Qwen-Powered SEO Alignment - Improving Query Matching

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This project focuses on Qwen-powered semantic alignment in SEO, showcasing how advanced language models can enhance query matching and content optimization. The system processes multiple queries and webpages, generating semantic comparisons that highlight how effectively each page addresses search intent.

Instead of relying on keywords alone, the approach leverages Qwen’s ability to capture contextual meaning, intent variation, and semantic similarity. This enables clients to see which pages are best aligned with user queries, where optimization opportunities exist, and how different URLs compare in addressing targeted search intents.

The results are presented through structured outputs and focused visualizations, giving SEO strategists actionable insights for content alignment, optimization, and improved search performance.

Project Purpose

The purpose of this project is to establish a robust framework for assessing how effectively webpage content aligns with search queries. Conventional keyword-based approaches often fail to capture the depth of meaning and intent that influence search rankings and engagement. This project addresses that limitation by integrating Qwen, a state-of-the-art language model, to evaluate semantic alignment between queries and content sections.

Qwen-powered semantic analysis ensures that alignment is based not only on surface-level terms but also on contextual relationships. This enables the identification of content coverage gaps, the measurement of relevance with greater precision, and the prioritization of content sections that hold the highest value for search visibility.

The framework is designed to support two key outcomes:

Clarity of alignment: highlighting areas where queries are well addressed and where improvements are needed.
Efficiency of analysis: managing multiple URLs and queries within a unified workflow, enabling large-scale assessment without sacrificing accuracy.

The overarching purpose is to transform advanced natural language processing capabilities into actionable insights that guide stronger content strategies, improve visibility, and enhance competitiveness in search results.

Project’s Key Topics Explanation and Understanding

Qwen

Overview

Qwen is a family of large-scale language models developed by Alibaba Group as part of their effort to advance natural language processing (NLP) and artificial intelligence. The name “Qwen” reflects its positioning as a versatile foundation model designed to support multiple real-world applications, including search, recommendation, dialogue, and content generation.

Built on modern transformer architecture, Qwen models are trained on vast and diverse datasets, enabling them to learn linguistic structure, semantic relationships, and contextual nuances across multiple languages and domains. This broad training makes Qwen particularly suitable for tasks where precise understanding of meaning and intent is crucial.

Development and Evolution

Alibaba introduced Qwen to address the need for scalable, general-purpose AI models capable of performing a wide range of natural language tasks with high accuracy. Over successive versions, Qwen has evolved into a competitive family of models that can operate effectively in multilingual settings, handle long text inputs, and provide fine-grained semantic interpretation.

Qwen’s development was driven by two primary goals:

To create a foundation model that could generalize across diverse industries and tasks without requiring complete retraining.
To enhance performance in semantic tasks such as reasoning, alignment, and relevance detection — areas where traditional keyword-driven approaches often fall short.

By combining large-scale pretraining with advanced fine-tuning methods, Qwen achieves robust performance on both general-purpose benchmarks and specialized downstream tasks.

Technical Characteristics of Qwen

At its core, Qwen leverages the transformer architecture, which has become the industry standard for NLP models. This architecture enables Qwen to:

Capture long-range dependencies in text through attention mechanisms.
Process input bidirectionally to understand both preceding and following context.
Generate embeddings that encode semantic information in dense vector representations.

These embeddings form the backbone of semantic understanding in Qwen. By representing words, phrases, and entire documents as high-dimensional vectors, Qwen can compare semantic similarity at a conceptual level rather than relying solely on surface-level word matching. This ability is key to aligning user queries with webpage content in a meaningful way.

Use of Embedding Models within Qwen

One of Qwen’s most impactful applications lies in its embedding models, which are specialized versions designed to transform text into numerical vectors. These embeddings make it possible to compute similarity between queries and documents, detect thematic overlaps, and identify semantic gaps.

Embedding-based approaches are significantly more powerful than keyword methods because they capture context, synonyms, and related concepts. For instance, “digital marketing strategy” and “online advertising plan” may not share many exact words, but embeddings reveal their semantic closeness. Qwen’s embedding models enable this type of conceptual mapping, making them invaluable for tasks such as search relevance, clustering, recommendation, and, as in this project, semantic alignment for SEO content.

Qwen in the Broader AI Landscape

Qwen is part of a global trend toward foundation models that serve as adaptable backbones for many industries. Similar to other large-scale language models, Qwen can be fine-tuned or applied directly for specialized purposes. Its multilingual and context-sensitive design makes it particularly relevant for international businesses and digital platforms where precise communication and intent alignment are critical.

By combining deep semantic understanding with flexible embeddings, Qwen enables organizations to move beyond superficial text analysis toward strategies that directly address user needs and search behaviors.

Semantic Alignment

Semantic alignment refers to the degree of similarity in meaning between two texts. In this project’s context, it is the measure of how closely webpage content aligns with a given query. Unlike simple keyword overlap, semantic alignment emphasizes intent, structure, and thematic consistency. For example, a query about “best running shoes for marathon training” aligns semantically with content discussing durability, cushioning, and long-distance suitability, even if the exact words differ.

Strong semantic alignment indicates that content is highly relevant and useful in answering a query, while weak alignment highlights areas where content may not fully address the search need.

Query Matching

Query matching is the process of evaluating how well a user’s search query corresponds with available content. Traditional query matching has often been limited to keyword-based systems, where direct word overlap determines relevance. However, modern approaches, powered by advanced language models, enable query matching at a conceptual level. This shift ensures that queries are matched not only to surface-level keywords but also to related ideas, synonyms, and contextual expressions.

This deeper level of query matching is essential in search engine optimization, where the goal is to ensure that content responds effectively to real-world search behaviors and evolving patterns of user intent.

Content Optimization

Content optimization refers to the practice of refining and structuring content so that it effectively addresses user queries and aligns with search algorithms. While optimization once revolved around keyword density and placement, it now requires a broader strategy that includes semantic relevance, intent coverage, and content depth.

By understanding semantic alignment and query matching, optimization can be approached more strategically. The focus shifts from simply including keywords to creating content that resonates with user needs and provides comprehensive coverage of relevant topics. In this context, optimization becomes a balance between linguistic sophistication and strategic structuring, ensuring that content performs well both for human readers and search engines.

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Q&A — Understanding Project Value and Importance

Why is this project important in SEO?

Modern search engines evaluate not just keywords but the meaning and intent behind queries. Traditional keyword-only methods fail to capture this nuance, which often leads to mismatches between user intent and webpage content. This project introduces a semantic-first approach, enabling deeper alignment between queries and content. The importance lies in bridging the gap between how people search and how information is presented, resulting in stronger search visibility, higher ranking potential, and more meaningful user engagement.

What role does Qwen play in this project?

Qwen is a family of advanced language models designed for natural language understanding and reasoning. Developed as a state-of-the-art large language model, it is trained on diverse multilingual and domain-rich datasets, enabling it to capture context, nuance, and meaning with high precision. Within this project, Qwen serves as the semantic backbone — powering the ability to understand queries and content beyond surface-level keywords. This ensures alignment is based on meaning rather than word overlap, which is critical in today’s search landscape.

What features make this project stand out?

Semantic Understanding of Content: Goes beyond keywords to capture true intent and context.
Granular Subsection-Level Insights: Breaks long-form content into sections and analyzes them individually for alignment with specific queries.
Scalability Across Queries and Pages: Designed to handle multiple queries and URLs efficiently, ensuring adaptability to real-world SEO needs.
Action-Oriented Insights: Structured analysis that directs optimization efforts where they matter most.
Integration-Ready Outputs: Designed for seamless integration into SEO workflows, dashboards, and planning processes.

Why is Qwen particularly suitable for SEO-focused projects?

Search engine optimization thrives on the ability to match diverse user queries with the most relevant and high-quality content. Qwen’s advanced language modeling capabilities make it particularly well-suited for this task because:

It captures contextual meaning rather than just literal word matches.
It understands synonyms, paraphrased questions, and variations of user intent.
It adapts well to multilingual and multi-domain contexts, supporting global SEO strategies.
It provides consistent semantic alignment, which is essential for building trust in optimization efforts.

How does this project contribute to building stronger content strategies?

By systematically identifying how queries semantically align with content sections, the project provides clarity on what areas of a webpage directly serve user intent and which areas fall short. This knowledge informs better content structuring, prioritization of updates, and clearer editorial planning. The outcome is a content strategy built on semantic strength — one that anticipates search intent and delivers relevance across entire pages.

What is the long-term value of adopting such a semantic-first approach?

Adopting semantic alignment ensures future-proofing SEO strategies. As search engines continue to evolve, they rely increasingly on semantic understanding and contextual ranking. A system built on models like Qwen positions websites to adapt seamlessly to these changes. The long-term value includes improved alignment with evolving algorithms, sustained organic visibility, and content ecosystems that remain competitive in highly dynamic search environments.

Function fetch_html

Overview

The fetch_html function retrieves the raw HTML content from a given URL. Its role is to provide a reliable input for structured content extraction. It includes error handling and polite request delays, which ensures stable operation across multiple web pages, even when some URLs fail or respond slowly.

Key Code Explanations

time.sleep(delay)

Introduces a delay between requests to avoid overloading web servers or being blocked by anti-bot mechanisms. This is essential for large-scale SEO analysis where multiple pages are fetched sequentially.

Sends an HTTP GET request with a browser-like user-agent. This increases compatibility with sites that serve different HTML for unknown agents or block automated requests.

Function clean_html

Overview

clean_html removes unnecessary or non-content elements from HTML, such as scripts, styles, navigation, headers, footers, and forms. Cleaning ensures that only meaningful textual content is processed, which improves semantic analysis accuracy and reduces noise in embedding computations.

Key Code Explanations

soup = BeautifulSoup(html_content, “lxml”)

Parses the raw HTML into a BeautifulSoup object for structured traversal and manipulation. Using “lxml” provides fast and reliable parsing for complex pages.

Iterates over non-content HTML tags and removes them entirely from the DOM. This ensures no hidden or nested content (like ads, scripts, or navigation) affects semantic analysis.

Function _clean_text

Overview

The _clean_text function normalizes raw textual content by collapsing extra whitespace and ensuring consistent spacing. This preprocessing step ensures that downstream analysis, including embeddings and similarity calculations, works on clean and predictable text.

Key Code Explanations

return ” “.join(text.split())

Splits the string by any whitespace sequence and joins it back with single spaces.
Removes extra spaces, tabs, and newlines.
Ensures consistent formatting for block-level content, which is critical for semantic embeddings to produce reliable similarity scores.

Function _split_text

Overview

_split_text divides long text into smaller chunks, preserving word boundaries. Large content blocks can be broken into manageable units suitable for embedding computation, preventing embedding models from exceeding maximum token limits and improving semantic granularity.

Key Code Explanations

Checks if the text is already short enough.
Returns it as a single block if it fits within the maximum character limit.
Avoids unnecessary splitting for short content.

Iterates over words and accumulates them until reaching max_block_chars.
Creates natural splits near word boundaries, maintaining readability and semantic integrity.
Resets the counter and buffer for the next chunk.

Appends any remaining words as a final chunk.
Ensures no text is lost in the splitting process.

Function _extract_blocks

Overview

The _extract_blocks function parses the cleaned HTML content into a hierarchical structure of sections, subsections, and blocks. This structure enables precise semantic analysis, query alignment, and content coverage evaluation. Sections are determined by headings (h1, h2), subsections by lower-level headings (h3, h4), and content blocks by paragraph-level elements (p, li, blockquote).

Key Code Explanations

Detects whether there is only a single h1 tag, which is often used as the main page title.
Determines if the top-level title should be extracted from h1 or from <title>.
Ensures the page title is captured for reference in the structured content.

Iterates over relevant HTML elements to build the hierarchy.
Cleans each text block for consistency.
Skips empty or whitespace-only elements to avoid creating meaningless blocks.

Creates a new section whenever an h1 (except for single-h1-as-title) or h2 is encountered.
Resets subsection context to properly nest the content.
Ensures hierarchical integrity for downstream analysis.

Adds paragraph-level content to the current subsection if it meets the minimum length.
Assigns unique block IDs and captures the heading chain for traceability.
Preserves semantic context for each block, which is critical for embeddings and query alignment.

Function extract_structured_content

Overview

extract_structured_content is the top-level wrapper that orchestrates fetching HTML, cleaning, and extracting structured content. It produces a unified dictionary containing the URL, page title, and hierarchical sections, ready for embedding generation and alignment.

Key Code Explanations

Fetches raw HTML from the URL with error handling and polite delay.
Returns an empty structured result if the page cannot be retrieved, preventing downstream failures.

Cleans the HTML content to remove scripts, styles, and irrelevant elements.
Extracts the hierarchical content structure from the cleaned HTML.
Ensures the extracted sections, subsections, and blocks are clean and normalized for analysis.

Provides a fallback to extract the page title if not set during section extraction.
Guarantees that every page has a title field, which is important for result reporting and visualization.

return {“url”: url, “title”: page_title, “sections”: sections}

Returns a structured dictionary suitable for embedding computation, query alignment, and coverage analysis.
Standardizes the data structure across all pages for consistent downstream processing.

Function preprocess_text

Overview

preprocess_text prepares raw block-level text for downstream NLP tasks by normalizing characters, removing boilerplate content, and enforcing quality constraints. This ensures that only meaningful and clean text is retained for embeddings, query alignment, and similarity analysis.

Key Code Explanations

Decodes HTML entities and normalizes Unicode characters to a consistent form.
Ensures text is standardized for downstream processing, avoiding encoding inconsistencies.

Replaces non-standard punctuation, spaces, and special characters with consistent forms.
Improves text uniformity and readability for NLP tasks like embedding generation.

Filters out common boilerplate or navigation content that is irrelevant to content analysis.
Ensures only meaningful text is processed, improving signal-to-noise ratio for similarity computations.

Removes text that is too short, too long, or lacking diversity in words.
Maintains high-quality content for reliable semantic processing.

Function _chunk_text

Overview

_chunk_text splits long text into overlapping segments to prevent overly long inputs in downstream tasks. Overlapping ensures semantic continuity between chunks, preserving context for embeddings or similarity measures.

Key Code Explanations

Builds text chunks while respecting maximum character limits.
Uses overlap to maintain context between consecutive chunks, which improves embedding quality and alignment accuracy.

Function preprocess_page

Overview

preprocess_page applies preprocess_text and _chunk_text hierarchically across sections and subsections of a page. It produces cleaned blocks, optionally merges them into subsection-level text, and ensures long subsections are split into manageable chunks for downstream semantic analysis.

Key Code Explanations

Cleans each block and skips irrelevant or low-quality text.
Ensures only meaningful content contributes to the merged subsections.

Merges cleaned blocks into a single text for each subsection.
Splits long merged text into overlapping chunks if it exceeds max_chunk_chars, creating multiple subsections with part numbers for clear traceability.

Sanitizes titles by removing numbered prefixes or bullets, which may interfere with clean visualization and reporting.
Ensures human-readable titles for structured content presentation and top_k ranking display.

Returns a fully preprocessed hierarchical page, ready for embedding computation, query alignment, top_k extraction, and coverage analysis.
Standardizes the page structure to ensure consistent handling across multiple URLs and queries.

Function load_embedding_model

Overview

load_embedding_model initializes a sentence-transformer embedding model for semantic representation of text. Embedding models transform textual content into dense vector representations, which can then be used to compute semantic similarity between page subsections and queries. Device management ensures that the model runs on GPU if available, otherwise on CPU, optimizing performance for large-scale multi-page processing.

Key Code Explanations

device = device or (“cuda” if torch.cuda.is_available() else “cpu”)

Determines the runtime device automatically based on GPU availability.
Ensures efficient computation by leveraging hardware acceleration where possible, reducing embedding generation time for large content.

model = SentenceTransformer(model_name, device=device)

Loads the specified embedding model as a SentenceTransformer object, ready to encode text into vector embeddings.
Abstracts the embedding process, allowing all downstream tasks (similarity computation, top_k ranking, coverage analysis) to use consistent semantic representations.

Model Used: Qwen3-Embedding-0.6B Model

Overview

Qwen/Qwen3-Embedding-0.6B is a state-of-the-art transformer-based embedding model designed to generate high-quality semantic representations of text. It converts raw text into dense vector embeddings that capture contextual meaning, enabling the comparison of content based on semantic similarity rather than just exact keyword matches.

In this project, Qwen is used to transform both user queries and content subsections into embeddings, forming the foundation for precise semantic alignment.

Architecture

The model, as instantiated in this project, has three main components:

· Transformer Backbone (Qwen3Model)

Processes text sequences up to 32,768 tokens, allowing extremely long content blocks to be embedded without truncation.
Generates contextualized token embeddings, understanding the meaning of words in the context of surrounding content.
Preserves subtle semantic nuances, which is crucial for detecting query-content alignment in long-form SEO pages.

· Pooling Layer

Configured to use the last token of the transformer output for generating embeddings.
This design ensures that the most contextually significant token contributes to the embedding, which is particularly effective for structured content like stepwise guides or technical documentation.
Includes prompt-awareness, meaning the model can incorporate contextual cues when processing queries or specialized instructions.

· Normalization Layer

Converts embeddings to unit vectors, making them suitable for cosine similarity calculations.
Ensures consistency in similarity scores across multiple queries and pages, which is critical for comparing sections reliably.

How It Works

· Input Processing

Each query or content subsection is tokenized and passed through the transformer.
The model generates a sequence of contextual token embeddings that encode the semantic meaning.

· Pooling and Embedding Generation

The pooling layer selects the last token’s embedding as the representative vector for the entire input.
The vector is normalized, producing a 1024-dimensional embedding suitable for similarity comparisons.

· Similarity Computation

Query embeddings and subsection embeddings are compared using cosine similarity, allowing for a score between 0 and 1.
Higher scores indicate stronger semantic alignment, guiding decisions about content optimization.

Importance for the Project

High fidelity semantic matching: Qwen captures nuanced meanings beyond simple keyword overlap, ensuring that content is aligned with search intent.
Scalable to long-form content: With a maximum sequence length of 32k tokens, it can process entire pages or merged content blocks without losing context.
Supports multi-query, multi-page analysis: Embeddings are consistent and comparable across different pages and queries, enabling aggregated insights such as top-k similarity rankings and coverage gaps.
Actionable insights: The embeddings directly feed into similarity scoring, coverage gap detection, and visualization, forming the backbone of the project’s deliverables.

Practical Takeaways

Embeddings are dense vectors (1024-dimensional) that encode meaning rather than literal text.
Using cosine similarity on these embeddings allows accurate ranking of sections based on query relevance.
Qwen’s robust handling of context makes it ideal for SEO content analysis, especially for technical guides, structured documentation, and multi-intent queries.

Function embed_page

Overview

embed_page generates semantic vector representations for each subsection within a page. Using the previously loaded embedding model, it converts textual content (merged_text) into high-dimensional embeddings. These embeddings form the foundation for subsequent similarity calculations, enabling query alignment, top-k ranking, and coverage analysis.

Key Code Explanations

Iterates through the hierarchical page structure (sections → subsections).
Retrieves the cleaned and merged text from each subsection, which serves as input for embedding generation.

Handles empty subsections gracefully by assigning None to the embedding field.
Ensures downstream processes can detect missing content without breaking the workflow.

Encodes the subsection text into a numeric vector using the embedding model.
Stores the resulting embedding directly inside the subsection dictionary, keeping all semantic information locally with the content.

Catches and logs errors during embedding generation for robustness.
Returns the page object even if some subsections fail, preventing interruption of multi-page processing.

Function embed_page_batched

Overview

embed_page_batched is an optimized version of the embedding generation process for subsections. It processes multiple texts in batches, which is especially useful for pages with many subsections, reducing memory overhead and improving runtime efficiency. The function preserves the hierarchical structure of the page while storing embeddings directly within each subsection.

Key Code Explanations

Iterates through sections and subsections to collect all merged_text values.
Maintains a reference to each subsection to assign embeddings back later, ensuring the hierarchical structure remains intact.

Filters out empty or invalid texts to avoid unnecessary embedding computation.
Tracks the original indices so embeddings can be accurately mapped back to the corresponding subsections.

Generates embeddings in batches using the specified batch_size, improving performance for large numbers of subsections.
Converts results to NumPy arrays for efficient downstream calculations.

Assigns embeddings back to the correct subsections using the reference list.
Subsections without valid text are explicitly marked with None, ensuring clarity in subsequent analyses.

Function embed_queries

Overview

The embed_queries function generates embeddings for a list of query texts, converting raw query strings into dense vector representations suitable for semantic comparison with page content. These embeddings allow alignment of queries with relevant content sections based on semantic similarity, facilitating practical SEO analysis and content optimization.

Key Code Explanations

Attempts to generate embeddings using the prompt_name=”query” option to provide the model with contextual guidance for queries.
Converts embeddings to NumPy arrays for efficient numerical processing.
Hides progress bars to reduce console clutter in batch workflows.

Provides a fallback in case the model does not support prompt_name.
Ensures robust embedding generation without interrupting the workflow.

Ensures each embedding is a NumPy array of type float32.
Packages each query with its corresponding embedding in a dictionary, making downstream similarity computations straightforward and consistent..

Function compute_similarity

Overview

The compute_similarity function calculates a similarity score between a query embedding and a subsection embedding. The score quantifies semantic closeness, enabling the identification of content sections that best match a given query. The function is designed to be robust, providing multiple fallback methods to ensure a valid similarity score is always returned.

Key Code Explanations

Attempts to use the model’s native similarity method if available.
Converts the result to a float for uniform handling across pipelines.

Fallback to sentence_transformers’s cos_sim function for cosine similarity.
Works with both NumPy arrays and PyTorch tensors.
Ensures numerical consistency by converting the tensor output to a Python float.

Manual cosine similarity calculation if both the model method and util.cos_sim fail.
Normalizes vectors to prevent magnitude-related distortions.
Returns 0.0 when either vector is zero-length, avoiding division errors.

Function align_queries_to_page

Overview

The align_queries_to_page function systematically compares all query embeddings against every subsection embedding of a single page. It computes semantic similarity scores, stores them at the subsection level, and identifies the top-ranking subsections for each query. This enables clear insights into which parts of a page are most relevant to specific queries.

Key Code Explanations

query_scores = {q[“query”]: [] for q in queries}

Initializes a dictionary to collect similarity scores for each query across all subsections.
Ensures top-k computation can be performed efficiently after processing all sections.

score = compute_similarity(model, q_emb, s_emb)

Computes the similarity between the current query embedding and subsection embedding using the robust compute_similarity function.
Guarantees a numeric score even if some methods fail internally.

Stores the computed similarity score within the subsection dictionary under a results field keyed by the query text.
Enables direct access to query-level relevance at the subsection level for further analysis or visualization.

Collects all similarity scores for a query to compute top-k results.
Preserves subsection metadata to allow context-aware analysis of relevance.

Sorts the collected scores in descending order and retains only the top-k subsections for each query.
Provides a concise, actionable list of most relevant content blocks per query.

Function compute_coverage_gaps

Overview

The compute_coverage_gaps function evaluates whether each query is adequately addressed within a page’s content, both at the subsection level and the overall page level. It determines if there are “coverage gaps” by comparing similarity scores to a predefined threshold, enabling identification of content areas that may require enhancement.

Key Code Explanations

Iterates through all subsections and their stored query similarity results.
Computes a boolean coverage_gap for each query in a subsection: True if the similarity score falls below the threshold.
Enables fine-grained identification of underrepresented content at the subsection level.

Computes the page-level coverage for each query using the average similarity of the top-k subsections.
Stores results in page[“coverage_gaps”], providing a concise view of overall content coverage and highlighting queries that may require additional content.

Function main_pipeline

Overview

The main_pipeline function orchestrates the entire workflow for multi-URL, multi-query content alignment and analysis. It handles content extraction, preprocessing, embedding generation, query alignment, top-k selection, and coverage gap computation in a single, end-to-end pipeline. This ensures consistent processing across multiple pages and queries, producing a structured and actionable output for further analysis.

Key Code Explanations

Loads the embedding model for semantic representation of text.
Generates embeddings for all input queries to be used for similarity computation across pages.

page = extract_structured_content(url, min_block_chars=min_block_chars, max_block_chars=max_block_chars)

Fetches the raw HTML for a URL and parses it into hierarchical sections, subsections, and content blocks.
Provides structured content that preserves logical document flow for downstream semantic analysis.

Cleans and normalizes text within blocks, removing boilerplate or irrelevant content.
Optionally merges blocks into subsections and splits long content into manageable chunks, preparing text for embedding.

page = embed_page(embedding_model, page)

Generates embeddings for each subsection using the merged text.
Embeddings serve as the semantic representation for aligning content with queries.

page = align_queries_to_page(embedding_model, page, query_embeddings, top_k=top_k)

Computes similarity scores between query embeddings and subsection embeddings.
Stores results per subsection and identifies top-k subsections per query for easy reference.

page = compute_coverage_gaps(page, similarity_threshold=similarity_threshold)

Evaluates content coverage at both subsection and page level.
Flags subsections and pages where query coverage is below the similarity threshold, highlighting content gaps.

Function display_results

The display_results function provides a concise, readable presentation of processed page results. It highlights the top subsections for each query, including section and subsection headings, a preview of the content, similarity scores, and coverage gap status. Additionally, it summarizes page-level coverage metrics for each query, offering a quick assessment of content alignment across all pages. This function is primarily for visualization in the console and does not modify or process any data.

Result Analysis and Explanation

Top-Matching Subsections

The analysis shows the alignment of page subsections with the query intent, ranked by similarity score:

· Steps to Implement Canonical Tags for PDF, Image, and Video URLs Using HTTP Headers → Using Browser Developer Tools

Similarity Score: 0.612
Coverage Gap: False
Interpretation: This subsection demonstrates strong alignment with the query, providing highly relevant, actionable guidance. The content can be considered primary reference material for understanding how to handle different document URLs.

· Steps to Implement Canonical Tags for PDF, Image, and Video URLs Using HTTP Headers → Example: Setting a Canonical Tag for a PDF File

Similarity Score: 0.587
Coverage Gap: False
Interpretation: This subsection also aligns well with the query, offering practical examples. The slightly lower score indicates that it complements the primary content with supporting information rather than presenting the full procedure alone.

· Steps to Implement Canonical Tags for PDF, Image, and Video URLs Using HTTP Headers → Step 1: Identify the URLs That Need Canonicalization

Similarity Score: 0.536
Coverage Gap: False
Interpretation: This subsection provides partial alignment, giving introductory or preparatory information. It is relevant but less comprehensive than the top-scoring subsection.

The similarity scores demonstrate the relative relevance of each subsection. Scores above 0.6 reflect strong alignment, while scores around 0.5 indicate moderate relevance. Coverage gaps are marked as False for all subsections, confirming that content is sufficient to address the query.

Page-Level Coverage

Average Similarity Score: 0.518
Gap: False

This metric shows that the page, as a whole, sufficiently covers the query intent. The average score slightly above 0.5 indicates balanced relevance across multiple subsections. No coverage gaps are present, meaning the page content collectively provides adequate information for the topic.

Interpretation and Insights

Subsections with higher similarity scores represent the strongest sources of relevant information and can be used as key reference points.
Moderate-scoring subsections provide supplementary or preparatory content, reinforcing the overall coverage.
Page-level average similarity and coverage gap assessment provide a clear, high-level understanding of content effectiveness and alignment with the query intent.
The results allow for prioritization of content review, optimization, or internal linking based on relative alignment scores and subsection importance.

Result Analysis and Explanation

Subsection Similarity Scores and Interpretation

The similarity scores represent how closely each content subsection aligns with the respective query intent. Scores are continuous values ranging from 0 to 1, where higher values indicate stronger alignment. Understanding these scores can be structured using threshold bins:

High Alignment (≥ 0.65): Subsections are strongly relevant to the query and fully address the intended topic. These sections generally require minimal additional content or adjustments.
Moderate Alignment (0.50 – 0.64): Subsections have partial relevance. These sections are useful but may need enhancements, additional examples, or better framing to fully satisfy the query.
Low Alignment (0.35 – 0.49): Subsections show weak alignment. These sections may address tangential aspects but fail to meet the core intent. Content gaps are likely present, and restructuring or additional targeted content is recommended.
Very Low Alignment (< 0.35): Subsections have minimal relevance. These are unlikely to satisfy the query intent and may need replacement or substantial rewriting.

Coverage Gap Insights: Each subsection is also marked with a coverage gap flag, indicating whether its similarity score falls below a defined threshold. Sections flagged as gaps highlight opportunities to improve query coverage or supplement content for better relevance.

Page-Level Query Coverage

Average similarity scores are aggregated at the page level for each query. This provides a holistic view of how well the overall page addresses multiple search intents:

High Page Coverage (Average ≥ 0.60): The page effectively covers the query topic across its content blocks. Minimal improvement is required.
Moderate Page Coverage (0.50 – 0.59): The page partially addresses the query. Some subsections may be strong while others are weak, indicating room for optimization.
Low Page Coverage (0.40 – 0.49): The page covers the topic inconsistently. Several subsections fall below the alignment threshold, suggesting targeted improvements are needed.
Very Low Page Coverage (< 0.40): The page is poorly aligned with the query intent. Content restructuring, addition of new sections, or rewriting is advised.

This aggregation allows for quickly identifying queries that are underperforming at a page level and require strategic attention.

Cross-Query and Multi-Page Observations

Across multiple queries and pages, several patterns emerge:

Query-Specific Strengths: Certain queries consistently achieve high subsection and page-level similarity, indicating that content across pages is highly relevant and well-structured for those topics.
Query-Specific Gaps: Other queries show lower average scores and more frequent coverage gaps, highlighting areas where content development is insufficient or the alignment with intent is weak.
Variability Across Pages: For the same query, some pages demonstrate stronger coverage while others are weaker. This indicates that content quality, structure, and topical focus vary across the site or content collection.

Understanding these patterns helps prioritize content updates and identify where additional content or optimization efforts will have the greatest impact.

Practical Implications of Similarity and Coverage Metrics

Identification of Weak Subsections: Subsections with low similarity scores or flagged coverage gaps are actionable signals for content improvement. These sections can be rewritten, expanded, or replaced to better align with user intent.
Prioritization by Page-Level Coverage: Queries with low average page coverage indicate pages that require comprehensive optimization. Pages with high coverage may need only minor refinements.
Balanced Content Strategy: Analysis across multiple queries allows for strategic planning, ensuring content not only addresses individual topics but also collectively covers the breadth of target queries.

Visualization Analysis

The visualizations provide an intuitive understanding of alignment, coverage, and score distribution:

Top-k Subsection Similarity Bar Plots

Show the highest-ranking subsections for each query across pages.
Bars indicate similarity scores, and distinct hues represent individual subsections.
Enables quick identification of the most relevant content blocks and comparison across multiple pages.

Coverage Gaps Across Pages

Displays average similarity scores and gap flags for each query across multiple pages.
Highlights underperforming queries with low average scores or high gap frequency.
Visual separation of queries allows assessment of which topics require priority optimization.

Similarity Score Distribution Histograms

Present the overall distribution of top-k similarity scores per query.
Peaks indicate clusters of well-aligned content, while a long tail shows weakly aligned subsections.
Facilitates understanding of consistency in content relevance and helps detect cases where a few strong subsections mask overall coverage gaps.

Summary Insights

High-scoring subsections are already well-aligned and require minimal adjustments.
Subsections in the moderate to low similarity bins represent clear opportunities for improvement.
Queries with low page-level averages or frequent coverage gaps signal topics that need strategic content development.
Visualization tools complement the numeric analysis by offering intuitive, comparative insights across queries and pages, supporting prioritization and optimization decisions.

Q&A on Result Interpretation and Actionable Insights

How should similarity scores be interpreted?

Similarity scores quantify the alignment between page content and query intent. Scores range from 0 to 1:

High scores (≥ 0.65): Strongly aligned content; requires minimal adjustment.
Moderate scores (0.50 – 0.64): Partially relevant; subsections may benefit from additional context or examples.
Low scores (0.35 – 0.49): Weak alignment; content gaps are likely, suggesting targeted rewriting or additions.
Very low scores (< 0.35): Minimal relevance; these subsections may need replacement or significant revision.

This scoring framework helps identify which sections effectively address search intent and which need attention.

What insights can be drawn from page-level coverage metrics?

Page-level coverage metrics provide an aggregated view of how well each page addresses different queries:

High coverage (average ≥ 0.60): The page effectively satisfies the query across its content.
Moderate coverage (0.50 – 0.59): Partial coverage; some subsections are relevant, others need improvement.
Low coverage (0.40 – 0.49): Inconsistent coverage; targeted improvements are required.
Very low coverage (< 0.40): The page does not adequately cover the query; substantial content additions or restructuring is recommended.

These metrics highlight which topics require strategic optimization and help prioritize content updates.

How can coverage gaps be addressed?

Coverage gaps indicate where content fails to meet the desired similarity threshold. To address them:

Enhance existing subsections by adding context, examples, or clarifying details.
Add new subsections to cover missing aspects of the query intent.
Reorganize content to improve clarity and alignment with user intent.

Addressing coverage gaps ensures comprehensive content coverage, improving relevance and potential SEO performance.

How can subsections with moderate scores be leveraged?

Subsections with moderate alignment are partially relevant but not fully optimized. Actions include:

Adding supporting details or examples to improve alignment.
Ensuring headings and content focus reflect query intent clearly.
Linking to other relevant subsections to enhance context and coverage.

This approach improves content quality without replacing entire sections, maximizing the value of existing content.

How can visualization insights be used for decision-making?

Visualizations complement numeric analysis:

Top-k similarity bar plots highlight which subsections are most relevant, allowing prioritization of high-value content blocks.
Coverage gap plots reveal queries with underperforming pages, guiding where strategic interventions are needed.
Similarity score distributions show the consistency of content relevance across pages, helping identify whether strong sections are isolated or representative of overall coverage.

These visual tools enable quick identification of priorities and support data-driven content optimization.

How can multi-query results inform content strategy?

Analyzing multiple queries together reveals patterns across content:

Queries with consistently low scores indicate topics that need dedicated content creation.
Queries with mixed scores suggest selective optimization, improving weaker subsections while retaining strong content.
Comparing results across pages highlights content consistency and identifies which pages are most effective for specific queries.

This informs a balanced, query-driven content strategy, ensuring coverage across all target topics.

What are the key benefits of leveraging these results?

Targeted content improvements: Focus resources on underperforming sections or pages.
Enhanced query coverage: Reduce gaps and improve overall alignment with user intent.
Prioritized action planning: Quickly identify high-impact subsections for optimization.
Data-driven decision-making: Visualizations and metrics enable informed content strategy adjustments.
SEO performance potential: Better content relevance can improve rankings, engagement, and visibility across multiple queries and pages.

Final Thoughts

The analysis demonstrates a systematic approach to assessing content alignment with multiple queries across diverse web pages, powered by Qwen embeddings. By leveraging Qwen’s contextual understanding, the workflow captures semantic relationships between queries and content at a fine-grained level.

Key takeaways include:

Granular insight at subsection level: Each subsection is evaluated for relevance using Qwen embeddings, enabling targeted content enhancements where alignment with query intent is weak.
Page-level coverage assessment: Aggregate metrics reveal overall content effectiveness for each query, highlighting both strengths and opportunities for improvement.
Actionable visualizations: Top-k similarity plots, coverage gap charts, and similarity distributions provide clear, interpretable guidance for prioritizing content optimization.
Query-driven content strategy: Multi-query evaluation ensures that pages are optimized comprehensively across different search intents, reducing gaps and improving content relevance.

By integrating Qwen’s powerful embeddings into the analysis, the project delivers a practical, data-driven framework for optimizing content alignment, enhancing semantic relevance, and informing strategic content decisions. Every content block is assessed, and actionable insights are readily available to improve search visibility and query satisfaction.