Complex Query Decomposition for Improved Relevance Ranking

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In modern SEO workflows, users frequently submit complex, multi-intent queries that represent layered informational goals—such as combining metrics evaluation, strategic recommendations, and optimization techniques in a single search. Traditional content evaluation and ranking methods often treat such queries as atomic units, which can limit their ability to assess relevance accurately across all embedded intents.

This project addresses that challenge by decomposing complex queries into simpler, targeted sub-questions and ranking webpage content blocks based on how well they respond to each one. By analyzing content against each sub-question individually, the system offers a more precise view of a webpage’s strength and weakness in addressing specific aspects of user interest. This helps identify optimization opportunities, highlight content coverage gaps, and support more actionable SEO decision-making.

Project Purpose

The purpose of this project is to improve relevance assessment and content optimization in SEO by enabling a more granular, intent-level analysis of complex queries. When search queries contain multiple dimensions of information needs, evaluating the query as a whole often overlooks specific gaps in content relevance. This system addresses that limitation by splitting the query into multiple sub-questions, then evaluating the semantic match between each sub-question and content blocks on a page.

This decomposition-and-ranking approach helps ensure that content is aligned with how users actually formulate their search behavior. SEO specialists can use this process to measure whether each part of a page meets its target intent, while website owners gain insight into what types of improvements might boost search performance. Compared to conventional SEO analysis workflows, this method brings clarity, specificity, and a structured framework for aligning content with search demand.

Project’s Key Topics Explanation and Understanding

This project involves two central concepts directly tied to the title: complex query decomposition and relevance ranking. Each of these plays a pivotal role in modern information retrieval, especially in SEO-driven content evaluation. Understanding these topics is crucial for recognizing how the project improves traditional SEO analysis and enables more actionable insights.

1. Complex Query Decomposition

Definition and Role in SEO

A complex query is a search query that contains multiple underlying intents, usually expressed in a single sentence. For example, the query *”What SEO metrics matter and how to improve ranking using them?”* includes both an information-seeking intent (finding the right metrics) and an action-oriented intent (applying strategies to improve ranking).

Traditional SEO tools typically analyze the query as a single unit. However, this approach may not reveal whether a page answers all parts of the query. Query decomposition solves this problem by breaking the complex query into multiple sub-questions, each representing a distinct user intent or focus area.

This decomposition helps in:

Identifying specific content needs.
Matching content blocks to narrower, clearer objectives.
Enabling targeted analysis of how well a page performs across different informational dimensions.

Importance of Decomposition in Content Optimization

Without decomposition, a page might appear moderately relevant to a complex query, even if it strongly answers one part but fails on others. By evaluating content against each sub-question individually, SEO audits become more granular and diagnostic, allowing teams to:

Recognize which parts of a page are effective.
Detect gaps where user intent is not satisfied.
Take informed content rewriting or structuring actions.

This leads to more meaningful improvements, especially for high-value queries that drive traffic and conversions.

2. Relevance Ranking

Purpose of Ranking After Decomposition

Once sub-queries are generated, it’s essential to assess the relevance of content blocks within a page for each one. This allows determination of whether specific segments (e.g., headers, paragraphs) align with user expectations. The project uses semantic embeddings to evaluate this match.

Unlike traditional keyword matching, semantic relevance ranking:

Captures conceptual similarity between sub-questions and content.
Understands rephrased expressions and synonyms.
Ranks content based on deeper meaning, not just literal overlap.

Content Blocks as Ranking Units

Rather than evaluating an entire page, this project focuses on content blocks—discrete segments such as <h1>, <h2>, <h3>, and <p> tags. Each block is cleaned, preprocessed, and embedded as a separate unit. This allows:

Pinpointing the most relevant paragraph or section for each sub-question.
Avoiding generic or templated content that might dilute overall scores.
Highlighting only the meaningful, optimized parts of a page.

Similarity-Based Scoring

For each sub-question, content blocks are scored using cosine similarity between their semantic vector representations. This produces a relevance score typically between 0 and 1, where a higher score indicates a stronger match to the query’s meaning. The system then selects the top-ranking content blocks—usually the top 3—as the most relevant responses.

3. Connection Between Decomposition and Ranking

The innovation in this project lies not in decomposition or ranking alone, but in their combination. Decomposing queries enables intent-level separation, while ranking content against each intent produces targeted insights. This layered approach simulates how modern search engines interpret user behavior and relevance:

Decomposition mimics how search engines expand and refine user intent internally.
Ranking aligns with how results are personalized and re-scored based on content granularity.

Together, they provide a structured framework to audit and optimize content at the intent level, which is critical for competing in intent-rich SEO environments like featured snippets, voice search, and conversational interfaces.

Why is query decomposition necessary for SEO audits?

Search queries often contain multiple intents within a single sentence. Evaluating content based on the full query may hide the fact that some parts of the user’s needs are not addressed. By decomposing the query, it becomes possible to analyze each part individually, providing a more complete and accurate assessment of how well a webpage serves its audience.

This allows businesses to:

Identify content strengths and weaknesses at a granular level.
Avoid overgeneralized SEO audits.
Create content that addresses all facets of user intent.

How does this system improve content evaluation compared to traditional keyword matching tools?

Traditional systems rely on literal keyword overlap, which can miss semantically relevant content that uses different phrasing. This project evaluates meaning using embedding-based similarity, which:

Understands the intent behind the query and content.
Recognizes related terminology and paraphrased answers.
Ensures high-scoring matches reflect actual relevance, not superficial matches.

This leads to a deeper understanding of why content ranks or fails to rank for high-value queries.

How can the project guide SEO improvements?

The output of the project identifies which specific content blocks on a page are most relevant to each part of a complex query. This enables content teams to:

Enhance or rewrite low-performing sections.
Add missing content that answers uncovered sub-intents.
Optimize high-performing blocks further by improving clarity or adding internal links.

Such precise targeting of improvements increases efficiency and yields faster, more focused SEO gains.

What makes this project valuable for long-term SEO strategy?

The project provides a repeatable, data-driven approach to understanding content relevance at the intent level. This supports long-term goals such as:

Better targeting for evergreen content.
Ongoing content optimization aligned with evolving search behavior.
Improved semantic coverage across different content clusters or topic hubs.

As search engine algorithms increasingly reward content that aligns with user intent, this system ensures alignment is continually assessed and improved.

Libraries Used

Transformers

The Transformers library by Hugging Face is a widely adopted NLP framework that provides access to state-of-the-art pretrained models such as BERT, T5, GPT, and Mistral. It allows developers to load, configure, and run large language models efficiently across CPU or GPU environments. The library supports both PyTorch and TensorFlow backends and includes advanced generation features such as temperature sampling, beam search, and model parallelism.

In this project, the Transformers library is used to load and interact with the Mistral-7B-Instruct model, which performs the task of decomposing complex SEO queries. Its flexibility in handling autoregressive text generation enables precise control over inference behavior, allowing decomposition to be structured, grammatically correct, and contextually relevant to the original query. This is crucial for extracting high-value sub-questions that align with SEO intent.

· AutoTokenizer and AutoModelForCausalLM: Automatically load tokenizers and causal language models required for query understanding.

· utils.logging: Silences progress and warning logs to maintain a clean, client-friendly notebook interface.

LlamaIndex

LlamaIndex (formerly GPT Index) is a high-level library designed to facilitate the interaction between large language models and structured data. It offers convenient wrappers for both language models and embedding models, streamlining the interface and configuration process. The HuggingFaceLLM and HuggingFaceEmbedding components are especially useful for integrating models from the Hugging Face ecosystem into custom NLP pipelines.

In this project, LlamaIndex is used to manage both the decomposition model and the embedding generation logic. The HuggingFaceLLM wrapper allows seamless integration of the Mistral model into the pipeline with minimal boilerplate, while the HuggingFaceEmbedding wrapper enables efficient embedding of both queries and content blocks using Sentence-Transformers. This abstraction improves code modularity and supports rapid model replacement or experimentation.

Sentence-Transformers

Sentence-Transformers is a specialized library built on top of Transformers to generate semantically meaningful vector embeddings for sentences and short texts. It uses contrastive learning and pooling strategies to ensure that similar text inputs are close in embedding space, making it ideal for use cases like semantic search, information retrieval, and clustering.

In this project, Sentence-Transformers is responsible for generating dense embeddings for both the decomposed sub-queries and the webpage content blocks. The model all-MiniLM-L6-v2 is selected due to its strong performance-to-speed ratio. These embeddings are essential for ranking the content blocks based on semantic similarity, which directly supports the project’s goal of relevance-based ranking.

BeautifulSoup

BeautifulSoup is a Python library used for parsing HTML and XML content. It provides an intuitive API for traversing the structure of a web document and extracting specific content while removing unwanted tags, scripts, or styles. It supports multiple parsers and can handle malformed markup gracefully.

In this project, BeautifulSoup is employed to extract clean textual content from the HTML of each URL. The focus is on retrieving key content-bearing tags like <h1>, <h2>, <h3>, and <p>, while discarding boilerplate or layout-related elements. This ensures that the text passed to the relevance ranking module is both meaningful and SEO-relevant.

Requests

Requests is a widely used HTTP library in Python that simplifies the process of sending HTTP/HTTPS requests. It supports custom headers, timeouts, response handling, and robust error reporting, making it an ideal choice for building web crawlers or accessing online resources.

In this project, Requests is used to download the raw HTML content from each provided URL. It ensures reliable page fetching by including appropriate headers and error handling logic. This is the foundational step that enables the subsequent parsing and decomposition pipeline.

re and unicodedata

The re module provides support for regular expressions in Python, enabling powerful pattern matching and text substitution. The unicodedata module is used for normalization and cleanup of Unicode strings to maintain consistent formatting, especially in multilingual or symbol-rich texts.

In this project, these libraries are applied during the preprocessing stage to clean and normalize extracted content. This includes removing unwanted punctuation, redundant whitespace, tracking URLs, and templated phrases. These operations improve the quality of the text used for embedding and ranking, contributing directly to more accurate results.

torch

Used to manage device allocation (e.g., GPU or CPU) and optimize model inference speed and memory usage.

logging

Implements robust logging to track issues such as request failures or HTML parsing problems, making the pipeline reliable and production-ready.

csv

Used to export the final processed results into a structured CSV file for external review and SEO reporting.

numpy

Powers the mathematical operations required to compute cosine similarity between embeddings. This is a core step in determining content relevance.

Function: extract_content_blocks

Overview

This function is responsible for extracting clean, structured, and relevant content blocks from any given webpage URL. It plays a foundational role in the pipeline by retrieving the raw HTML, removing non-informative elements (like JavaScript, navigation, or hidden content), and isolating only those textual sections that are likely to hold SEO value — such as <h1>, <h2>, <h3>, and <p> tags. Each valid block is tagged with an identifier and the original tag name for later reference.

This ensures that only meaningful, visible, and unique content segments are passed downstream for further analysis, embedding, and ranking.

Important Lines Explained

This sets up a browser-like request header to prevent bot blocking and ensure successful page fetches. A 10-second timeout is enforced to keep processing responsive and reliable.

This line parses the HTML content using BeautifulSoup, turning the raw HTML into a traversable object model for further cleaning and tag selection.

This segment removes boilerplate and non-informative tags that don’t contribute to SEO analysis (e.g., scripts, headers, footers, forms, etc.). These sections are not useful for content ranking and would add noise to the analysis.

Here, inline styles that hide text (like display:none) are identified and excluded, ensuring only visible text is kept.

Only selected semantic tags are extracted. Each block is checked for minimum text length to avoid noise from very short sections (like one-word headers or placeholders).

This line ensures vocabulary diversity, helping to filter out templated or boilerplate blocks that don’t add meaningful information (e.g., repetitive headers across pages).

Each content block is recorded along with a unique ID and its original HTML tag type. This structure allows later components (ranking, display, export) to trace the block’s origin and purpose.

Function: preprocess_blocks

Overview

The preprocess_blocks function is responsible for cleaning and filtering the raw content blocks extracted from a webpage. This step ensures that only high-quality, meaningful text segments proceed to the embedding and ranking stages of the pipeline. The function removes superficial formatting, repetitive boilerplate phrases, promotional fragments, and any overly short or non-diverse content.

This preprocessing significantly improves the relevance and accuracy of downstream semantic analysis by focusing computation on well-formed, informative text. It accepts as input a list of blocks in the form (block_id, tag_type, text) and outputs a cleaned and filtered version of the same.

Important Lines Explained

Function: load_model

Overview

The load_model function is responsible for loading the core language model used in this project — a causal language model from the Hugging Face Hub — for performing complex query decomposition. This function wraps the model using LlamaIndex’s HuggingFaceLLM interface, enabling standardized generation behavior across the pipeline. The function supports token-based access for gated or private models and ensures compatibility with GPU acceleration where available.

This model serves as the backbone for generating semantically accurate sub-queries that enhance the ability to rank content blocks with greater precision, aligning directly with the project’s goal of relevance improvement through query decomposition.

Important Lines Explained

Model Overview: Mistral‑7B‑Instruct

Overview of the Model

Mistral‑7B‑Instruct is a 7 billion‑parameter instruction‑tuned language model released under the Apache 2.0 license. It was developed by Mistral AI in late 2023 and made available through platforms like Hugging Face and Cloudflare’s API. As an open-weight model, it enables sophisticated language understanding and generation comparable with larger models—sometimes matching or outperforming 13 billion‑parameter models in benchmarks like reasoning, reading comprehension, math, and coding.

Architecture

Mistral‑7B‑Instruct builds upon a transformer decoder architecture that incorporates two key enhancements:

Grouped-Query Attention (GQA): Splits queries across multiple groups to reduce the number of attention operations, achieving faster inference without degrading quality.
Sliding Window Attention (SWA): Allows efficient handling of long contexts—up to 32 K tokens—by applying attention over local windows and leveraging stacked layers for global context comprehension.

These innovations allow Mistral‑7B‑Instruct to deliver high-quality outputs while maintaining low latency and deep context support.

Why This Model Was Chosen

Mistral‑7B‑Instruct offers an optimal balance between performance, cost, and speed, making it ideal for this project:

Performance Efficiency: Outperforms larger models (e.g., Llama 2 13B) on various language and reasoning benchmarks, offering superior output quality per parameter.
Inference Speed: GQA and SWA provide faster throughput and longer context handling at lower hardware cost.
Open-Source & Instruction‑Tuned: Fully open licensed with strong instruction-following capability, ensuring clarity and flexibility, especially in generating SEO-relevant sub-questions.

This makes it especially suitable for real-world SEO use cases, where speed, scalability, and quality coexist.

How Mistral‑7B‑Instruct Supports SEO Tasks

1. Decomposing Complex Queries The instruction-optimized model excels at generating clear, structured sub-questions from compound SEO queries. This helps uncover multiple intents within a single user search phrase, aligning directly with the goal of improving relevance at the intent level.

2. Context Retention Over Long Queries SEO queries tend to be long and layered. The model’s 32 K token context window ensures that no part of a query is truncated or misinterpreted, allowing for more accurate sub-question generation.

3. High Precision Generation Strong performance on reasoning and comprehension benchmarks ensures that the generated sub-questions remain grammatically correct, contextually relevant, and semantically aligned with SEO terminology.

4. Efficiency for Batch Processing The model’s fast inference speeds and low compute requirements enable each URL-query batch to be processed efficiently—critical for scaling across large datasets of URLs and queries.

Key Advantages for This Project

Produces fine-grained, SEO-aligned sub-questions that facilitate targeted analysis.
Supports multi-turn context and long queries without losing comprehension quality.
Offers a fast, low-latency runtime suitable for batch processing of multiple URLs.
Enables a fully open-source pipeline, removing reliance on restricted or expensive API access.

Function: decompose_complex_query

Overview

The decompose_complex_query function is designed to split complex SEO-related queries into simpler and more actionable sub-questions. This step directly supports the project’s primary objective of enhancing content relevance through fine-grained query decomposition, allowing the system to evaluate web page content blocks against more focused semantic intents.

This function uses a causal language model (wrapped via HuggingFaceLLM) to generate structured sub-questions in natural language. The instruction prompt is carefully crafted to ensure the model understands the goal: generating multiple sub-questions that reflect the multi-intent nature of the input query. The outputs of this function serve as direct inputs to the content block relevance ranking phase.

Important Lines Explained

Function: load_embedding_model

Overview

The load_embedding_model function loads a sentence-transformer-compatible embedding model using the HuggingFaceEmbedding wrapper provided by the LlamaIndex framework. This function abstracts the initialization of the embedding backbone and ensures that it can be reused consistently throughout the content relevance ranking pipeline.

The embedding model plays a critical role in transforming both decomposed queries and content blocks into dense vector representations. These embeddings serve as the foundation for computing cosine similarity scores that indicate semantic alignment between user intents and website content.

Important Line Explained

Embedding Model Overview: all‑MiniLM‑L6‑v2

Overview

The all‑MiniLM‑L6‑v2 model is a small, high-performance transformer-based language model designed specifically for generating sentence and paragraph embeddings. With approximately 22 million parameters, the model produces 384-dimensional dense vectors that encode semantic meaning and is widely used for tasks such as semantic search, clustering, and information retrieval.

Fine-tuned using contrastive learning on over one billion sentence pairs, this lightweight model captures nuanced semantic relationships despite its small size. It supports automatic integration via the Sentence-Transformers and Hugging Face frameworks, offering fast inference and flexible deployment across both CPU and GPU environments.

Architecture and Design

all‑MiniLM‑L6‑v2 is built upon the MiniLM architecture, consisting of six transformer layers derived through knowledge distillation from larger models like BERT or RoBERTa. Training uses a contrastive objective, enabling the model to embed semantically similar sentences into nearby vector space locations. Training data sources include large-scale public corpora such as Reddit comments, Wikipedia, and question-answer forums.

The final output is a 384-dimensional vector, facilitating efficient yet robust semantic similarity computations. Input text beyond 256 BPE tokens is automatically truncated, helping maintain performance and minimize noise.

Benefits for This Project

1. Efficiency at Scale With only 22 million parameters, the model delivers fast inference and low memory usage — ideal for the batch processing of multiple URLs and queries where embedding thousands of text blocks is required.

2. Semantic Relevance Due to its contrastive training on diverse sentence pairs, the model produces embeddings that reflect meaning rather than lexical overlap. This supports accurate ranking of content blocks based on semantic alignment with decomposed sub-queries.

3. Balanced Performance In benchmarks, all‑MiniLM‑L6‑v2 offers accuracy close to larger models like all-mpnet-base-v2, but with a 5x improvement in speed. This balance is critical for maintaining responsiveness in SEO analysis workloads.

4. Deployment Flexibility Compatible with common model wrappers (Sentence-Transformers, Hugging Face) and hardware (CPU/GPU), the model enables easy integration into various environments—such as local colabs, servers, or client platforms.

Model Capabilities and Limitations

Embedding Quality: Delivers robust semantic representations for sentences and short paragraphs, supporting clustering, similarity scoring, and information retrieval tasks out of the box.
Input Constraints: Text longer than 256 tokens will be truncated, so embedding units must be of manageable size. This matches the project’s design, as content blocks are typically paragraph-level.
Resource Suitability: Optimized for scenarios where performance and speed are prioritized over ultra-high accuracy, while still providing reliable results for SEO analysis.

Summary of Model Fit

The all‑MiniLM‑L6‑v2 model delivers a compelling combination of semantic quality, operational efficiency, and deployment versatility. It seamlessly aligns with the project’s need to generate precise, semantically-grounded relevance scores across many content blocks with minimal resource overhead.

Together with the query-decomposition capabilities of Mistral‑7B‑Instruct, this embedding model completes the core pipeline structure—enabling intent-level content ranking that is fast, accurate, and scalable.

Query Embedding: embed_query

Overview

The embed_query function transforms a single SEO query or sub-query into a dense vector representation using a pre-initialized embedding model. This vector serves as the semantic signature of the query and is critical for computing relevance scores when comparing with pre-embedded content blocks.

This step allows the system to evaluate how closely each decomposed query aligns with specific content sections across multiple webpages.

Important Line Explained

This single line executes the core logic. The function delegates the task of embedding to the get_text_embedding method of the HuggingFaceEmbedding model wrapper (imported from LlamaIndex). The model converts the input query into a fixed-size dense vector that encodes its semantic meaning. Internally, this may involve tokenization, model inference, and optional pooling or normalization—depending on the embedding architecture used.

Practical Importance

This function provides semantic grounding for each decomposed sub-query. By converting natural language into a mathematical format, it enables accurate and efficient similarity scoring using cosine similarity. In turn, this drives the core logic for content ranking by query relevance—helping identify which blocks best answer each user intent.

While compact in implementation, this function plays a critical role in connecting the query decomposition layer with the embedding and ranking pipeline.

Block Embedding: embed_blocks

Overview

The embed_blocks function processes a list of pre-cleaned content blocks and computes a dense embedding for each. Each block consists of a block ID, HTML tag name, and the cleaned textual content. These embeddings are used later to compare semantic similarity with query vectors, enabling precise ranking of content based on relevance to decomposed search intents.

The function returns an enriched list of tuples containing the original metadata along with the computed embedding for each block.

Important Line Explained

This list comprehension iterates over all content blocks and embeds their text content using the embed_query function. Each result is returned as a tuple: (block_id, tag_type, block_text, embedding_vector).

embed_query(embed_model, text) internally calls the embedding model’s get_text_embedding method, transforming each block’s content into a semantic vector.
The metadata (block ID and tag) is preserved, which allows traceability and structured output in later display and export stages.

Practical Importance

Embedding content blocks is essential for scalable semantic search and intent alignment. These vectorized representations allow the system to compare each sub-query against all blocks using cosine similarity. This step ensures that even loosely worded or rephrased sub-queries can retrieve the most relevant textual content—bridging gaps between diverse query formulations and web content.

This function also ensures that all relevant metadata (ID and tag) remains coupled with each embedding, allowing for structured downstream usage in display logic, ranking traceability, and CSV exports.

Relevance Scoring: rank_blocks_by_query_relevance

Overview

The rank_blocks_by_query_relevance function is responsible for ranking all content blocks in a webpage based on their semantic similarity to a given sub-query. This function takes a precomputed query embedding and a list of block embeddings—each consisting of block metadata and its vector representation. Using cosine similarity as the metric, it computes the relevance score for each block and returns the top-k most relevant matches.

This step serves as the foundation for surfacing the most meaningful sections of a webpage that align with individual aspects of a complex SEO query. It is a critical component for generating actionable insights from decomposed search intents.

Important Line Explained

This line performs the core operation of the function: calculating the cosine similarity between the sub-query’s embedding and the content block’s embedding. Cosine similarity is a widely accepted metric in semantic search, capturing how directionally aligned two vectors are in high-dimensional space.

dot(query_embedding, emb): Measures the inner product between the query and block vectors.
norm(query_embedding) and norm(emb): Compute the L2 norms (magnitudes) of each vector to normalize the result.

By normalizing the dot product, the function ensures that scores represent pure semantic closeness, independent of vector magnitude.

This line sorts all blocks in descending order of their similarity scores and selects the top k blocks—ensuring that only the most relevant content is retained for each sub-query.

Function: export_results_to_csv

Overview

The export_results_to_csv function is responsible for converting the final structured output of the entire query decomposition and content relevance pipeline into a standard CSV file format. This utility serves as the data delivery mechanism, allowing clients to download, store, share, or further analyze the results outside the notebook environment.

The function receives a nested result dictionary structured by URL → Original Query → Sub-Query → Top Content Blocks. It then systematically flattens and writes this information to a well-formatted CSV file with explicit columns for each relevant attribute.

Each row in the exported CSV file represents a single match between a sub-query and a webpage content block, including key metadata: the originating URL, the original complex query, the decomposed sub-query, block ID, HTML tag type, computed relevance score, and the matched content snippet. This makes the file actionable and easy to interpret for SEO professionals and stakeholders.

Display Function: display_ranked_results

Overview

The display_ranked_results function is designed to output the final results of the query decomposition and content relevance analysis in a clear, client-facing format. This function formats the output for readability and interpretability, ensuring the clients—whether SEO specialists or general website owners—can easily understand which parts of their webpage content align most closely with specific sub-queries.

The function optionally accepts an original complex query and a list of ranked results (sub-queries and their matched blocks). For each sub-query, it displays the top-k most semantically relevant content blocks along with their respective relevance scores. This format supports direct interpretability and actionability.

Result Analysis and Explanation

The system processes the original query by decomposing it into multiple targeted sub-queries and identifying the most relevant content blocks for each. This process enables fine-grained alignment between user intent and document content, improving the interpretability and actionability of SEO data.

Original Query Interpretation and Breakdown

The original query was:

“What essential metrics needs to track for SEO success and how they can improve SEO?”

This broad question was effectively decomposed into four focused sub-queries that reflect distinct SEO strategies and measurement areas:

Which SEO metrics are crucial for measuring the success of a website’s SEO efforts?
How can keyword rankings be improved to enhance SEO performance?
What role do backlinks play in SEO success and how can they be acquired effectively?
How can on-page optimization techniques, such as meta tags and content, contribute to improved SEO?

Each sub-query reflects a specific dimension of SEO—ranging from performance measurement to ranking, link-building, and on-page strategies—allowing deeper coverage and more precise content alignment.

Sub-Query Relevance Scoring and Ranking

Each sub-query was used to rank content blocks from the page based on cosine similarity between dense vector embeddings. This numerical relevance score quantifies how closely a block of content matches the query’s meaning.

Key insights for each sub-query:

Sub-Query 1: Which SEO metrics are crucial for measuring the success of a website’s SEO efforts?

· Top Score: 0.6682

· Interpretation: The scores here are in the mid-range (~0.65–0.67), indicating partial alignment with the query. The top-ranked block discusses Core Web Vitals, which are essential performance metrics recognized by Google. The presence of “Share of Search” as a recurring topic in the next two blocks shows semantic relevance around visibility metrics.

· Assessment: Content moderately supports the query. These scores may indicate the page touches on SEO metrics but lacks concentrated detail on broader measurement frameworks.

Sub-Query 2: How can keyword rankings be improved to enhance SEO performance?

· Top Score: 0.7642

· Interpretation: All three top blocks score well above 0.75, suggesting strong relevance. The top block gives a thorough explanation of how keyword rankings impact SEO visibility and includes several practical strategies (optimization, backlinking, algorithm tracking).

· Assessment: This section of the content is highly aligned with the sub-query. Strong content performance indicates effective coverage of ranking optimization strategies.

Sub-Query 3: What role do backlinks play in SEO success and how can they be acquired effectively?

· Top Score: 0.8327

· Interpretation: This sub-query achieved the highest scoring blocks, with values between 0.76 and 0.83. The top block provides foundational understanding, followed by trustworthiness signals and actionable backlink acquisition strategies.

· Assessment: This is a strong content area for the page. High scores reflect both conceptual depth and tactical advice. This also implies that the page could perform competitively for backlink-related SEO queries.

Sub-Query 4: How can on-page optimization techniques, such as meta tags and content, contribute to improved SEO?

· Top Score: 0.6401

· Interpretation: The scores are lower here (~0.56–0.64), indicating weaker relevance. Although related terms like meta tags and keyword placement appear, the content may not fully elaborate or focus on comprehensive on-page optimization.

· Assessment: This area may benefit from content expansion or revision to improve semantic coverage and depth. It appears under-represented compared to other SEO dimensions like backlinks and keyword rankings.

Score Threshold Interpretation

Scores above 0.75: High relevance — content is strongly aligned with user intent and suitable for SEO targeting.
Scores between 0.65–0.75: Moderate relevance — content partially addresses the query but may benefit from improvement or refinement.
Scores below 0.65: Low relevance — indicates a lack of direct or complete coverage; potential gaps in SEO focus.

These thresholds help content strategists and website owners assess what content is strong and what may require rewriting or augmentation to enhance SEO alignment.

Conclusion on Analysis

The decomposition and ranking approach allows pinpointing strengths and weaknesses across the SEO content spectrum. In this result:

Strong Areas: Backlinks and keyword strategy are well-covered.
Moderate Areas: Core metrics receive partial focus.
Weak Areas: On-page optimization appears underdeveloped.

This kind of insight supports focused content optimization, guiding updates not just at the page level, but at the section and intent level—making SEO more precise, measurable, and scalable.

Result Analysis and Explanation

This section outlines the effectiveness of the system in interpreting a complex SEO-related query, breaking it down into semantically rich sub-questions, and mapping each one to the most relevant segments of content from different website URLs. The goal is to evaluate how well the content of each page addresses specific, high-intent aspects of the query and to identify areas where SEO relevance is strong or needs improvement.

Sub-Query Decomposition and Intent Alignment

Complex SEO queries often contain multiple intertwined intents, making it difficult to directly match them with relevant content. In this system, such a complex query is decomposed into a set of focused sub-queries. Each sub-query represents a clear and actionable intent—for example, exploring ranking strategies, assessing on-page practices, or evaluating off-page contributions like backlinks or reviews.

This decomposition enhances the system’s ability to precisely measure how well a webpage addresses different dimensions of SEO strategy, rather than assessing the page’s relevance based on a single, vague query. The process also enables search optimization for long-tail keywords tied to each sub-question.

Relevance Score Thresholds and Interpretation

Each sub-query is matched against the content blocks of a webpage using dense vector embeddings. The comparison yields a relevance score, calculated through cosine similarity, which ranges between 0 and 1. Higher scores reflect stronger semantic alignment with the sub-query.

General score interpretation:

Above 0.75: Strong alignment. The content block directly addresses the sub-query and is highly useful.
Between 0.60 and 0.75: Moderate alignment. The block is somewhat relevant but may not fully satisfy the intent.
Below 0.60: Weak alignment. The content only tangentially relates to the query or lacks actionable value.

These thresholds help determine whether a page covers all critical subtopics of a query and whether each section of content is SEO-optimized for clarity, depth, and specificity.

Relevance Distribution Across URLs

In this evaluation, two different URLs were assessed using the same original query. The sub-queries derived from it covered multiple angles of SEO performance, including metrics tracking, keyword ranking strategies, on-page and off-page optimizations, and overarching content clarity.

· For one URL, most relevance scores for sub-queries remained below 0.50, indicating that while the page addresses technical aspects of SEO such as HTTP headers and indexing behaviors, it does not sufficiently cover performance metrics, keyword strategies, or optimization techniques in depth. These lower scores suggest a narrow topical focus or limited strategic depth, which may hinder the page’s performance for broad SEO-related queries.

· The second URL, in contrast, demonstrated consistently higher scores across all sub-queries, many ranging between 0.45 and 0.49, indicating moderate to strong coverage. The content discussed competitive benchmarks, local optimization strategies, user engagement signals, and keyword-related ranking insights. Although not exceeding high thresholds (0.75+), the scores suggest that the content is semantically aligned and tactically oriented, especially for business profile optimization and localized SEO metrics.

This contrast shows how the same original query can be differently addressed across multiple pages and reveals the strategic coverage and SEO positioning strength of each.

Block-Level Content Insights

Each top-ranked block is also identified by its block ID and HTML tag type (such as <p> or <h2>). This allows content strategists to map relevance directly to individual content sections and structure. In several cases, strong blocks were drawn from mid or lower positions in the document, demonstrating that deeper content, not just headers, contributes meaningfully to relevance. This block-level granularity helps:

Identify which sections are already optimized for user intent.
Detect where content restructuring or rewriting may boost SEO performance.
Understand which content types (paragraphs, headings) carry the most semantic weight for specific queries.

Overall Evaluation

The results reveal that sub-query decomposition combined with relevance ranking can uncover targeted strengths and weaknesses in a page’s SEO content. A page may score well for one sub-intent (e.g., off-page optimization) but underperform on another (e.g., keyword ranking methods). This kind of fine-tuned diagnosis provides direct input for content refinement, allowing SEO teams to:

Align existing content more closely with segmented search intent.
Create new, high-quality content blocks that address underrepresented sub-topics.
Improve keyword mapping across headers, metadata, and paragraphs to support better coverage of long-tail search queries.

By offering this level of precision, the system transforms traditional SEO audits into actionable, structured, and context-aware optimization strategies—resulting in higher relevance, better user satisfaction, and increased visibility in search engine rankings.

How does this project break down complex SEO queries and why is that valuable?

At the core of the project is a process that takes an original complex query and breaks it into multiple, focused sub-queries. Each sub-query represents a specific facet of the user’s intent, such as metrics tracking, on-page strategies, or backlink acquisition. This decomposition allows the system to evaluate page relevance at a granular level, revealing which parts of a page speak effectively to each individual intent.

This process is critical in SEO where user queries are becoming increasingly multifaceted. Instead of relying on a single generalized relevance score, this approach provides a multi-dimensional evaluation that supports more targeted optimization and ensures no major aspect of the user’s needs is overlooked.

What practical information do the relevance scores provide to website owners or SEO teams?

Relevance scores serve as a quantitative reflection of how well each content block aligns with each sub-query. The score is based on semantic similarity between the query embedding and the block’s content embedding. Scores generally range from 0.0 to 1.0, with higher values indicating stronger relevance:

Scores above 0.7: Highly relevant – typically indicates strong content alignment with user intent.
Scores between 0.5 and 0.7: Moderately relevant – content partially satisfies the sub-query.
Scores below 0.5: Weak relevance – often signals lack of clarity or misalignment.

This granularity allows clients to understand not just if their content is relevant, but how relevant and to which part of the query.

What actions should be taken when relevance scores are consistently low for certain sub-queries?

Low relevance scores across multiple blocks for a particular sub-query suggest content gaps or semantic misalignment. The following corrective steps are recommended:

Introduce dedicated sections on the page to directly address the sub-query theme.
Refine or rewrite existing paragraphs to focus more precisely on the sub-topic.
Incorporate high-signal phrases or examples that are typical for the sub-query’s subject area.
Use internal linking to relevant high-quality pages if the main content lacks sufficient coverage.

By mapping these actions to specific sub-queries, website owners can pursue precision SEO optimization, reducing content dilution and improving user satisfaction.

What content strategy benefits can be derived from query decomposition?

Query decomposition creates an actionable SEO roadmap. Each sub-query can guide:

New content creation for uncovered or under-covered sub-topics.
Page restructuring to improve semantic clarity.
Editorial prioritization by identifying high-impact areas with weak coverage.

Rather than producing content based on assumptions or broad topics, this strategy uses actual decomposed intents to ensure alignment with real-world search behavior.

How can relevance scoring support decision-making across a large content portfolio?

The scoring output enables comparative content analysis across URLs for the same query. This makes it possible to:

Benchmark different pages against the same sub-query set.
Detect which pages serve certain intents better than others.
Make data-driven decisions about consolidation, internal linking, or canonical selection.

This supports strategic SEO planning across category pages, product pages, and blog content, enhancing site-wide semantic coverage and authority.

What specific SEO benefits can be expected from improving content based on this project’s outputs?

When content blocks are revised or expanded based on the sub-query scores, the following SEO gains are expected:

Improved visibility for long-tail and intent-rich keywords.
Increased topical relevance and depth of individual pages.
Higher chance of ranking for multiple sub-intents of a user’s query.
Strengthened crawlability and indexability, as structured and focused content is easier to evaluate for relevance.

These benefits directly contribute to better rankings, higher engagement, and reduced bounce rates.

How does this system handle variations in SEO query formats and user intents?

By applying semantic embeddings and not relying on surface-level keyword matching, this system generalizes across phrasing differences. For example, “How can keyword rankings be improved?” and “What helps increase keyword visibility?” are treated as semantically similar. This enhances the system’s ability to match sub-queries to content even when language usage varies, making it robust and adaptive to real-world user behavior.

How does this project align with Google’s evolving emphasis on relevance, intent, and content quality?

The project addresses core areas of modern search engine expectations:

Intent alignment through query decomposition.
Contextual understanding using dense embeddings.
Content quality measurement via relevance scoring per sub-intent.

These are consistent with best practices outlined in Google’s Search Quality Evaluator Guidelines, particularly around E-E-A-T, user-centric content, and multi-intent query handling.

What actions should clients take after receiving the results?

Clients are advised to:

Review sub-query coverage for each key landing page.
Identify sub-topics with weak scores and add targeted content to address those gaps.
Retain and highlight content blocks with strong scores, possibly repositioning them higher on the page.
Use the findings to plan future content and optimize internal linking around semantically related content.

By following these steps, clients can progressively build content that performs well across diverse search intents, gaining sustained SEO advantage.

What actions should be taken when a page shows low relevance scores across sub-queries?

When multiple blocks on a page consistently receive low scores for a sub-query, this indicates a gap in content relevance. Recommended actions include:

Adding new content that directly addresses the missed sub-topic.
Rewriting existing content using clearer, intent-aligned language.
Improving keyword specificity by incorporating terms closely tied to the sub-query’s focus.
Reassessing content structure, such as creating distinct sections for different SEO dimensions (on-page, off-page, metrics, etc.).

These steps ensure that every angle of the original query is covered with clarity and depth, leading to improved alignment with both user expectations and search engine algorithms.

How should strong-performing blocks be used in SEO strategy?

Blocks with high relevance scores reflect well-optimized and semantically strong content. These sections can serve as models for other areas of the site. Best practices include:

Replicating the style, tone, and structure of high-performing blocks in other content areas.
Reusing or linking to strong blocks in content clusters to reinforce topic authority.
Ensuring these blocks are properly indexed and accessible, possibly by enhancing internal linking or schema markup.

This promotes better topic coverage site-wide and signals to search engines the depth and consistency of relevant content.

Can these results help in content planning and editorial workflows?

Yes, the decomposition and scoring output can serve as a content planning blueprint. Editorial teams can prioritize sub-queries with weak support, identify themes for new blog posts or guides, and use the result data to structure landing pages around real search intent. This leads to more targeted publishing cycles and improves topical authority over time.

Which key features of this project deliver measurable business value to clients?

The following features, backed by the project’s implementation, directly support client ROI:

Semantic sub-query breakdown enables high-precision content diagnostics.
Granular relevance scoring facilitates informed optimization actions.
Cross-URL scoring comparison enables strategic decision-making.
Actionable SEO signals guide writers and strategists in targeting meaningful improvements.
Generalization across domains and topics allows consistent usage in blogs, landing pages, and technical content.

These features ensure that every decision made using this tool is supported by clear evidence and aligned with measurable outcomes, reducing guesswork and maximizing SEO effectiveness.

Final Thoughts

This project demonstrates a structured and scalable approach to addressing the growing complexity of search behavior and content expectations in modern SEO. By breaking down multi-intent queries into focused sub-queries and evaluating page content against each of them, the system offers granular, relevance-oriented insights that go far beyond traditional keyword-based methods.

The integration of dense embeddings, semantic scoring, and content block-level evaluation enables clients to make informed decisions backed by precise data. Whether identifying underperforming areas, optimizing high-value sections, or planning content expansion strategies, the outputs support targeted, high-impact SEO actions.

Ultimately, this solution empowers clients to align their web content more effectively with real-world search intent, delivering improved visibility, engagement, and search performance across varied SEO contexts.

If you’re not leveraging this update yet, you’re missing a chance to turn searchers into loyal buyers—before they even click through to your site. So start marking up your loyalty program today, and give your brand the rich visibility it deserves.

Tuhin Banik

Thatware | Founder & CEO

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