Semantic Content Cannibalization Detection

Get a Customized Website SEO Audit and SEO Marketing Strategy

Semantic Content Cannibalization Detection is designed to identify and address one of the most persistent challenges in SEO: multiple pages from the same domain competing for the same or very similar search intent. When such overlaps occur, search engines struggle to determine which page should rank, often resulting in diluted visibility and lost traffic opportunities. This project applies advanced semantic similarity techniques using transformer-based embeddings to move beyond simple keyword matching. By analyzing how closely sections of different pages align in meaning, the system detects both obvious duplication and subtle intent overlaps.

The output provides structured insights into where content competition exists, how significant the overlap is, and what type of action is warranted—whether merging, differentiating, consolidating, or linking pages. A combination of textual analysis, similarity scoring, and threshold-based recommendations ensures that the results are not just technically accurate but also directly applicable to SEO strategy. This project bridges the gap between technical NLP capabilities and actionable optimization measures, making it possible to refine site architecture, improve topical clarity, and maximize ranking efficiency across the domain.

Project Purpose

The purpose of this project is to equip SEO professionals with a precise, data-driven method to diagnose and resolve internal content competition. Traditional audits often rely on surface-level keyword analysis, which cannot fully capture semantic overlaps or intent similarity between pages. This leads to missed cases where two articles target the same user need with slightly different phrasing, or where subtle duplication fragments ranking signals. By applying semantic embeddings, this project detects overlap at a contextual level, ensuring coverage is analyzed the way modern search algorithms interpret it.

The resulting insights support key strategic goals: consolidating duplicate coverage, reinforcing clear topical separation, and ensuring each page contributes uniquely to overall domain authority. By quantifying overlaps across strong, high, and moderate thresholds, the analysis not only flags issues but also provides structured recommendations aligned with SEO best practices. The purpose extends beyond detection into actionable guidance, enabling more efficient resource allocation, stronger rankings for targeted topics, and improved user experience through clearer, more distinct content pathways.

Project’s Key Topics Explanation and Understanding

Content Cannibalization in SEO

Content cannibalization occurs when multiple pages from the same domain target similar search queries or cover overlapping semantic topics. Instead of improving visibility, this overlap can confuse search engines about which page should rank, ultimately weakening the overall performance.

For example, a site may publish two blog posts:

“Best Tools for Keyword Research”
“How to Choose Keyword Research Tools for SEO”

Although the titles appear different, both address nearly the same user intent. As a result, they compete against each other in rankings. Search engines may split authority, impressions, and clicks between them, reducing the site’s ability to dominate results for that topic.

Detecting such overlaps across large websites manually is almost impossible. Automated detection through semantic analysis is essential for maintaining clear topical targeting and avoiding internal competition.

Semantic vs Keyword Overlap

Traditional approaches to identifying cannibalization rely on keyword matching — checking whether two pages both rank for or target the same keyword. However, modern SEO must go beyond keyword overlap to capture semantic overlap.

Semantic overlap happens when two pieces of content convey similar meaning, even if they use different wording. For instance:

“How to Fix Broken Links in WordPress”
“Repair 404 Errors in a WordPress Website”

These may not share exact keywords, but semantically they overlap heavily because both solve the same problem. If only keyword-level analysis is performed, this duplication risk would be missed.

By using semantic similarity, it becomes possible to identify overlaps that traditional keyword audits cannot detect, ensuring a more accurate and future-proof solution.

Embeddings and Similarity Scoring

Embeddings are numerical vector representations of text generated by advanced language models. Instead of comparing words directly, embeddings allow comparison of meanings. Two different phrases such as “increase website visitors” and “boost site traffic” would have highly similar embeddings because their intent aligns.

In this project, embeddings are used to detect semantic duplication across web pages and even within sections of a page. Similarity scoring, particularly cosine similarity, is applied to these embeddings. This produces a numerical score between 0 and 1 that quantifies how close the meanings of two chunks of text are.

This method captures overlaps missed by keyword-only systems, providing SEO professionals with deeper insight into where cannibalization risks lie.

Page-Level vs Section-Level Analysis

Many audits evaluate cannibalization only at the page level. While this helps identify when two entire pages are similar, it often misses nuances. A single page can contain multiple topics — some unique and others overlapping with different pages.

For example:

A long guide on “Technical SEO” may include a section on canonical tags.
Another dedicated page may also cover canonical tags in detail.

Even if the overall pages differ, these sections create duplication risk. Detecting overlap only at the page level would fail to flag this issue, but section-level analysis highlights it precisely.

This project implements both:

Page-level reporting → Shows whether entire pages compete.
Section-level reporting → Reveals exactly where duplication occurs and provides actionable insights for refinement.

SEO Implications and Resolution Strategies

Detecting cannibalization is only the first step. The value comes from applying the right resolution strategy. Based on the severity and distribution of overlaps, several options are recommended:

1. Merge Content

Best for strong duplication across multiple sections or entire pages.
Consolidating two or more overlapping pages into a single authoritative resource improves topical authority and prevents ranking dilution.

2. Canonicalization

Useful when similar pages are needed (e.g., product variations or location-specific content) but only one should carry ranking weight.
A canonical tag signals to search engines which version is the “primary” one, preventing competition.

3. Differentiate Content

Appropriate when overlap is moderate. Pages can be refined to target distinct user intents, subtopics, or funnel stages.
For instance, one page may target beginners while another focuses on advanced users.

4. Internal Linking

Best suited for partial overlaps. Connecting related pages through structured internal links helps search engines understand their relationship, reduces ambiguity, and enhances topical clusters.

5. Minor Adjustments

For weak overlaps, simple refinements such as clarifying headings, adjusting keywords, or emphasizing unique sections may suffice.

By aligning detection with these strategies, the project not only highlights issues but also provides a practical roadmap to resolve them effectively.

keyboard_arrow_down

Q&A on Project Value and Importance

Why is detecting content cannibalization critical for search visibility?**

Content cannibalization occurs when multiple pages target similar topics, leading search engines to distribute ranking signals across competing pages instead of consolidating authority in one. This confusion reduces the likelihood of any single page achieving top rankings. Detecting cannibalization ensures that search engines can identify one authoritative source for each topic, which strengthens topical authority and improves the ability to secure stable rankings. Without this detection, valuable ranking opportunities are wasted, and traffic potential is diluted across competing assets.

How does semantic analysis improve detection compared to keyword-based methods?**

Keyword-based audits often rely on identifying duplicate phrases or overlapping keyword sets. While useful, this method misses subtler overlaps where different wording communicates the same intent. Semantic analysis evaluates meaning rather than surface-level text. For example, the phrases “optimizing images for better visibility” and “SEO techniques for improving image search rankings” differ in wording but align semantically. By embedding content into vector representations and calculating similarity, this system reveals overlaps invisible to keyword-only approaches. This deeper analysis provides a more accurate view of true topical competition across content.

What advantages does section-level analysis bring to the process?**

Analyzing content at the section level avoids broad generalizations about entire pages. A long-form article often covers multiple subtopics, but not all sections compete with other pages. Section-level analysis identifies exactly where overlaps occur, such as a single subsection on canonical tags that matches another dedicated article. This precision enables targeted decisions, like rewriting or merging specific sections rather than restructuring entire pages. The result is optimized content that preserves unique value while eliminating redundancy, ensuring efficiency in both strategy and execution.

Why are similarity scores valuable in evaluating overlaps?**

Similarity scores transform qualitative overlap detection into a measurable framework. Each pair of sections or pages receives a score that indicates how closely their meanings align. Scores at the higher end signal near-duplicate content, medium scores show partial topical overlap, and lower scores indicate distinct coverage. This quantification allows prioritization. Instead of treating all overlaps equally, attention can be directed to the strongest conflicts first, ensuring resources are invested where the impact is highest. The scoring framework provides transparency, turning subjective assessments into objective, data-driven insights.

How does resolving cannibalization contribute to stronger SEO performance?**

Eliminating overlaps enhances both user experience and search engine interpretation. For search engines, clear differentiation signals authority and topical focus, increasing the likelihood of higher rankings. For users, resolving cannibalization means consistently landing on the most relevant page for their query, without confusion caused by multiple competing options. The outcome is stronger keyword targeting, improved content clarity, and better alignment between search intent and page content. Over time, this contributes to greater trust in the domain’s authority, resulting in more consistent traffic growth.

What forms of optimization can follow from identifying overlaps?**

Once overlaps are identified, multiple optimization pathways become available:

Merging content into a single, comprehensive resource when two pages duplicate coverage.
Canonicalization to indicate a preferred version when multiple similar pages must coexist.
Content differentiation by refocusing overlapping sections on unique subtopics or user intents.
Internal linking adjustments to clarify topical relationships and guide search engines.
Selective refinements where only minor overlaps exist, avoiding unnecessary large-scale changes.

These optimization options ensure flexibility, allowing tailored actions depending on the type and severity of the detected cannibalization.

How does this system support long-term content strategy?**

Beyond fixing immediate overlaps, the system establishes a sustainable framework for content planning. By providing visibility into where topics overlap, it prevents new cannibalization issues during future content creation. Teams can evaluate whether a planned page would compete with existing assets before publishing, ensuring each piece of content occupies a distinct position within the site’s topical structure. This proactive approach not only protects rankings but also strengthens topical coverage in a systematic way, positioning the site for long-term authority and stability.

Libraries Used

requests

The requests library is a widely used Python module for sending HTTP requests and handling responses. It provides a simple interface to interact with web resources, enabling retrieval of raw HTML or structured data from remote servers. Known for its reliability and ease of use, requests is a core tool in data gathering workflows.

In this project, requests is used to fetch web page content directly from URLs. Since content cannibalization analysis requires real page data, this library serves as the entry point for collecting the raw HTML that is later parsed and processed. Its role ensures that the system works seamlessly with live website inputs rather than relying on pre-downloaded text.

logging

The logging module is part of Python’s standard library and provides a flexible framework for emitting log messages from applications. It supports different severity levels (e.g., INFO, WARNING, ERROR), making it useful for both debugging during development and monitoring during production.

In this project, logging is configured to capture warnings and suppress unnecessary detail. It helps track issues such as failed URL fetches, parsing errors, or unexpected data structures without interrupting the execution pipeline. This ensures stability and makes troubleshooting easier if unexpected content or errors appear during the analysis.

re

The re module is Python’s built-in library for handling regular expressions. It enables pattern matching and text manipulation through concise rules, making it efficient for tasks like searching, cleaning, or extracting specific string patterns.

Here, re is employed during preprocessing to normalize and refine raw HTML text into structured sections. For example, it helps clean markup, remove unwanted characters, and detect specific patterns in headings or content. These operations are essential for ensuring that only meaningful text is fed into the similarity models.

html

The html module provides utilities for handling HTML entities, including encoding and decoding special characters. It is particularly useful for converting characters like & into their readable equivalents or preparing text for output.

In this project, html is used to decode encoded characters present in web page content. This ensures that analysis is carried out on clean, human-readable text instead of raw encoded symbols. Without this step, similarity calculations could be distorted by artifacts from encoding.

unicodedata

The unicodedata module offers access to Unicode character properties and normalization functions. It is commonly used to standardize text by ensuring consistent encoding across characters and scripts.

Here, unicodedata helps clean and normalize text sections by removing inconsistencies like accent marks or invisible characters. This standardization is critical when generating embeddings, as even minor differences in encoding could result in varied vector representations.

time

The time module provides functions for measuring and managing time within Python programs. It supports operations such as delays, timestamps, and performance tracking.

In this project, time is used to handle request timing and manage pauses between operations when required. This ensures smoother execution when processing multiple pages in sequence and helps avoid issues such as request rate limits from servers.

bs4 (BeautifulSoup)

BeautifulSoup is a Python library for parsing HTML and XML documents. It provides a tree-based interface for navigating, searching, and modifying markup, making it an essential tool for web scraping and text extraction.

Here, BeautifulSoup is used to parse the raw HTML of each webpage into structured text sections. By identifying headings, paragraphs, and other elements, it enables the system to isolate meaningful content blocks that can later be analyzed for semantic similarity. This forms the foundation for overlap detection at both section and page levels.

typing

The typing module introduces type hints in Python, enabling clearer, more maintainable code. It supports static type checkers and improves readability by documenting expected data structures.

Within this project, typing ensures that functions handling overlaps, sections, and statistics explicitly define input and output types. This makes the codebase easier to maintain, reduces ambiguity, and minimizes the risk of passing incorrect data structures through the pipeline.

numpy

NumPy is a fundamental package for numerical computing in Python. It provides fast and efficient support for arrays, matrices, and mathematical operations, serving as the backbone for scientific computing.

In this project, NumPy supports vectorized operations for similarity calculations and aggregation of statistics. It ensures efficient handling of numerical data such as similarity scores across multiple page sections, making the analysis scalable and precise.

sentence_transformers

The sentence_transformers library extends transformer models for sentence-level and semantic similarity tasks. It allows text to be encoded into dense vector embeddings, enabling advanced natural language understanding and comparison.

Here, sentence_transformers powers the semantic similarity engine by converting text sections into embeddings. These embeddings are the basis for measuring overlap between pages, providing a deep semantic layer that goes beyond surface-level keyword matching. This ensures accurate detection of cannibalization risks in real-world content.

torch

PyTorch is a deep learning framework that provides tools for building, training, and running machine learning models. It supports dynamic computation graphs, GPU acceleration, and efficient tensor operations.

In this project, PyTorch acts as the underlying engine for transformer-based models. The embeddings generated by sentence-transformers depend on PyTorch for execution, making it a critical component in transforming raw text into numerical vectors.

transformers.utils

The transformers library is a leading framework for working with transformer models such as BERT, RoBERTa, and DeBERTa. The utils submodule allows fine control over logging and progress indicators during model execution.

For this project, transformers.utils is configured to suppress verbose logging and progress bars. This ensures a clean execution environment where outputs focus on meaningful results rather than overwhelming technical logs, making the workflow more professional and report-friendly.

matplotlib.pyplot

Matplotlib is a core Python library for data visualization. The pyplot interface provides functions for creating a wide range of static, animated, and interactive plots.

In this project, Matplotlib is used to generate visual reports of overlap patterns, similarity distributions, and top competing sections. These visuals transform raw statistics into clear insights that can be easily understood, making the findings actionable.

seaborn

Seaborn is a statistical data visualization library built on top of Matplotlib. It simplifies the process of creating attractive and informative plots with built-in support for advanced features like heatmaps and categorical plots.

Here, Seaborn enhances the visual presentation of overlap results by applying polished styles and simplifying the creation of comparative charts. It ensures that similarity distributions, section counts, and other metrics are presented in a way that is both professional and easy to interpret.

Function _is_pseudo_heading

Overview

The _is_pseudo_heading function identifies heading-like text within content that is not explicitly marked as a heading element. These pseudo headings can appear as short bold phrases, title-case sentences, or text ending with a colon. Detecting them ensures that meaningful structure within paragraphs or lists is preserved during section extraction. The function relies on multiple heuristics, such as word length, capitalization ratios, and punctuation patterns, to determine whether a text string should be treated as a heading substitute.

Function _collect_following_content

Overview

The _collect_following_content function gathers the textual content immediately following a heading-like element until the next heading is encountered. It captures content from paragraphs, lists, blockquotes, tables, and other block-level tags. This ensures that each heading is paired with its associated section content, maintaining document structure for downstream similarity analysis. By grouping these elements, the function produces coherent content blocks aligned with their corresponding headings.

Function preprocess_text

Overview

The preprocess_text function is designed to clean and normalize raw text extracted from HTML documents. The process removes irrelevant or repetitive boilerplate elements such as “read more” or “subscribe,” strips URLs, normalizes Unicode inconsistencies, and standardizes characters like quotes and dashes. It also collapses excessive whitespace to produce clean, standardized text ready for further analysis. This preprocessing ensures that downstream models and similarity calculations operate on meaningful, consistent data rather than noisy or redundant text.

Key Line of Code Explanations

· boilerplate_regex = re.compile(…):

Compiles a regular expression to identify common boilerplate phrases and optional extra patterns. This ensures frequent irrelevant content is systematically removed.

· url_regex = re.compile(r’https?://\S+|www\.\S+’):

Detects and removes embedded URLs, which are irrelevant for semantic overlap analysis.

· unicodedata.normalize(“NFKC”, text):

Ensures characters are standardized to a consistent Unicode representation, preventing variations in encoding from affecting similarity models.

· substitutions = {…} and subsequent replacement loop:

Converts typographic symbols such as smart quotes and em-dashes into simpler ASCII equivalents. This prevents semantic distortion in embeddings caused by inconsistent punctuation.

· re.sub(r”\s+”, ” “, text).strip():

Collapses multiple spaces, tabs, or line breaks into single spaces for uniform formatting.

Function extract_structured_sections

Overview

The extract_structured_sections function is responsible for extracting and organizing meaningful content sections from a webpage. Its main role is to break down raw HTML into structured components like headings and content blocks, which are later used in semantic analysis. The function operates in two phases: a primary extraction strategy and a fallback strategy. The primary approach looks for standard HTML heading tags (h2–h6) and collects the text beneath them. If the page lacks proper headings or is poorly structured, the fallback mechanism steps in, building sections from continuous text blocks such as paragraphs and lists. It also detects potential pseudo-headings (short, title-like lines) and, if necessary, groups content by a word budget. The output is a structured dictionary with the page URL, title, and an array of sections, each containing a heading, its hierarchical level, and the associated content.

This modular design ensures robustness across different types of webpages. Some pages may be well-structured with clear headings, while others may be irregular, missing standard markup, or cluttered with scripts and irrelevant HTML elements. The fallback mechanisms safeguard against these variations, ensuring that the function always produces usable sections. By enforcing minimum word thresholds and cleaning unwanted tags, the function provides clean and meaningful text blocks suitable for further processing, such as embedding, similarity analysis, or intent detection.

Key Line of Code Explanations

· resp = requests.get(url, timeout=request_timeout, headers={“User-Agent”: “Mozilla/5.0”})

This line fetches the webpage using the requests library. The User-Agent header mimics a standard browser to reduce the chance of being blocked, while the timeout prevents indefinite waiting on unresponsive sites.

This loop removes irrelevant or non-content elements such as scripts, styles, navigation menus, and forms. Eliminating these ensures that only meaningful content is retained for analysis.

The function captures the page’s main title (typically found in the <h1> tag) and applies preprocessing to normalize it. If no <h1> exists, the title is set to None.

heading_tags = soup.find_all([“h2”, “h3”, “h4”, “h5”, “h6”])

This line searches for all secondary headings in the document, which form the backbone of the primary extraction strategy. Each heading anchors a section.

For each heading found, the function collects and cleans the content that follows it until the next heading appears. This ensures that each section includes both the heading and the related text.
if _is_pseudo_heading(blk, max_words=pseudo_heading_max_words):

This condition detects pseudo-headings—short lines resembling titles but not formally marked as headings in HTML. They serve as markers for splitting text into structured sections during fallback processing.

If no pseudo-headings are detected, the function groups text by word count, creating balanced chunks. This ensures the fallback output is not too large or too small for analysis.
sections = [s for s in sections if len((s.get(“content”) or “”).split()) >= min_words]

A final filtering step enforces the minimum word threshold, ensuring only substantial sections are retained. Very short or empty sections are discarded to maintain quality.

Function chunk_sections

Overview

The chunk_sections function is designed to break large content sections into smaller, manageable chunks that fit within the constraints of embedding models. Embedding models typically have token limits, and sending very long text sections risks truncation or failure. To address this, the function uses word count as a proxy for token length and applies chunking with an overlap. Overlap ensures that consecutive chunks share some words, preserving continuity of meaning across boundaries. This way, important context is not lost during segmentation.

The function operates section by section, taking the structured output from extract_structured_sections as input. For each section, it determines whether the text fits within the max_words limit. If yes, the section remains a single chunk. If not, the section is divided into multiple overlapping chunks. Each chunk is stored as a dictionary with a unique chunk_id and the associated text. The updated structure enriches each section with a new field, chunks, ensuring downstream tasks (such as embeddings, similarity checks, or classification) can work reliably with length-controlled inputs.

Key Line of Code Explanations

· words = section[“content”].split()

The section’s content is split into a list of words. This creates a simple and efficient way to approximate token length without relying on the model’s tokenizer.

If the section length is within the safe word limit, the section is kept as a single chunk. The heading is prepended to the content to maintain context, and the chunk is assigned a unique identifier.

For sections exceeding the word limit, this loop divides the text into smaller parts. Each chunk includes up to max_words words, prefixed by the section heading.

start = end – overlap

After each chunk, the starting index is shifted forward but overlaps by overlap words with the previous chunk. This overlap ensures continuity, preventing the boundary between chunks from breaking context flow.

The generated chunks are stored back into the section under the key chunks. The enriched section is appended to the updated list of sections, maintaining the original structure while adding detailed granularity.

Function load_embedding_model

Overview

The load_embedding_model function is responsible for loading a SentenceTransformer model, which is the core engine for generating embeddings. Embeddings are numerical vector representations of text that capture semantic meaning and relationships between words, sentences, or sections. By converting text into embeddings, advanced similarity calculations and semantic alignment tasks become possible, which is central to SEO-related analysis.

The function uses HuggingFace’s sentence-transformers library to load a model such as “all-mpnet-base-v2”, which is widely known for producing high-quality embeddings in terms of semantic closeness. It includes robust error handling to ensure that, if loading fails due to network, environment, or compatibility issues, the error is logged clearly and a runtime exception is raised. This makes the function safe for real-world usage where reliability and transparency are important.

Key Line of Code Explanations

· model = SentenceTransformer(model_name)

This line initializes and loads the SentenceTransformer model specified by model_name. By default, it loads “sentence-transformers/all-mpnet-base-v2”, a strong general-purpose embedding model. The model is downloaded from HuggingFace if not already cached locally.

Sentence Transformer Model: all-mpnet-base-v2

Model Overview

The sentence-transformers/all-mpnet-base-v2 model is part of the Sentence-Transformers library, which builds on top of Hugging Face’s transformers. It is a pre-trained transformer model fine-tuned for generating high-quality sentence embeddings. These embeddings capture semantic meaning, making it possible to compare entire sentences, paragraphs, or sections based on meaning rather than exact wording.

Unlike traditional keyword-based similarity approaches, this model allows comparisons at a semantic level, where two texts can be recognized as similar even if they use different words or phrasing. This makes it particularly useful for detecting content overlap or duplication in SEO contexts.

Model Architecture

The underlying architecture is based on MPNet (Masked and Permuted Network), which is an advancement over BERT and RoBERTa. MPNet combines the benefits of masked language modeling (predicting missing words) and permutation-based modeling (capturing word order more effectively).

Key aspects of the architecture include:

Transformer encoder: A stack of self-attention layers that learns contextual relationships between words.
Permutation-based training: Improves understanding of word dependencies by predicting tokens in various orders.
Sentence embedding fine-tuning: The model is specifically optimized for generating embeddings that represent the meaning of full sentences or chunks of text, not just individual words.

This results in embeddings that are both dense and semantically rich, making them excellent for similarity tasks.

Model Features

Dimension size: Each embedding is represented as a 768-dimensional vector.
Pre-trained on large-scale datasets: Trained on diverse data, covering many real-world text scenarios.
State-of-the-art performance: Consistently scores highly in semantic textual similarity benchmarks.
Efficiency: Optimized for fast inference, making it practical for real-world applications like SEO audits.
Versatility: Works for tasks like clustering, semantic search, duplicate detection, and paraphrase identification.

Importance in SEO

In SEO, content overlap and cannibalization are critical issues where multiple pages on the same domain unintentionally compete for the same search intent. Traditional keyword matching fails to detect subtle overlaps when different terms are used to express similar meaning.

The all-mpnet-base-v2 model solves this by:

Detecting semantic similarity between page sections.
Identifying near-duplicate or redundant content even when rephrased.
Differentiating between pages that are genuinely unique versus those targeting the same topic.
Supporting actionable SEO decisions such as whether to merge pages, adjust targeting, or add internal linking.

How Used in This Project

In this project, the model is used to encode webpage content into embeddings at the section or chunk level. Each chunk of text is transformed into a dense vector that captures its semantic meaning. These embeddings are then compared using cosine similarity to quantify how much two chunks (or pages) overlap in meaning.

Specifically, the workflow is:

Webpage content is extracted and preprocessed.
Content is broken into manageable chunks (to fit model limits).
Each chunk is passed through the all-mpnet-base-v2 model to generate embeddings.
Cosine similarity between embeddings is computed to detect overlaps.
Overlap statistics are aggregated to provide actionable SEO recommendations.

This ensures the model is not just providing raw embeddings, but enabling a full pipeline that translates into business value for SEO strategists.

Function embed_sections

Overview

The embed_sections function is designed to generate embeddings for every section of a webpage. It operates at the chunk level, ensuring that long content pieces, previously split by the chunk_sections function, are properly embedded without exceeding model limitations. If chunks do not already exist, the function falls back to embedding the raw section content.

Each chunk is processed with the SentenceTransformer model, converting text into a dense numerical vector representation. These embeddings form the foundation for semantic similarity analysis, intent matching, and identifying content cannibalization patterns. Embeddings are stored directly inside each chunk dictionary, making the enriched data structure ready for downstream processes such as similarity scoring, clustering, or visualization.

The function also incorporates robust error handling, ensuring that if a particular chunk fails to embed, the process continues while logging the issue. This allows the pipeline to remain resilient and avoids complete failure when encountering problematic text data.

Key Line of Code Explanations

This section ensures that embedding can proceed even if no pre-chunks exist. A fallback is created by embedding the entire section as a single chunk, preserving robustness.
chunk[“embedding”] = model.encode(chunk[“text”], show_progress_bar=False).tolist()

Each chunk’s text is passed to the SentenceTransformer model, which generates an embedding. The vector is converted to a list to ensure compatibility with JSON-like structures that may not support NumPy arrays directly.

Function cosine_similarity

Overview

The cosine_similarity function calculates the cosine similarity between two embedding vectors. Cosine similarity is a widely used metric in natural language processing and information retrieval to measure how similar two pieces of text are in terms of their vector representations.

The value ranges between -1 and 1, but since embeddings in this project are non-negative and normalized, results typically fall between 0 and 1, where:

1.0 indicates perfect similarity (vectors point in the same direction).
0.0 indicates no similarity (vectors are orthogonal).

This function is fundamental for comparing chunks of content across different web pages to identify semantic overlaps, redundancies, and potential cannibalization. By abstracting similarity calculation into a dedicated function, the pipeline ensures consistency, clarity, and reusability.

Key Line of Code Explanations

Both input vectors are converted into NumPy arrays. This allows the function to leverage efficient linear algebra operations from NumPy, ensuring fast and reliable computations.
sim = float(np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))

This line implements the cosine similarity formula:

cosine_similarity=v1⋅v2||v1||×||v2||

It calculates the dot product of the two vectors, then divides by the product of their magnitudes (L2 norms). This normalizes the comparison, making it scale-invariant.

Function detect_page_overlap

Overview

The detect_page_overlap function identifies semantic similarities between two web pages at the chunk level. By comparing embeddings of chunks from both pages, it detects where meaningful overlaps occur and categorizes them into threshold ranges (moderate, high, strong).

This function not only lists all overlapping sections but also produces summary statistics about the extent of overlap. These statistics are crucial for downstream interpretation, such as detecting risks of cannibalization or identifying opportunities for consolidation.

In short, this function operationalizes the project’s core goal: measuring and quantifying semantic overlap across pages.

Key Line of Code Explanations

Iterates through all sections and their chunks in the first page. Each chunk will be compared with every chunk in the second page. This ensures comprehensive pairwise comparison across the two pages.

Computes similarity between two chunks using the cosine_similarity function. The score is added to a list (sims) so that average similarity and counts by thresholds can later be calculated.

Only similarities above the moderate threshold are stored as overlaps. This prevents trivial similarities (noise) from cluttering the analysis. Each overlap entry includes the matched sections, chunk IDs, and the similarity score.

Summarizes the results into actionable metrics:

Total overlaps: How many section-level overlaps were found.
Strong, high, moderate counts: Distribution of overlaps across similarity intensity levels.
Average similarity: Overall similarity signal between the two pages.

Produces a structured dictionary that combines detailed overlap listings with aggregated statistics. This makes the result usable for both direct inspection (what content overlaps) and summary-level reporting (how much overlap exists).

Function compare_all_pages

Overview

The compare_all_pages function systematically checks for semantic content overlaps across multiple webpages. Instead of looking at just one pair of pages, it performs pairwise comparisons between all pages in the input list, making sure no possible duplication or cannibalization is missed.

Its workflow is:

Loops through all pages and compares each pair.
Calls detect_page_overlap for each pair to get similarity insights.
Sorts the overlapping sections by similarity score (highest first).
Records overlap statistics and section-level similarities for every pair.
Returns a list of all comparisons, ensuring transparency about both overlaps and cases where none are detected.

This function is the central engine that scales the analysis from a two-page comparison to a full dataset of webpages. It is crucial for detecting larger patterns of duplication across a whole website or content set.

Key Line of Code Explanations

· for i in range(len(pages)):

Initiates the outer loop to select one page (page_a). This sets up the baseline for comparison.

· for j in range(i + 1, len(pages)):

Ensures that each page (page_b) is compared only with subsequent pages. This avoids duplicate comparisons (e.g., A vs B and B vs A) and prevents self-comparisons.

· overlap_result = detect_page_overlap(page_a, page_b, thresholds=thresholds)

Delegates the heavy lifting to the detect_page_overlap function, which calculates chunk-level embeddings and similarity scores between the two selected pages.

· overlaps_sorted = sorted(…, key=lambda x: x[“similarity”], reverse=True)

Orders the overlaps by similarity score, ensuring that the most problematic or highly redundant sections are prioritized in the results.

· results.append(pair_result)

Aggregates the processed comparison into a growing list of results, which will later provide a comprehensive overview of overlap patterns across the entire dataset.

Function run_full_pipeline

Overview

The run_full_pipeline function is the end-to-end orchestrator of the entire semantic overlap detection system. Instead of working with individual pieces, it ties everything together into a complete workflow.

Its responsibilities include:

Loading the embedding model — ensures that the chosen sentence-transformer is available for vectorization.
Extracting structured sections from each webpage to preserve hierarchy and readability.
Chunking long sections into smaller, embedding-friendly units that avoid token-length issues.
Generating embeddings for each chunk to capture semantic meaning.
Comparing all pages pairwise to detect content overlaps and cannibalization issues.
Returning structured results containing both processed page data and detected overlaps.

This function serves as the one-stop pipeline: a user can supply a list of URLs, and the function outputs a complete analysis report without requiring manual steps.

Key Line of Code Explanations

· if not thresholds: thresholds = {“strong”: 0.85, “high”: 0.80, “moderate”: 0.70}

Provides default similarity thresholds when none are supplied. These thresholds define the strength of overlap detection categories.

· model = load_embedding_model()

Loads the chosen sentence-transformer model. This is the foundation for embedding generation, and if it fails, the function gracefully exits with empty results.

· page_data = extract_structured_sections(url)

Retrieves and parses webpage content into structured sections, preserving headings and their associated text. This ensures context is maintained.

· chunked_page_data = chunk_sections(page_data)

Splits long sections into manageable chunks to fit embedding model token limits. Prevents truncation or information loss.

· embedded_page_data = embed_sections(chunked_page_data, model)

Generates embeddings for each chunk. These embeddings are critical for later semantic similarity comparisons.

· processed_pages.append(embedded_page_data)

Collects all processed page data into a central list for pairwise comparison.

· overlaps = compare_all_pages(processed_pages, thresholds=thresholds)

Performs pairwise comparisons across all processed pages. This is where overlap detection is executed at scale.

· final_result = {“pages”: processed_pages, “overlaps”: overlaps}

Packages both page-level data and overlap results into a single dictionary, making the output structured, reusable, and ready for reporting or visualization.

Function _recommend_action

Overview

The _recommend_action function transforms raw similarity statistics into practical optimization guidance. Instead of just reporting numbers, it interprets the level of semantic overlap between two pages and translates it into clear, actionable recommendations.

Its logic is hierarchical:

Strong duplication across multiple sections → suggests merging pages or applying canonical tags.
Significant overlap → recommends differentiating content or consolidating coverage.
Moderate overlap → points toward internal linking and refining targeting.
Low or no overlap → indicates no major action is necessary, except for optional minor adjustments.

It also provides section-level nuance, breaking down how many sections fall into strong, high, or moderate overlap ranges, and what adjustments should be made at that granularity.

This makes the output more actionable by combining page-level strategy with section-specific details.

Key Line of Code Explanations

· strong = stats.get(“strong_count”, 0)

Retrieves the count of strongly overlapping sections. Defaults to 0 if not found. Similar lines capture high_count, moderate_count, and totals.

· if avg_sim >= thresholds[“strong”] and strong >= 2:

Checks if the average similarity is very high and if at least two sections overlap strongly. This combination signals critical duplication risk requiring merging or canonicalization.

· elif avg_sim >= thresholds[“high”]:

Handles significant overlap scenarios, where differentiation of topics or consolidation is needed.

· elif avg_sim >= thresholds[“moderate”]:

Covers moderate overlap, guiding toward internal linking strategies and content refinement.

· elif total == 0:

If no overlaps exist at all, the function directly concludes that no action is required.

· details.append(f”{strong} section(s) show near-duplicate content…”)

Adds specific section-level recommendations, making the advice more granular and tailored rather than generic.

Function _safe_get

The _safe_get function is a lightweight helper designed to safely retrieve values from a dictionary. Instead of directly accessing a single key, it iterates over a list of possible keys and returns the first matching non-None value. If no match is found, it returns a provided default value.

This approach ensures flexibility in handling dictionaries that may have slightly different structures (for example, when a key might be named page_a_url in one case and url_a in another). By using this function, downstream code avoids repetitive if key in dict checks and ensures cleaner, more fault-tolerant logic.

Function _snippet

The _snippet function is another helper utility that generates readable previews of long text sections. It trims large blocks of content into shorter, more manageable snippets, ensuring that output remains digestible.

It works by normalizing whitespace, limiting the output length, and attempting to cut at natural text boundaries such as periods, exclamation points, or spaces. This design ensures that truncated content does not abruptly cut off mid-word or mid-sentence, keeping the previews professional and easy to understand.

The function is particularly important in overlap reporting, where entire sections may be too long to display, and showing only a preview makes the report more user-friendly.

Function display_overlaps

The display_overlaps function serves as the primary reporting utility in the workflow. It takes the final results of semantic comparison and presents them in a structured, readable format. This includes:

Listing compared page pairs.
Providing a numerical summary of overlaps across threshold ranges (strong, high, moderate).
Displaying top overlapping sections with short content snippets.
Offering tailored recommendations for action at the page level.

The function relies on _safe_get to handle flexible dictionary keys and _snippet to make section content previews concise. At the end of each page pair report, it calls _recommend_action to translate similarity statistics into clear next steps.

This design ensures the results are not just technical outputs but are presented as actionable insights that can be immediately understood and acted upon.

Result Analysis and Explanation

This section provides a detailed interpretation of semantic overlap results across multiple webpages, highlighting key insights, overlap severity, and actionable implications. The analysis combines quantitative metrics with practical understanding of content similarity in the context of SEO.

Overview of Overlap Metrics

The overlap analysis evaluates semantic similarity at a section-level granularity, providing a robust understanding of content duplication or alignment across pages. Key metrics include:

· Total overlaps: Indicates how many sections share semantic similarity above defined thresholds between a pair of pages. Higher counts reflect greater overlap and potential risk of content cannibalization.

· Severity levels:

Strong overlap: Sections with near-duplicate content (typically ≥ 0.85 similarity) which may warrant immediate corrective actions such as merging or canonicalization.
High overlap: Sections with significant similarity (≈ 0.80–0.85), suggesting refinement or differentiation may be needed.
Moderate overlap: Sections with measurable similarity (≈ 0.70–0.80) where small adjustments, added context, or internal linking can reduce ambiguity.

· Average similarity: Provides an overall sense of semantic closeness between pages, summarizing section-level comparisons into a single representative score.

This multi-tiered approach ensures a balanced assessment, identifying both high-risk duplicate content and moderate overlaps that may subtly impact page performance.

Section-Level Insights

· Sections are compared individually, and the top overlaps are highlighted to pinpoint specific content areas causing semantic similarity.

· Top overlapping sections reveal which portions of the content share meaning across pages, enabling focused editorial action:

Strong overlaps often represent near-duplicate content that may directly compete for search visibility.
Moderate overlaps suggest similar topics or examples, which can be enhanced with unique examples, additional data, or clarifying content to maintain distinct page value.

· Section-level granularity helps in targeted content optimization rather than broad-stroke changes, improving SEO efficiency and user experience.

Recommended Actions Based on Overlaps

The recommendation logic considers both the severity of overlaps and section counts:

No overlaps detected: No immediate action is needed; pages are semantically distinct.
Low overlap / moderate overlaps: Minor adjustments, such as internal linking or adding unique content, can enhance differentiation.
High or strong overlaps: Content should be refined, rewritten, or consolidated to prevent internal competition, improve ranking focus, and maintain clarity in search intent coverage.

This action-oriented guidance ensures that recommendations are practical, prioritized, and aligned with SEO objectives.

Visualization Insights

The results are complemented by several visualization plots, providing intuitive understanding of content overlaps:

· Pairwise Overlap Counts

A grouped bar chart displays the number of overlapping sections per page pair, segmented by severity levels (strong, high, moderate).
Enables quick identification of which page pairs have the highest overlap burden and which sections may require immediate attention.

· Average Similarity Heatmap

Shows the average semantic similarity between all page pairs in a color-coded matrix.
Highlights clusters of semantically similar pages, helping SEO strategists identify potential content redundancy across multiple pages.

· Top-K Section Overlaps

A horizontal bar chart highlighting the top sections with highest similarity across all pairs.
Provides actionable insights at section-level resolution, helping content teams focus efforts on areas with maximum risk of cannibalization.

· Overall Overlap Severity Distribution

A pie chart aggregates the counts of strong, high, and moderate overlaps across all page pairs.
Offers a global view of overlap severity, giving a clear sense of overall content duplication trends and prioritizing corrective actions.

Practical Interpretation for SEO

Content differentiation: Moderate overlaps suggest opportunities for enriching content with unique examples, data, or case studies, enhancing the page’s value proposition.
Internal linking strategy: When moderate overlaps exist without strong duplication, linking related pages improves contextual relevance while maintaining distinct content.
Risk management: Strong overlaps highlight pages competing for the same semantic intent. Consolidation or canonicalization ensures search engines clearly understand the primary source, preventing ranking dilution.
Efficiency in content auditing: Section-level analysis combined with visual summaries allows teams to quickly identify high-risk areas without manually reviewing every page, making large-scale SEO audits manageable.

Summary

The analysis delivers a comprehensive, multi-dimensional view of semantic overlap:

Quantitative metrics identify the number and severity of overlapping sections.
Section-level insights pinpoint specific content requiring attention.
Visualizations provide intuitive understanding and actionable guidance for editorial and SEO teams.

Overall, this approach ensures that overlapping content is managed effectively, improving content quality, search engine ranking efficiency, and user experience across the website.

Q&A Section: Result Interpretation and Actions

How can I quickly identify which pages on my website might be competing for the same search intent?

This project analyzes semantic overlap between pages at the section level, highlighting areas of potential cannibalization. By examining overlap counts, severity levels, and average similarity metrics, you can identify which pages share highly similar content or target the same intent. The visualizations, like pairwise overlap bar charts and heatmaps of average similarity, allow you to immediately spot clusters of pages with overlapping content.

From these insights, you can prioritize content audits. Pages with high semantic similarity can be evaluated for consolidation, differentiation, or internal linking. This ensures that each page maintains a distinct focus, reducing internal competition and improving overall SEO performance without manual review of each page.

What type of content adjustments are recommended based on the overlap insights?

Based on the section-level overlap analysis, the platform provides nuanced guidance:

For strong overlaps, consider merging pages or applying canonical tags to avoid multiple pages competing for the same ranking.
For high similarity overlaps, differentiate content by targeting unique subtopics, adding original examples, or focusing on slightly different user intents.
For moderate overlaps, minor adjustments such as clarifying examples, expanding explanations, or adding internal links between pages can help distinguish content while improving contextual relevance.

This systematic approach ensures that editorial efforts are targeted and efficient, maximizing SEO impact without unnecessary rewriting of low-risk content.

How can I use this analysis to optimize internal linking strategy?

Internal linking is most effective when pages are related but distinct. Overlap metrics highlight sections with moderate semantic similarity, which are ideal candidates for linking. By creating contextual links between overlapping sections, you guide users and search engines, reinforcing topic relevance while avoiding cannibalization.

The top-k section overlap visualization helps identify exact content areas that would benefit from linking. This ensures that internal linking is not random but strategically aligned with semantic relationships, improving both UX and search engine understanding.

How does this project help in content prioritization for SEO?

The analysis provides actionable prioritization based on overlap severity and frequency. Pages with multiple strong or high overlaps indicate high-risk areas where search intent conflicts could harm ranking performance. Conversely, pages with low overlap are safe to optimize or expand without impacting other pages.

By using the severity distribution and visual summaries, allocate resources efficiently—focusing on high-risk content first while planning enhancements for moderately overlapping content. This ensures that SEO efforts are both data-driven and cost-effective.

Can this system guide me in managing large-scale content updates?

Yes. The project allows you to process multiple pages simultaneously and provides structured outputs that summarize overlaps, highlight top overlapping sections, and suggest recommended actions. For websites with numerous pages, this means you can quickly identify high-priority areas without manual inspection.

Visualization modules like heatmaps and bar charts help in strategic planning by showing which pages or sections may benefit from consolidation, differentiation, or linking. Overall, this supports scalable, precise content optimization, saving time while maintaining SEO effectiveness.

How can the overlap insights improve content strategy?

By understanding semantic similarities across pages, the analysis enables data-driven content planning. You can determine which topics are overrepresented, which pages need differentiation, and which sections could serve as hubs for internal linking. This ensures that each page uniquely addresses user needs, improves topical authority, and strengthens overall site structure.

The insights also inform future content creation, helping teams avoid unintended duplication and focus on high-value topics that complement existing pages, enhancing both visibility and user experience.

How reliable are these recommendations for preventing SEO cannibalization?

Recommendations are based on quantitative semantic overlap metrics, ensuring objectivity in identifying content redundancy. Section-level granularity, combined with multiple severity thresholds, ensures that even subtle overlaps are detected. The system is designed to prioritize actionable insights, highlighting areas that could impact rankings and providing practical guidance for differentiation, linking, or consolidation.

This approach allows SEO teams to take confident, informed actions that reduce internal competition, optimize content coverage, and improve search visibility across the site.

Final Thoughts

The Semantic Content Cannibalization Detector provides a comprehensive, data-driven approach to identifying and managing overlapping content across your website. By analyzing pages at a section-level granularity and leveraging advanced semantic embeddings, the system highlights areas where multiple pages target the same user intent.

This project enables clients to make informed decisions about content consolidation, differentiation, and strategic internal linking. The structured insights allow for efficient prioritization of high-impact areas, ensuring that each page maintains a distinct focus and contributes effectively to the overall SEO strategy.Through clear visualizations, detailed overlap metrics, and actionable recommendations, the project transforms complex semantic relationships into practical, client-ready guidance. Ultimately, this ensures that your website’s content ecosystem is optimized for search performance, user clarity, and long-term relevance, providing measurable value in day-to-day SEO operations.

Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.