Inference-Driven Multi-Source Semantic Search System
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Solution Overview
Problem
Current information retrieval systems face challenges in identifying relevant documents due to the limitations of keyword searches, which often miss relevant information and retrieve irrelevant data, especially for complex queries that require information from multiple sources.
Innovation Solution
The method involves dividing a query into parts, identifying sources that address each part using a knowledge base, and combining these sources to provide an answer, employing logical proofs and theorem proving to generate sequences of statements that support the query's conclusion, with the knowledge base linking assertions to documents and ranking results by relevance and parsimony.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword searches are used, then search speed is fast, but relevant documents are missed and irrelevant ones are retrieved
Solution Approach 1:
The query is divided into multiple sub-queries or components, each addressing a specific aspect of the information need. The system processes each segment separately and combines results, allowing for more precise matching while maintaining overall search efficiency.
Solution Approach 2:
The system introduces intermediate processing steps between the user query and document retrieval, including query expansion, synonym generation, and multi-stage filtering. These intermediaries bridge the gap between simple keyword matching and complex semantic understanding.
2Measurement precision
If semantic search with natural language understanding is used, then relevance accuracy is improved, but system complexity increases
Solution Approach 1:
The complex semantic search process is broken down into manageable modules: query analysis, entity recognition, relationship extraction, and result synthesis. Each module handles a specific aspect of the problem, making the overall system more tractable and maintainable.
Solution Approach 2:
The system employs universal natural language processing components that can handle multiple types of queries and document formats. These multi-functional modules reduce overall system complexity by avoiding the need for specialized handlers for each query type.
3Loss of information
If multiple sources are combined to satisfy information needs, then completeness of answer is improved, but search time increases
Solution Approach 1:
The system performs preliminary organization and indexing of information from multiple sources during the preprocessing phase. This advance preparation allows for faster retrieval and combination of information when queries are executed, reducing search time while maintaining completeness.
Solution Approach 2:
The system retrieves slightly more information than strictly necessary from multiple sources, then filters and synthesizes the results. This approach ensures that all relevant information is captured while allowing for efficient post-processing to eliminate redundancy.
Data Source
AI summary
A method, system and computer program product are disclosed for searching for information using a knowledge base. In one embodiment, the method comprises receiving a query; formulizing the query, including dividing the query into a plurality of parts; for each of the parts, identifying a source, using the knowledge, that addresses that part; and combining the sources to answer the query. In one embodiment, the query includes text; the text is separated into a plurality of segments; and, for each of the segments, at least one source is identified addressing the segment. In an embodiment, a logical proof is formulated having a conclusion that is an answer to the query, and a sequence of statements that establish said conclusion; and a proof of this conclusion is generated by identifying two or more documents that assert the sequence of statements.