concepts across sections, to improve query support.

3. Section-Level Topical Contribution

Only 4 out of 38 sections contain meaningful entity signals:

• ‘Get a Customized Website SEO Audit and Online Marketing Strategy and Action Plan’ (entities retained: 1)
• ‘1. What Are HTTP Headers?’ (entities retained: 1)
• ‘2. Important HTTP Headers for SEO’ (entities retained: 1)
• ‘Steps to Implement Canonical Tags for PDF, Image, and Video URLs Using HTTP Headers’ (entities retained: 1)

Interpretation:

• Most sections lack identifiable entities, which contributes to the low topical coverage ratio.
• The few sections with entities appear to be highly localized and do not span multiple parts of the page.

Action Suggestions:

• Expand entity-rich content across more sections to improve coverage.
• Repeat key technical terms or related concepts across multiple sections to increase reinforcement and provide stronger semantic connections between sections.
• Ensure that actionable content, such as implementation steps or examples, includes the most relevant entities to increase the depth of entity signals.

4. Entity Role Summary

• Core entities: 1 (http)
• Reinforcing entities: 0

Interpretation:

• Only one entity is classified as core, highlighting that the page’s content lacks a strong central concept repeated and reinforced across multiple sections.
• No reinforcing entities are present, which further limits the semantic reinforcement network.

Action Suggestions:

• Identify key concepts that should be designated as core (e.g., HTTP headers, canonical tags, document URLs) and ensure they are consistently referenced in multiple sections.
• Introduce related terms or sub-concepts to act as reinforcing entities and enhance topical depth.

5. Topical Network Insight

• Entity nodes: 1
• Semantic links: 0

Interpretation:

• The network is extremely sparse, consisting of only a single entity node with no semantic links. This means the page does not demonstrate interconnected concepts or reinforced topical structure.
• A lack of semantic relationships reduces the interpretability and strength of entity-based topical mapping.

Action Suggestions:

• Introduce multiple related entities within the same sections to allow for co-occurrence and reinforcement.
• Link key concepts contextually across sections (e.g., canonical tags, headers, PDF/image handling) to build a more robust network that highlights topical authority.
• Use the entity network to guide content expansion, ensuring that each new entity introduced contributes to the network density.

Summary

Overall, the analysis shows that while the page contains relevant content for some queries, the low entity density and minimal network connectivity limit its effectiveness for entity-driven topical mapping. Only a single core entity exists, with no reinforcing entities or semantic links, and just four sections provide meaningful signals.

Actionable Recommendations:

• Increase entity coverage by integrating technical terms and examples throughout the page.
• Repeat and reinforce core concepts to allow entities to gain prominence and improve network connectivity.
• Expand content in partially supported queries with structured explanations and examples.
• Aim to interlink entities across sections to build a stronger topical network, enhancing both user comprehension and SEO relevance.

Result Analysis and Explanation

This section provides a comprehensive analysis of the multi-page topical entity results, explaining each aspect of the content’s semantic and entity-based structure. The analysis covers page-level diagnostics, query-level alignment, section contribution, entity roles, topical network structure, and visualization insights.

1. Page-Level Topical Diagnostic

Overview

The page-level diagnostic measures the overall presence and density of named entities across sections, the stability of core entities, reinforcing depth, and the ratio of contextual entities. These metrics provide a high-level understanding of how well a page covers important concepts relevant to the queries.

• Sections Analyzed vs. Sections Contributing Entities: A higher number of sections analyzed with fewer contributing entities indicates low named-entity density. Pages in this dataset show a small fraction of sections actively contributing entity signals, signaling that the page may be rich in descriptive or procedural content rather than entity-dense content.
• Topical Coverage Ratio: This ratio captures the proportion of sections with meaningful entities relative to total sections. Threshold guidance: ratios above 0.3 indicate strong coverage; 0.1–0.3 indicates moderate coverage; below 0.1 reflects low coverage and a potential lack of structured topical emphasis.
• Core Entity Stability and Reinforcing Depth Score: Both metrics are indicators of the strength and consistency of main entities across sections. Zero or near-zero values suggest that there is minimal repetition or reinforcement of key entities, which may limit a page’s ability to convey authority on specific topics.
• Contextual Noise Ratio: Higher values indicate a prevalence of background or supporting entities relative to core/reinforcing entities. Elevated contextual noise may dilute the page’s perceived topical authority.

Interpretation and Actionable Insights

Pages with low coverage and low core/reinforcing stability require editorial focus on introducing and reinforcing key entities systematically. Authors should identify the primary concepts they wish to convey and ensure consistent mentions across multiple sections to enhance core entity stability and depth.

2. Query-Level Support Assessment

Overview

Query-level support measures how well the page content aligns semantically with user-targeted queries. Scores are derived from entity-query alignment:

• Strong Support (≥0.35): Indicates entities strongly aligned with the query. Pages meeting this threshold reliably answer or cover the query topic.
• Partial Support (0.25–0.34): Some alignment exists, but additional content or entity reinforcement could improve clarity and coverage.
• Weak / Unclear Support (<0.25): Minimal alignment; the query is largely unaddressed.

Interpretation and Actionable Insights

For queries with weak or unclear support, content gaps exist. Actions include adding sections or entities specifically targeting these queries, increasing explicit mentions of relevant entities, and structuring the content to reinforce these concepts. Queries with partial support can be improved through better integration of core and reinforcing entities, such as linking concepts across multiple sections to enhance semantic cohesion.

3. Section-Level Topical Contribution

Overview

This aspect identifies which sections carry the most topical weight based on filtered entities. The number of contributing entities per section reflects its topical richness:

• Sections with retained entities are the primary drivers of query coverage and overall page authority.
• Even pages with many sections may show only a few sections contributing significantly, indicating uneven topical distribution.

Interpretation and Actionable Insights

Sections with high entity retention are critical for reinforcing core topics. Authors should consider replicating the structure of high-contributing sections in other parts of the page or creating internal links to emphasize these sections. Low-contributing sections may require entity enrichment or restructuring to enhance topical coherence.

4. Entity Role Summary

Overview

Entities are categorized into core, reinforcing, and contextual roles based on their relevance, section coverage, and query alignment:

• Core Entities: Represent the main concepts driving page authority. Consistent appearance across sections increases semantic reinforcement.
• Reinforcing Entities: Support core concepts and provide depth to the topical narrative.
• Contextual Entities: Background concepts that add minor informational value but do not drive authority.

Interpretation and Actionable Insights

A low number of core entities or zero core entities indicates weak topic anchoring. Actions include identifying high-priority topics and ensuring repeated and structured mentions across sections. Reinforcing entities should be strategically paired with core entities to strengthen semantic cohesion. Excessive contextual entities relative to core/reinforcing may indicate diluted content focus and warrant editorial trimming or rebalancing.

5. Topical Network Insight

Overview

The entity network captures relationships among entities across the page, integrating semantic similarity, co-occurrence, and reinforcement signals:

• Nodes: Each represents a unique entity. A higher node count suggests broader topic coverage.
• Edges: Connections reflect semantic or contextual relationships. Stronger edges indicate cohesive topic reinforcement.
• Strongest Relationships: Highlight the most semantically and contextually reinforced entity pairs.

Interpretation and Actionable Insights

Sparse networks or low edge strength indicate weak entity interconnections, suggesting the content lacks integrated topical structure. To improve cohesion, authors should ensure that core and reinforcing entities are referenced across multiple sections in ways that create logical relationships, improving both readability and search relevance.

6. Visualization Module Insights

The visualization module provides a visual understanding of the above results. These plots help interpret, benchmark, and plan content improvements.

6.1 Entity Role Distribution Plot

What it shows: The distribution of core, reinforcing, and contextual entities across pages.

Interpretation: Pages with a higher proportion of core entities demonstrate strong topical authority. Conversely, pages dominated by contextual entities may lack focus.

Actionable Insights: Focus on converting high-value contextual entities into reinforcing or core entities by linking them to primary topics and repeating them across relevant sections.

6.2 Section-Level Topical Contribution Plot

What it shows: Top sections per page by number of filtered entities.

Interpretation: Highlights the most topical sections driving entity coverage. Low entity counts in most sections indicate uneven coverage.

Actionable Insights: Reinforce low-contributing sections by introducing relevant core or reinforcing entities, and replicate structural strategies from high-contributing sections for consistency.

6.3 Network Strength Distribution Plot

What it shows: Histogram of entity network edge strengths across pages.

Interpretation: Higher edge strength implies better semantic cohesion and topical reinforcement. Weak or sparse edges suggest that entities are not effectively connected.

Actionable Insights: Enhance connections by interlinking key concepts, using internal linking, and ensuring repeated mentions of core and reinforcing entities across sections.

6.4 Top Entity Connectivity Plot

What it shows: The top entities based on network-derived connectivity (reinforcement scores).

Interpretation: Highly connected entities represent central topics. Low connectivity of critical entities indicates gaps in semantic reinforcement.

Actionable Insights: Increase mentions and contextual integration of central entities, pair them with reinforcing entities, and ensure consistent coverage across relevant sections to improve overall page topical authority.

7. Summary and Recommendations

Overall, the multi-page analysis reveals that:

• Pages often have low named-entity density and uneven topical coverage.
• Some queries are strongly supported while others are weakly addressed, indicating gaps in query-focused content.
• Section-level contributions are highly concentrated, suggesting certain sections carry most of the topical weight.
• Core entity presence is limited, and entity networks are sparse, indicating potential improvement areas for semantic cohesion.

Recommended Actions:

1. Content Enrichment: Introduce or reinforce entities for weakly supported queries.
2. Section Optimization: Ensure more sections contribute meaningful entities to balance topical coverage.
3. Entity Reinforcement: Promote consistent use of core and reinforcing entities across multiple sections.
4. Semantic Connectivity: Create logical relationships between entities through repeated mentions and internal linking to strengthen the topical network.

This holistic analysis allows content strategists to systematically enhance topical authority, query alignment, and semantic cohesion across multiple pages.

Project Result Understanding and Action Suggestions — Q&A

What does a low topical coverage ratio indicate for my pages, and how should I act on it?

A low topical coverage ratio indicates that only a small fraction of sections on the page contain meaningful, query-relevant entities. Essentially, much of the page lacks dense, actionable topical signals that search engines or readers can recognize as authoritative. For instance, pages with a coverage ratio below 0.1 may be dominated by generic content or procedural explanations without highlighting core concepts.

Action Suggestions: To improve topical coverage, identify the key entities relevant to your target queries and integrate them consistently across multiple sections. This can involve:

• Adding new sections that address gaps in entity coverage.
• Enhancing existing sections with stronger mentions of core and reinforcing entities.
• Linking related concepts across sections to create cohesive topical coverage.

The benefit of this approach is a stronger semantic signal to both users and search engines, improving perceived topical authority and query relevance.

How should I interpret core entity stability and reinforcing depth scores, and why are they important?

Core entity stability measures how consistently core concepts appear across sections, while reinforcing depth score captures the supporting strength of entities that augment these core concepts. Low scores indicate that the page lacks repeated reinforcement of key ideas, meaning critical topics are only mentioned sporadically or superficially.

Action Suggestions: Focus on ensuring that each core concept is mentioned in multiple sections, ideally paired with reinforcing entities that support the topic. For example, a page targeting “SEO audits” should repeatedly reference this concept along with complementary entities like “SEO metrics,” “Google Search Console,” or “backlink analysis.” This structured repetition increases the page’s topical cohesion, improves readability, and signals authority to search engines.

The benefit is that higher core stability and reinforcing depth translate to stronger semantic signals, improving both SEO performance and user comprehension.

What do weak or partially supported queries imply, and how can they be addressed?

Weakly supported queries (average alignment <0.25) indicate that the content does not provide sufficient information or entity-based coverage for that specific query. Partially supported queries (0.25–0.34) are covered to some extent but lack depth or reinforcement. These gaps can lead to lower search visibility for targeted queries and incomplete user guidance.

Action Suggestions:

• For weak queries, add sections or paragraphs explicitly addressing the topic. Include relevant entities that directly correspond to the query.
• For partially supported queries, enhance existing content by integrating reinforcing entities and examples, linking related concepts, and increasing cross-sectional coverage.

This improves both user satisfaction and search engine understanding of page relevance.

How can I use section-level topical contribution to optimize content structure?

Section-level analysis identifies which sections contribute most to query coverage based on filtered entities. A small number of sections driving most of the entity coverage suggests uneven topical distribution. Sections with few or no contributing entities may be underdeveloped or not aligned with key queries.

Action Suggestions:

• Prioritize high-contributing sections as anchor points, and replicate their structure, formatting, and entity integration in other sections.
• Enrich low-performing sections with core and reinforcing entities relevant to the target queries.
• Ensure that every section adds measurable topical value rather than duplicating unrelated content.

By strategically enhancing sections, can be achieved more balanced coverage and consistent semantic reinforcement across the page.

What insights can be derived from entity roles (core, reinforcing, contextual) and how should they guide content development?

Entity roles provide a hierarchy of topical importance. Core entities define the primary topics, reinforcing entities support these topics, and contextual entities provide minor, background information. Pages with few core entities or many contextual entities may lack clear thematic focus.

Action Suggestions:

• Identify the key core entities that should anchor the page’s topic.
• Expand reinforcing entities to provide depth and connectivity between sections.
• Reduce excessive contextual entities that do not contribute to authority, or integrate them meaningfully with core concepts.

This ensures content communicates a strong, clear topical message, improving user comprehension and search engine trust.

How can the topical entity network and edge strengths guide semantic optimization?

The entity network visualizes semantic and co-occurrence relationships between entities. Strong edges indicate tightly coupled, semantically reinforced concepts. Sparse networks or weak edge strengths reveal that important concepts are not sufficiently interlinked.

Action Suggestions:

• Strengthen relationships between core and reinforcing entities by cross-referencing concepts across multiple sections.
• Introduce internal linking strategies that connect related entities to enhance network cohesion.
• Monitor edge strength metrics for key entity pairs to ensure critical relationships are reinforced.

Well-structured networks increase topical authority, improve content comprehensibility, and can positively impact search rankings for related queries.

How can visualization outputs support content decision-making?

Visualization plots offer intuitive insights into page topicality, entity roles, section contributions, network strength, and connectivity:

• Entity Role Distribution Plot: Highlights the proportion of core, reinforcing, and contextual entities. A dominant core presence signals strong topical authority, while too many contextual entities suggest dilution.
• Section-Level Topical Contribution Plot: Shows which sections carry the most entity weight. Enables prioritization of high-impact sections and highlights underperforming areas for enrichment.
• Network Strength Distribution Plot: Reveals the strength of semantic links. Weak or sparse networks indicate the need for reinforcing connections between entities.
• Top Entity Connectivity Plot: Identifies the most central entities driving semantic reinforcement. Low connectivity of important entities indicates gaps to be addressed.

Action Suggestions: Use these plots to quickly identify content gaps, prioritize section-level improvements, and optimize entity reinforcement strategies. They serve as actionable visual guides for content planning and semantic strengthening.

What are the overall benefits of using these insights for SEO and content strategy?

Applying the insights from entity-based analysis allows website owners to:

• Enhance page topical authority by reinforcing core concepts and their supporting entities.
• Fill content gaps for weakly supported queries, improving relevance and search visibility.
• Optimize content structure by balancing section-level contributions, ensuring every section adds measurable value.
• Strengthen semantic relationships across content, improving comprehension and user engagement.
• Make data-driven decisions using visualizations to identify high-impact areas for improvement.

The outcome is content that is both user-friendly and search-engine-optimized, delivering measurable improvements in query coverage, topical authority, and semantic cohesion.

Conclusion

The Entity Topical Network Analyzer successfully delivers a comprehensive assessment of page-level topical authority by analyzing the distribution, reinforcement, and structural connectivity of key entities. Through a multi-layered approach, the tool evaluates how core and supporting concepts are presented across sections, measures their semantic reinforcement, and identifies the strength of inter-entity relationships to provide a holistic view of topical consistency and coverage.

At the page level, metrics such as topical coverage ratio, core entity stability, and reinforcing depth offer clear insights into the density and cohesion of entity-driven content. High core stability and reinforcing depth indicate that primary concepts are consistently emphasized and well-supported, ensuring thematic clarity. Conversely, the contextual noise ratio highlights sections where background entities exist, allowing an understanding of the balance between main concepts and supplementary information.

The query-level assessment provides actionable intelligence on how effectively content addresses targeted queries. By quantifying the alignment of entities with specific queries, the tool identifies areas where topics are strongly supported, partially addressed, or minimally covered. This facilitates strategic content refinement to maximize query relevance and topical coverage.

The section-level diagnostics highlight which sections contribute the most to the page’s topical strength. This allows stakeholders to focus on high-impact sections while understanding the role of each section in reinforcing core concepts. It also ensures that content structure aligns with semantic goals, enhancing readability and coherence.

Through entity role classification and network analysis, the tool elucidates the hierarchical importance of entities and the strength of semantic relationships. Core entities define the thematic anchors, reinforcing entities add depth, and the network structure illustrates interconnections that drive topical cohesion. The analysis of edge strengths and connectivity identifies central concepts and key relationships that form the backbone of authoritative content.

Visualizations further enhance interpretability, offering intuitive representations of entity roles, section contributions, network strength distributions, and top entity connectivity. These visual outputs translate complex entity interactions into actionable insights, enabling data-driven decisions for content optimization and strategic topical reinforcement. Overall, the analyzer equips stakeholders with a detailed understanding of how content communicates its key topics, the structural and semantic strength of its entities, and the relationships that drive topical authority. By providing both quantitative diagnostics and interpretable visualizations, it enables informed decisions to strengthen content relevance, coherence, and authority, ensuring a robust and measurable approach to topical optimization.