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Contextual Thesaurus Expansion — Adds synonyms and related terms dynamically based on context for broader retrieval is a client-focused SEO enhancement tool that improves search result matching by resolving vocabulary mismatches between user queries and content. In many cases, relevant content exists on a website but remains unretrieved due to differing terminology between user input and on-page language.
This project addresses that gap using a contextual language model to extract high-quality synonym and related terms for each query, based on their usage context. These contextually expanded terms are used to evaluate content blocks extracted from client websites, enabling more flexible and semantically accurate matching.
The system processes long-form web content into smaller semantic blocks, identifies blocks that contain any of the original or expanded query terms, and scores their relevance using similarity-based techniques. It then provides block-level match results with interpretable scores and coverage metrics.
The outcome is a practical, scalable retrieval enhancement framework that improves search coverage, content discoverability, and user satisfaction—without altering the original website content. This directly supports SEO objectives such as increasing visibility and driving qualified traffic.
Project Purpose
This project aims to enhance the retrieval and discovery of relevant website content by addressing vocabulary mismatch—an issue where user search queries use terminology that differs from the language used in the actual web content.
In standard SEO workflows, exact-match strategies often fail to capture relevant content simply because the terms used in queries differ semantically from those on the page. This leads to missed opportunities in search visibility and user engagement.
To solve this, the project leverages contextual synonym expansion using masked language modeling. Each user query is expanded into a high-quality alternative term based on its surrounding linguistic context. This contextually derived term are then matched against segmented content blocks extracted from client web pages.
Importantly, only the expanded term—not the original queries—are used to identify semantically relevant blocks through a similarity scoring process. This allows retrieval based on meaning rather than surface form, improving recall without compromising precision.
This approach supports SEO professionals by:
- Increasing retrieval coverage for relevant but lexically divergent content
- Detecting valuable blocks hidden under non-obvious terminology
- Enabling block-level visibility insights across different URLs
- Providing an interpretable scoring mechanism for optimization decisions
By integrating contextual understanding into the retrieval pipeline, the project offers a modern, scalable method to surface underutilized content and align search exposure with user intent.
Project’s Key Topics Explanation and Understanding
Contextual Thesaurus Expansion
Traditional thesaurus tools provide generic synonyms without considering how a term is used in specific situations. In contrast, contextual thesaurus expansion refers to the dynamic generation of synonyms and related terms that reflect the actual meaning of a word within its surrounding language context.
In this project, this concept is central to solving vocabulary mismatch problems. By expanding terms based on their usage context, rather than relying on static synonym lists, the system is able to discover alternative expressions that better align with how different users describe the same concept. This allows for more accurate and relevant matches between search queries and on-page content.
Synonyms and Related Terms
The project does not limit expansion to strict synonyms; it also includes related terms—words or phrases that are not exact matches but are semantically close enough to indicate relevance. For example, a query about “cost” might be related to content using the word “pricing.”
In SEO, this broader lexical matching ensures that search systems can connect user queries with valuable content that uses different but contextually appropriate terminology. This is especially critical in domains where vocabulary can vary significantly between users and content creators.
Dynamically Based on Context
Rather than relying on predefined synonym sets, the project generates alternatives dynamically based on the specific linguistic context in which a term appears. This ensures the generated terms are both relevant and accurate for the given usage.
The dynamic nature of this process also allows the system to adapt to any query or content set, without requiring manual curation or prior domain knowledge. It scales across different topics, industries, and client websites.
For Broader Retrieval
The ultimate goal of the project is broader retrieval—expanding the reach of search systems so they can identify and surface more relevant content, even when there is no direct keyword match.
This is especially beneficial in SEO, where missing relevant content due to vocabulary differences leads to lost visibility. By enabling broader retrieval, the project helps surface hidden or underperforming content and ensures it gets matched to appropriate user queries.
Q&A: Understanding the Project’s Value and Importance
Why is this project important for SEO performance?
Modern SEO is no longer limited to exact keyword matching. Users often phrase their queries differently from how content is written on a website. For example, a user may search for “affordable smartphones” while the page only mentions “budget mobile devices.” Traditional retrieval systems may fail to match these, resulting in missed opportunities for visibility and traffic.
This project directly addresses that issue by enabling retrieval systems to bridge the gap between user intent and on-page language. It ensures that relevant content—regardless of the exact words used—can still be discovered and surfaced. This significantly enhances organic reach, improves alignment with user needs, and increases the likelihood of engaging qualified traffic. For clients, this translates into more effective content utilization and improved SEO ROI.
What are the key features of this project that set it apart from basic synonym matching?
Unlike basic synonym libraries or keyword expansion tools, this project introduces context-aware language intelligence. It does not rely on static or generic word lists; instead, it identifies alternative terms that make sense only within the specific context of the query or topic.
Key features include:
- Contextual synonym generation tailored to actual language usage.
- Dynamic term expansion that adapts to new topics and queries without reconfiguration.
- Broader retrieval support that helps capture relevant content written using different terminology.
- Block-level precision, ensuring expansion efforts are targeted and semantically accurate.
These features make the project both flexible and scalable, offering long-term value across multiple content domains and user query patterns.
How can clients benefit from this project in a real-world SEO strategy?
Clients benefit from this project by uncovering and leveraging content that might otherwise remain invisible in search results. Many websites already contain valuable information, but it often underperforms simply because it does not use the same vocabulary that users do in their searches.
By expanding search and indexing capabilities to account for contextual synonyms and related terms, this project ensures:
- Greater content visibility for diverse search intents.
- More efficient content reuse, as existing pages become relevant for a wider range of queries.
- Better alignment between user search behavior and content exposure, which drives more qualified traffic.
- Insight into vocabulary gaps that can inform future content planning and optimization efforts.
Ultimately, it helps SEO teams extract more value from their content investments without requiring changes to the existing site copy.
Why is vocabulary mismatch a major problem in search and SEO?
Vocabulary mismatch occurs when users phrase their queries differently from how businesses describe the same concepts on their websites. It’s a common issue that impacts both informational and commercial queries. For instance, a user searching for “pain relief” might not find a page optimized around the term “analgesic therapy,” even though the page is relevant.
In traditional SEO practices, this mismatch leads to lost visibility, lower engagement, and poor coverage across diverse search intents. This project solves the problem by interpreting both user queries and content with deeper linguistic understanding. Instead of relying on exact wording, it allows matches to be made based on meaning, capturing semantically relevant relationships.
Solving this problem leads to increased discoverability, better search rankings, and improved content performance metrics.
Can this approach scale across different websites and industries?
Yes. One of the core advantages of this project is its domain-agnostic nature. Since it operates on contextual understanding rather than domain-specific rules, it can be applied across various industries, topics, and types of content—whether the website is e-commerce, healthcare, finance, education, or media.
This makes it a highly scalable solution for SEO agencies and enterprise teams managing large portfolios of clients or content properties. The system adapts to new language patterns, topic areas, and user behavior trends automatically, ensuring continued relevance and performance over time.
Libraries Used
requests
The requests library is a widely used HTTP client in Python that allows sending HTTP requests and handling responses with ease. It is known for its simplicity and human-friendly syntax, making it ideal for tasks such as downloading webpages, submitting forms, or accessing web APIs.
In this project, requests is used to retrieve the raw HTML content from the provided client URLs. This enables the system to work directly with live website content, forming the basis for downstream processing. The retrieved HTML is later parsed and cleaned to extract meaningful content blocks for analysis.
BeautifulSoup (from bs4)
BeautifulSoup is a Python library for parsing HTML and XML documents. It creates a parse tree for a page’s content that can be easily navigated and searched. It is especially useful for extracting and restructuring information from messy or inconsistent HTML.
In this project, BeautifulSoup is used to parse the raw HTML obtained from client URLs. It enables the identification and removal of unnecessary elements such as scripts, styles, boilerplate blocks, and hidden sections (e.g., elements with display: none). This is a crucial preprocessing step that ensures only the visible and meaningful content is retained for further semantic analysis.
re
The re module in Python provides support for regular expressions—powerful tools for pattern matching and text manipulation. It allows for searching, splitting, or replacing patterns within strings, which is highly efficient for rule-based text cleaning.
In this project, re is used during the content cleaning and normalization phases. It helps in removing unwanted characters, HTML tags, excess whitespace, and other artifacts that may interfere with tokenization or semantic interpretation. Regular expressions also support certain filtering rules when validating candidate terms for expansion.
html and unicodedata
The html module provides utilities for handling HTML-specific character encoding, such as decoding HTML entities into readable text. Meanwhile, unicodedata helps standardize Unicode text by removing or replacing accented characters and special Unicode symbols.
Both libraries are employed in this project to normalize extracted webpage text. html ensures that HTML entities are properly decoded, while unicodedata helps convert accented or non-standard characters into ASCII equivalents. This improves the consistency and comparability of the content blocks and query terms used throughout the expansion and matching pipeline.
numpy
NumPy is a fundamental package for scientific computing in Python. It provides high-performance array objects, mathematical operations, and support for linear algebra, making it a core dependency in data-intensive applications.
In this project, NumPy is used for numerical operations involved in similarity scoring, candidate term ranking, and vector manipulations. Its efficiency with array operations helps support large-scale processing of text embeddings and similarity matrices without compromising speed or accuracy.y.
spaCy
spaCy is a robust natural language processing (NLP) library that offers high-performance tools for tasks like part-of-speech tagging, named entity recognition, and syntactic parsing. It is widely used for its speed and production-readiness in large-scale text processing workflows.
In this project, spaCy is specifically used to perform part-of-speech (POS) tagging to identify and retain only noun and adjective tokens from the content blocks. These parts of speech are often the most informative for identifying meaningful entities and concepts. The filtering ensures that only high-value tokens are considered for contextual synonym expansion, improving the relevance of generated terms. Lemmatization and dependency parsing were not part of this project’s scope.
scikit-learn (sklearn)
scikit-learn is a comprehensive machine learning library in Python that includes tools for classification, regression, clustering, dimensionality reduction, and text feature extraction.
In this project, TfidfVectorizer from sklearn.feature_extraction.text is used to extract meaningful n-grams from content blocks. These candidate n-grams are potential terms for contextual expansion. By leveraging TF-IDF, the system identifies terms that are both locally significant within a block and distinct within the broader document, helping prioritize terms likely to influence retrieval effectiveness. sklearn’s similarity metrics were not used in this project.
transformers
The transformers library from Hugging Face provides easy access to state-of-the-art pretrained transformer models for natural language understanding tasks, including masked language modeling, question answering, and text classification.
This project uses transformers to load a masked language model capable of generating contextual synonym predictions. The model is queried using masked inputs constructed from content or query terms, and predictions are ranked and filtered to identify contextually appropriate expansions. This forms the core of the project’s synonym expansion logic.
nltk
The nltk (Natural Language Toolkit) library is one of the foundational Python libraries for working with human language data. It includes tokenizers, stopword lists, and basic preprocessing utilities.
In this project, nltk is used for stopword removal during token filtering. Removing common functional words ensures that expansion candidates focus on semantically meaningful terms rather than noise. This preprocessing step improves both the quality of extracted candidates and the reliability of the expansion process.
sentence-transformers
The sentence-transformers library extends Hugging Face models with tools for computing sentence or phrase embeddings and comparing semantic similarity using those embeddings. It is optimized for fast, scalable, and meaningful text similarity tasks.
This project uses sentence-transformers to generate embeddings for content blocks and expanded query terms. The cosine similarity between these embeddings—computed using util.cos_sim—is used to score the semantic relevance of each content block. This enables retrieval based on meaning rather than exact word matching, aligning with the project’s goal of broader semantic coverage.
pandas
pandas is a high-level data manipulation and analysis library that provides dataframes and tools for managing structured data. It is widely used for organizing and processing tabular results.
In this project, pandas is used to collect, aggregate, and format the results of the content-query matching process. It helps manage lists of expanded terms, block-level match scores, and overall retrieval summaries. This organized output supports later visualization phase.
matplotlib and seaborn
Both matplotlib and seaborn are Python libraries for data visualization. matplotlib provides a low-level interface for plotting, while seaborn builds on it with higher-level functions and improved aesthetics.
These libraries are used to visualize the retrieval effectiveness across different client URLs and queries. Specifically, grouped bar charts are used to show how many content blocks matched each query’s expanded terms, helping SEO professionals understand retrieval depth and term coverage in an interpretable way.
Function: extract_blocks_from_url()
Overview
This function retrieves and processes a webpage to extract its title and a list of clean, readable content blocks. The goal is to isolate the meaningful visible text from the webpage—ignoring scripts, navigation, boilerplate, and hidden elements—so that only relevant, deduplicated, and semantically useful text blocks remain.
This forms the foundation of the project’s block-level semantic analysis by ensuring that only high-quality, human-readable content is retained for contextual term expansion and matching.
Highlighted Key Lines
res = requests.get(url, headers=headers, timeout=timeout)
Fetches the HTML content of the given URL using a user-agent header to simulate a real browser. This enables access to web pages that may block non-browser clients.
Removes non-content HTML elements that are typically irrelevant for semantic analysis, such as scripts, forms, and site navigation components.
Detects and removes HTML elements that are hidden from view using inline CSS styles. Ensures that only visible content is retained for analysis.
Filters out blocks with predominantly non-ASCII characters, which often indicate junk, foreign-language content, or corrupted HTML. Helps maintain the quality of extracted blocks.
Avoids processing duplicate blocks by hashing the normalized text. Ensures each block is unique, reducing redundancy in downstream analysis.
Function: filter_blocks()
Overview
This function refines the initial set of raw content blocks by applying text normalization and removing common boilerplate, junk phrases, and irrelevant patterns such as URLs or generic footer content. Its role is to ensure that only clean, semantically meaningful blocks proceed to the next stages of contextual analysis.
This cleaning step significantly improves the quality of content used for term extraction by discarding promotional clutter, copyright notices, and formatting artifacts that do not contribute to semantic value.
Highlighted Key Lines
Defines a regex to detect and remove typical low-value text phrases that appear in footers, legal disclaimers, or promotional links. This helps filter out non-informative content.
Decodes HTML entities (like or &) and normalizes Unicode characters to standard forms, improving consistency and readability of the text data.
text = url_pattern.sub(“”, text)
Removes embedded URLs from the content. This avoids treating links or external references as part of the core text to be semantically analyzed.
Standardizes punctuation and whitespace characters (e.g., curly quotes, em-dashes) by replacing them with simpler equivalents. Ensures compatibility with downstream tokenization and modeling.
Applies a minimum word count threshold to eliminate very short or trivial blocks that are unlikely to be semantically useful or meaningful for expansion.
Function: extract_contextual_terms()
Overview
This function identifies high-value candidate terms from each block query that are suitable for contextual expansion. It combines linguistic cues (like part-of-speech and noun phrase structure) with statistical relevance (via TF-IDF scores) to extract contextually meaningful unigrams and strictly adjacent bigrams.
The goal is to generate a refined list of important terms for each block query-terms that are likely to carry semantic weight and represent the core meaning of the block. These terms form the input to the synonym expansion module in later stages.
Highlighted Key Lines
Fits a TF-IDF model on all content blocks to score unigrams and bigrams by their uniqueness and relevance. Helps prioritize statistically meaningful terms within each block.
Extracts noun phrases and ensures bigrams are only accepted if the two words appear side-by-side in the actual block. Prevents inclusion of disjoint phrases or noisy combinations.
Filters tokens by part-of-speech and stopword status. Ensures only content-bearing words (like nouns and adjectives) are retained as individual unigrams.
Ranks the combined set of linguistic and TF-IDF terms by their statistical weight within the block. Ensures top candidates reflect both semantic and numeric importance.
Filters overlapping terms to avoid redundancy (e.g., prevents selecting both “search” and “search engine”). Finalizes a clean set of distinct, high-quality terms per block.
Function: load_mlm_model()
Overview
This function loads a pretrained masked language model (MLM)—such as BERT—and its associated tokenizer. The MLM is critical to generating contextual synonyms and related terms for extracted keywords. It operates by predicting contextually appropriate words when a target term is replaced with a [MASK] token in its original sentence.
The function ensures the model is properly configured for inference, including automatic detection of GPU availability.
Highlighted Key Lines
device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)
Automatically detects whether a GPU (CUDA) is available. This ensures the model runs efficiently, defaulting to CPU only if necessary.
Downloads and loads the specified masked language model and its tokenizer using Hugging Face Transformers. The default is “bert-base-uncased”, a widely used BERT variant trained for general English language modeling.
Moves the model to the selected device (CPU/GPU) and sets it to evaluation mode, which disables dropout and other training-specific behavior for consistent predictions.
Model Used: BERT-Large-Uncased
About the Model
bert-large-uncased is a pre-trained deep language representation model developed by Google as part of the BERT (Bidirectional Encoder Representations from Transformers) family. The “large” variant significantly scales up the model’s capacity compared to the base version, making it more effective for capturing semantic and syntactic nuances in text.
- Model Size: 24 transformer layers, 1024 hidden units, 16 attention heads
- Pretraining Tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP)
- Case Handling: “Uncased” means the model treats uppercase and lowercase letters equivalently, which helps generalize across noisy or informal text
Architecture and How It Works
BERT-Large uses a deep transformer encoder stack with self-attention mechanisms to learn bidirectional contextual relationships across a sentence or passage. Key components:
- Bidirectional Contextual Understanding: Unlike traditional left-to-right models, BERT reads entire sentences in both directions, allowing it to learn richer context.
- Masked Language Modeling (MLM): During training, random tokens are masked, and the model learns to predict them using surrounding context.
- WordPiece Tokenization: Input text is split into subword units, allowing the model to handle rare words and word variations efficiently.
For example, in the phrase:
“Optimize your backlink profile”, BERT may tokenize it as [“optimize”, “your”, “back”, “##link”, “profile”], and understand that “backlink” refers to a single SEO concept even when broken down.
Why This Model Was Used in This Project
In this project, bert-large-uncased was used specifically for masked language modeling to generate high-quality contextual synonyms for important terms extracted from SEO-related content blocks.
Reasons for choosing bert-large-uncased:
- High Contextual Sensitivity: Its large-scale architecture improves understanding of domain-specific usage, such as interpreting “authority”, “indexing”, or “schema” differently based on context.
- Pretrained Knowledge: The model captures a broad understanding of English, allowing it to generate relevant synonym candidates even for long-tail or niche SEO terminology.
- Flexible Masking: Through the MLM mechanism, we can plug key terms into a sentence, mask them, and let BERT suggest replacements that make sense in that exact context.
Importance in SEO Query and Content Understanding
SEO content and queries are often ambiguous or varied in phrasing. For example:
- Query: “tools to analyze backlinks”
- Synonyms from BERT: “check”, “audit”, “evaluate”, “track”
Such expansions are context-aware, not just dictionary-based. This allows the system to connect diverse user intents to matching content blocks that may not use the exact original query terms.
BERT enables:
- Discovery of semantically aligned alternatives
- Richer query rewriting and matching
- Improved coverage in long-form content retrieval
Comparison to Simpler Models
Unlike traditional keyword matching or TF-IDF-based similarity methods, BERT captures deeper language semantics. This is especially valuable in SEO contexts where:
- A user’s query may not use the same vocabulary as the content
- Synonyms may differ depending on content topic or user intent
- Ranking relevance depends on subtle linguistic cues
Function: get_contextual_candidates()
Overview
This function generates contextual synonym candidates for a given term by using a masked language model (MLM). It replaces the target term in its original sentence with a [MASK] token and lets the MLM predict the most likely replacements based on the sentence’s context.
The output is a ranked list of alternative terms that are contextually meaningful, grammatically valid, and lexically appropriate for substitution—essential for accurate query expansion in SEO applications.
Highlighted Key Lines
This replaces the target term with the [MASK] token and tokenizes the sentence for input into the language model. The context is preserved while allowing the model to infer the best-fitting word for the missing spot.
Runs the masked language model in inference mode (without gradient tracking), producing logits (raw prediction scores) for all tokens at every position in the input sequence.
Locates the [MASK] token’s position and extracts the top predictions (by raw score) from that location in the model’s output. These represent the most probable replacements.
predicted_tokens = tokenizer.convert_ids_to_tokens(top_ids)
Converts the top token IDs back into readable words for further filtering and ranking.
Applies strict filters to remove noisy or irrelevant candidates (e.g., stopwords, short tokens, punctuation), and avoids returning the original term itself.
Ensures a fallback mechanism is in place—if all top candidates are filtered out, a looser set is returned to maintain functionality.
Function: expand_terms_from_blocks()
Overview
This function performs contextual expansion of terms within cleaned content blocks by leveraging a masked language model (MLM). It takes each candidate term extracted from the block and generates a list of context-sensitive synonyms or related terms. These expanded terms are then used for broader query matching and retrieval—especially important in SEO contexts where semantic relevance matters.
How It Works
Each term is matched precisely within its original block using word-boundary regex to ensure only full, valid term replacements are considered.
synonyms = get_contextual_candidates(…)
Calls the synonym generation function (already explained) to predict terms that contextually fit the masked position of the candidate term in the block.
Only non-empty synonym lists are stored, avoiding unnecessary clutter or null entries.
Return
A list of dictionaries—each dictionary represents one block and maps extracted terms to their top contextually relevant synonym candidates. This output is foundational for expanding user queries or content indexing in downstream SEO applications.
Function: generate_expanded_texts()
Overview
This function builds contextually expanded query strings by embedding synonym alternatives inline—immediately next to their original terms—using a grouped OR format. This allows for broader keyword coverage in retrieval systems without compromising on the original query’s intent.
It plays a central role in connecting user queries to expanded variants during search or matching processes, enabling more comprehensive and semantically inclusive results.
Key Functional Behavior
for query, orig_terms, exp_dict in zip(query_list, original_terms_list, expanded_terms_list):
The function processes multiple queries in parallel, each associated with its own list of original terms and a dictionary of context-aware synonyms.
Ensures that each term is only expanded once, and skips those with no available synonym set—maintaining query clarity and avoiding unnecessary duplication.
Constructs the inline expansion format. For example, if the original term is “fast” and synonyms are [“quick”, “speedy”], the resulting expression becomes:
This helps retrieval systems match more documents without requiring an external synonym expansion step at runtime.
Performs case-insensitive, word-boundary aware replacement of the term in the original query with its expanded group, preserving clean query formatting and avoiding substring mismatches.
Function: load_embedding_model()
Overview
This function loads a pretrained sentence embedding model from the Hugging Face sentence-transformers library. These models are capable of converting text into dense numerical representations (embeddings) that capture semantic similarity between sentences or terms.
This embedding capability is critical in the project, especially for contextual similarity comparison, which enables high-precision matching between queries and page content—even when exact keywords differ.
Key Characteristics
device = ‘cuda’ if torch.cuda.is_available() else ‘cpu’
Automatically detects GPU availability. If a CUDA-compatible GPU is present, the model is moved to GPU memory for faster computation—otherwise, it defaults to CPU.
Loads the specified sentence embedding model (default: all-mpnet-base-v2), a state-of-the-art transformer fine-tuned for sentence-level semantic similarity tasks. The model is then allocated to the appropriate device.
Embedding Model: all-mpnet-base-v2
The all-mpnet-base-v2 model plays a central role in the project by converting queries and content blocks into semantic vector representations. This enables accurate similarity matching based on meaning rather than keyword overlap.
About the Model
all-mpnet-base-v2 is a sentence embedding model released by SentenceTransformers. It is based on Microsoft’s MPNet architecture and trained using a combination of masked language modeling and permutation-based modeling to learn deeper semantic relationships.
The model is pre-trained and fine-tuned specifically for tasks like semantic search, question-answering, and textual similarity — making it well-suited for real-world SEO applications.
Architecture Highlights
- Base Architecture: Built on MPNet (Masked and Permuted Pre-training), which blends advantages of both BERT (autoencoding) and XLNet (permutation-based prediction).
- Training Objective: The model is fine-tuned using a contrastive learning objective — learning to bring semantically similar sentence pairs closer in embedding space.
- Tokenization: Uses WordPiece tokenizer similar to BERT, ensuring robust handling of real-world web content.
How It Works
- The model accepts raw text inputs — either user queries or content blocks.
- Each input is passed through a transformer encoder to produce a dense vector (embedding) that captures its semantic meaning.
- These embeddings are normalized and then compared using cosine similarity to determine how closely two pieces of text match in meaning.
This allows the system to detect relationships between differently worded phrases that express similar ideas — a critical need for SEO relevance matching.
Why It’s Important for SEO Applications
In SEO, users often phrase the same intent in multiple ways. For example, “reduce image load time” and “optimize picture rendering” are semantically close, but keyword-based systems might miss the match.
all-mpnet-base-v2 solves this by understanding the contextual intent of queries and matching them to content that reflects that intent — regardless of exact phrasing. This allows:
- Better content-to-query alignment
- Detection of near-matches missed by keyword-based systems
- Smarter analysis of whether content addresses searcher needs
Why This Model Was Chosen for the Project
This model was selected because:
- It delivers state-of-the-art performance on semantic similarity benchmarks (STS, retrieval, QA).
- It is efficient and compact, enabling real-time scoring of multiple content blocks without heavy infrastructure.
- It is robust to real-world SEO language, including incomplete phrases, synonyms, and content noise.
These qualities make it ideal for powering the core similarity engine behind query expansion and content block scoring in this project.
Function: score_expanded_queries_against_blocks()
Overview
This function evaluates how well contextually expanded queries match with the provided content blocks using semantic similarity. It leverages a SentenceTransformer embedding model to compute vector-based representations and cosine similarity, ultimately returning the most relevant blocks per query.
The function is critical for matching expanded user intent to meaningful document sections, thus enabling smarter content discovery and retrieval in SEO workflows.
Key Functional Behavior
Encodes both the content blocks and the expanded queries into normalized embedding vectors. Normalization ensures cosine similarity scores are properly scaled in the range [-1, 1], making similarity comparisons consistent across all queries and blocks.
sim_scores = util.cos_sim(query_embeddings[idx], block_embeddings)[0]
For each expanded query, calculates cosine similarity scores with all content blocks. These scores represent the semantic alignment between the query’s intent and the block’s content.
Pairs each block with its similarity score, retaining both the block text and a rounded semantic score. This structure allows traceable relevance interpretation per block.
top_blocks = sorted(block_scores, key=lambda x: x[“score”], reverse=True)
Sorts the blocks by descending similarity score to prioritize the most relevant matches.
Note: While top_k_blocks is passed as a parameter, it’s not used to truncate the result. You may want to apply slicing like top_blocks[:top_k_blocks] to reflect its purpose.
Each result record includes the original query, its expanded version, and the full list of ranked content blocks—allowing downstream steps to filter, display, or analyze matches based on thresholding or top-k logic.
Return Value
Returns a list of dictionaries—one per query—where each dictionary contains:
- The original query
- The expanded query
- A ranked list of matched blocks with similarity scores
This format supports easy retrieval, visualization, and relevance-based filtering, aligning tightly with the broader SEO goal of delivering contextually rich, query-relevant content exposure.
Function: display_expanded_query_results()
This function formats and presents the expanded query matching results in a clear, human-readable way. For each original query, it displays the expanded form and the top-matching content blocks along with their relevance scores. This output is particularly useful for demonstrating how contextual query expansion improves block-level content retrieval and relevance visibility for clients and SEO professionals. It simplifies interpretation by aligning queries with the most semantically relevant portions of content from a given URL.
Result Analysis and Explanation
This section explains how to interpret the output when the system analyzes a single query against a single URL. It provides practical insights for SEO professionals to evaluate content relevance and plan effective actions.
What the Output Contains
For each query-URL pair, the system returns:
- The original query.
- The contextually expanded query (combining meaningful synonyms and related terms).
- A ranked list of content blocks from the URL.
- A similarity score (ranging from 0 to 1) showing how relevant each block is to the expanded query.
Each block also includes its position (block ID) and full block text so that relevance can be assessed directly within the page structure.
How to Interpret the Similarity Scores
The similarity score reflects semantic alignment between the expanded query and the block — not exact keyword matching. Here’s how to interpret different score levels:
- Scores between 0.85 and 1.00 This indicates a very high contextual match. The content block strongly supports the query’s intent using closely related language, even if the exact words aren’t used. No changes are necessary here — the block is already effective.
- Scores between 0.70 and 0.85 These blocks are contextually relevant but may be missing key vocabulary or phrased in a way that reduces their clarity for search engines. Minor updates — such as adding missing synonyms or rephrasing slightly — can make them stronger.
- Scores between 0.50 and 0.70 These blocks have only a partial match. The connection to the query’s meaning is present but weak. Consider rewriting or expanding these blocks to include the full scope of the expanded query’s intent.
- Scores below 0.50 These blocks are weakly relevant or unrelated. They don’t support the query meaningfully. You may choose to revise the content heavily or introduce a new block that better addresses the expanded query.
Why These Scores Matter
These scores enable you to move beyond basic keyword checks. They show how well your content connects with real user intent, as interpreted through semantic expansion. This helps surface hidden gaps and optimize for broader search visibility.
How You Can Use the Output
To validate coverage: High-scoring blocks show where your content is already doing a good job. This can confirm that your SEO strategy is aligned with user expectations.
To identify weak spots: Blocks with moderate or low scores indicate under-optimized sections. These might lack relevant phrases, suffer from ambiguous wording, or simply not address the expanded intent.
To guide updates: You can directly edit or supplement these blocks using expanded synonyms or examples pulled from the expanded query. This improves both search match and on-page user clarity.
To inform new content: If the expanded query intent is not represented at all, it may indicate a need to write a new section — or even a new page — targeting that concept.
Example
If your query is “optimize image loading” and the expanded query becomes something like:
“optimize OR enhance OR improve” + “image OR picture” + “loading OR load OR rendering”
A top-scoring block might be:
“You can reduce the load time by compressing large pictures and using lazy loading techniques.”
Even though the exact phrase “optimize image loading” isn’t present, the block gets a high similarity score because it conveys the same idea using alternate but meaningful language. This shows your content is well-aligned with the user’s underlying intent.
Summary
This similarity-based analysis provides more than just a ranking — it offers a way to understand how your page communicates meaning. By focusing on expanded query relevance rather than exact matches, the system helps you:
- Ensure broader intent coverage
- Improve underperforming sections
- Guide on-page edits that truly reflect user needs
This method supports more intelligent content planning, especially in competitive SEO environments where user phrasing varies widely and simple keyword targeting is no longer enough.
Result Analysis and Explanation
This section provides an interpretative overview of how the contextual thesaurus expansion system performs in identifying relevant content based on user queries. The results generated are designed to help SEO professionals better retrieve contextually appropriate content from their websites—enhancing both discoverability and semantic alignment.
Query Expansion-Driven Matching
Each user query is expanded into a broader, contextually enriched form using high-quality synonym generation tailored to the semantic context. These expanded queries are matched against all extracted content blocks from each URL using sentence-level embeddings. The relevance of each match is measured through semantic similarity scores, which reflect how well a block of content aligns with the user’s intent after expansion.
The system consistently identifies meaningful and thematically relevant blocks that would often be missed by standard keyword-matching systems. This demonstrates the effectiveness of contextual expansion in surfacing deeper, semantically rich information across long-form SEO content.
Relevance Score Threshold Interpretation
To provide meaningful insight into the quality of content matches, the system assigns a relevance score to each block-query match. These scores are continuous and represent the degree of semantic alignment between the expanded query and the content block. For client interpretation, the scores can be grouped into qualitative bins:
- High Relevance (≥ 0.75): Strong semantic alignment. These blocks directly address the expanded query’s context and are ideal for highlighting in audits or retrieval systems.
- Moderate Relevance (0.50–0.74): Good contextual connection. These may not match the full intent perfectly but still reflect relevant concepts or partial overlap.
- Low Relevance (< 0.50): Weak alignment. These blocks may mention related terms but likely lack focused contextual value in relation to the expanded query.
These thresholds help guide clients in evaluating which content sections are most valuable for optimization, internal linking, or content restructuring.
Result Visualizations
The output of this project is accompanied by intuitive, client-friendly visualizations that make the results more actionable. These visual tools are generated dynamically based on the inputs and results, and support clear performance monitoring.
Grouped Bar Chart by URL and Query
This visualization shows grouped bars where each query’s top-matched content scores are plotted per URL. It provides a quick comparative view of which URL had stronger matches for which query—helpful for prioritizing site sections for SEO enhancement.
Per-URL Multi-Query Matching Plot
For each URL, a separate plot displays how all user queries performed on that page. Each bar shows the top relevance score found on that URL for each query. This view is useful to assess how comprehensively a single page addresses multiple user intents and whether that page is semantically broad or focused.
Per-Query Multi-URL Matching Plot
Conversely, for each query, this plot shows the relevance scores of the best-matched content across all URLs. It highlights which pages are most contextually aligned with a specific user query, making it ideal for content targeting, internal linking, and on-page strategy decisions.
Each of these visualizations is automatically adjusted for clarity—long URLs are shortened for readability, and color separation is used to distinguish entities. Together, these plots help clients grasp performance across multiple dimensions: by query, by URL, and across content relevance levels.
Practical Impact and Insights
This result generation process supports SEO professionals in several ways:
- Content Gap Detection: Helps identify where existing pages fall short in answering user-intent-rich queries.
- Optimization Prioritization: By observing score trends, clients can prioritize which sections to expand, rewrite, or reorganize.
- Strategy Alignment: The semantic nature of matching ensures that suggested changes align not just with search terms but with user intent.
The project elevates typical SEO audits into intent-aware evaluations, offering clients a data-backed lens into content effectiveness.
Client Value Summary
The results generated by this system provide a high-resolution map of how well current content matches real user intents, both in breadth and depth. The combination of contextual expansion and embedding-based matching ensures coverage beyond keyword overlap, uncovering meaningful blocks that contribute to user engagement and SEO outcomes.
These results, when viewed alongside the provided visualizations, empower clients to take targeted content actions—such as enhancing top-performing blocks, restructuring underperforming sections, and building internal links anchored on real semantic relevance.
How does this project improve SEO strategy using actual content performance data?
This project gives clients a data-driven lens into how well their existing content aligns with real user queries—even after expanding those queries contextually. Instead of guessing whether a page is relevant, clients receive evidence of content blocks that semantically match user intents. This allows SEO teams to:
- Identify content that already performs well semantically, making it suitable for highlighting, link targeting, or rich snippet optimization.
- Spot underperforming areas where blocks are either missing key intent coverage or drifting from contextual relevance, helping prioritize content updates.
- Understand content coverage across URLs and detect gaps where certain user intents are weakly addressed.
In short, the system bridges the gap between intent and content—based not just on keywords, but on deep contextual understanding.
What unique SEO value does the project deliver that keyword-based tools typically miss?
Most traditional SEO tools operate on lexical overlap—matching user queries to surface terms in content. This system uses contextual expansion and embedding-based semantic matching, which means it can:
- Capture latent relevance even when there is no direct keyword overlap.
- Recommend content blocks that are topically aligned with what users mean, not just what they type.
- Uncover secondary or related intents embedded within content that might support long-tail query targeting.
This opens a strategic advantage: content can be optimized for intent clusters and semantic breadth, improving discoverability across diverse search queries and increasing topical authority.
How can clients use the output to improve internal linking or site structure?
By analyzing the semantic relevance scores between expanded queries and content blocks, clients can:
- Identify anchor points within content that align closely with user intent, making them ideal destinations for internal links.
- Map query topics to URLs, helping structure hubs or pillar pages where multiple queries are strongly represented.
- Avoid over-linking weak blocks, ensuring that internal links guide users and search engines toward high-value, contextually relevant sections.
This approach moves internal linking from generic heuristics to evidence-based optimization, improving crawlability, engagement, and authority signals.
How can these results support content auditing and optimization decisions?
The project’s output effectively acts as a semantic audit layer for your content. Each result shows how well a content block responds to an expanded version of a query. From this, SEO and content teams can:
- Revise blocks that are scored as weak or off-topic for critical user queries.
- Repurpose or promote high-performing blocks across other pages or platforms.
- Prioritize content editing based on actual relevance gaps, not assumptions or fixed keyword lists.
By anchoring optimization decisions in intent satisfaction, clients can expect improved rankings, reduced bounce rates, and stronger content retention signals.
How does query expansion enhance retrieval and visibility beyond exact match keywords?
Contextual query expansion dynamically broadens user queries with high-quality synonyms and related terms specific to their meaning. The benefits for SEO include:
- Broader search coverage: Pages become retrievable for more variations of user intent.
- Reduced keyword dependency: Optimizing for meaning rather than surface terms increases content resilience across algorithm updates.
- Improved ranking opportunities: Pages that rank for semantically related queries have a higher chance of appearing in diverse search contexts, including featured snippets or voice search.
The results reflect how expansion improves the match between what users are looking for and what your content truly offers—even when phrased differently.
What are the key features in the result that demonstrate the system’s effectiveness?
Clients benefit from a number of advanced features embedded in the result layer:
- Per-query vs. multi-URL matching: Shows which URLs best satisfy specific user intents.
- Per-URL vs. multi-query matching: Reveals how comprehensively a page addresses various user needs.
- Score-based filtering: Allows teams to focus on content with high confidence relevance.
- Visualized relevance breakdown: Converts complex scores into actionable insight through intuitive plots and relevance thresholds.
Final thoughts
This project provides a powerful, context-aware approach to aligning user intent with existing web content, going beyond traditional keyword-based methods. By combining dynamic query expansion with semantic matching, it enables SEO teams to evaluate content performance based on relevance, not just surface term overlap.
The system’s ability to assess content blocks across multiple URLs and queries offers granular insight into how well a website satisfies diverse search intents. It highlights high-value content, uncovers missed opportunities, and provides actionable guidance for content refinement, internal linking, and broader visibility.
Ultimately, this project equips clients with a strategic advantage—enabling them to optimize for meaning, relevance, and real user needs. It bridges the gap between user language and content semantics, making SEO efforts smarter, more adaptive, and future-proof.
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.
