Document Object Model Node Weighting for Search Ranking
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Solution Overview
Problem
Current search engine systems face challenges in accurately ranking documents based on their relevance to search queries, as they rely on manual weighting of document sections and lack a systematic approach to determine semantic importance, leading to inefficient memory usage and suboptimal search results.
Innovation Solution
The implementation of a Document Object Model (DOM) that generates a platform- and language-neutral interface, allowing programs to dynamically access and update document content, structure, and style, and assigns weights to nodes based on search terms, enabling the calculation of a scoring function for ranking documents semantically relevant to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual weighting of document sections is used, then document ranking can be performed, but the system complexity increases and memory usage becomes inefficient
Solution Approach 1:
The system automatically generates a document object model and assigns weights to nodes based on search term occurrences, eliminating the need for manual weight assignment by users. The weighting process is self-executing through algorithmic analysis of document structure and query relevance.
Solution Approach 2:
The system dynamically changes weight parameters assigned to different document sections based on the specific search query and document content. Weights are not fixed but are adjusted according to the number of search terms contained in each portion of the document, making the ranking adaptable to different queries.
2Ease of operation
If manual weighting of document sections is used, then document ranking can be performed, but memory space is wasted in the search index
Solution Approach 1:
The system extracts only the essential information needed for ranking by identifying specific nodes in the document object model that contain search terms. Instead of indexing and weighting all document sections uniformly, it selectively processes and weights only the relevant portions, reducing memory consumption.
Solution Approach 2:
Different weight values are assigned to different sections of the document based on their relevance to the search query. Rather than applying a uniform weighting scheme, the system applies localized weighting where high-weight nodes correspond to portions containing search terms and low-weight nodes correspond to less relevant portions, optimizing memory usage.
3Ease of manufacture
If uniform weighting is applied to all document parts, then indexing is simplified, but search result accuracy decreases
Solution Approach 1:
The document is segmented into multiple nodes within the document object model, allowing differential weighting of different sections. The system divides the document structure into identifiable portions (such as title, body, metadata) and assigns weights to each segment based on its relevance to the search query, improving accuracy while maintaining manageable indexing.
Data Source
AI summary
Methods, systems, and apparatus, including computer program products, for assigning weights to parts of a document. In one aspect, document object is generated for a collection of documents. Weights for the respective portions of the document object model are generated, and a document is ranked in a search engine index, the ranking being based at least in part on the generated weights.