Semantic Binder for Search Content Enhancement
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Solution Overview
Problem
Current search engines struggle to effectively retrieve relevant documents from databases, especially in technically oriented databases, due to repetitive queries and the exclusion of relevant documents that do not contain specific keyword combinations or use different terminology, leading to user dissatisfaction.
Innovation Solution
The implementation of a semantic binder that associates documents with semantic nodes in a hierarchical taxonomy, transforming user queries into semantic nodes to include relevant documents that may not contain the original keywords, using a log analyzer to update the semantic dictionary and textual indices, allowing for broader search results without affecting response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search queries are made more specific to improve search result relevance, then search accuracy improves, but relevant documents may be excluded that use different terminology or do not contain exact keyword combinations
Solution Approach 1:
The patent introduces a semantic binder as an intermediary component that maps query terms to semantic nodes in a taxonomy. This mediator translates user queries into semantic concepts, allowing the system to retrieve documents based on semantic similarity rather than exact keyword matching, thus resolving the contradiction between search accuracy and result coverage
Solution Approach 2:
The system changes the search parameter from exact keyword matching to semantic node matching. By transforming queries into semantic nodes and using threshold-based matching instead of exact string matching, the system achieves both accuracy and broader coverage of relevant documents
2Adaptability or versatility
If search engines add synonymous search terms to broaden results, then search coverage improves, but additional real time examination of database logs and databases is required
Solution Approach 1:
The patent pre-processes and stores semantic taxonomies and term-to-node mappings in advance. When a query arrives, the system only needs to perform the quick lookup of transforming query terms into semantic nodes, rather than performing comprehensive real-time analysis of all database logs and contents, thus reducing processing time while maintaining broad search coverage
3Productivity
If search engines use traditional keyword matching methods, then search speed is maintained, but relevant documents are missed that do not contain requested query terms
Solution Approach 1:
The semantic binder acts as a mediator that bridges query terms and document content through semantic nodes. This allows the system to maintain fast search operation by using pre-computed semantic mappings while simultaneously discovering relevant documents that would be missed by traditional keyword matching alone
Data Source
AI summary
Whenever a document is going to be included into the textual database, a semantic binder is used to associate the document with one or more semantic nodes which are defined in a semantic taxonomy. When a search is performed, a search application looks through a semantic dictionary (which contains a table mapping queries to nodes on the semantic taxonomy) to see whether any corresponding semantic node can be found for the searchers query. If a match is found, the search application transforms the user's query into [“original query” OR “semantic node”] so that relevant documents, even they do not contain any user's keyword, can also be found in the database. The system binds semantic nodes arranged in a hierarchical structure of the taxonomy using a Log Analyzer which periodically looks through the system log for new queries and through textual indices for documents added to the database to generate the semantic dictionary and to bind the semantic nodes to the queries in the textual indices of the documents.