Document Search via Conceptual Associations and Knowledge Base
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Solution Overview
Problem
Traditional information retrieval technologies based on keyword indexing are inefficient in handling queries that do not contain explicit keywords, especially with rich ontologies, and semantic reasoning techniques like latent semantic analysis fail to leverage large volumes of crowd-sourced data effectively.
Innovation Solution
A method and system for searching, recommending, and exploring documents through conceptual associations by extracting concepts from documents, calculating their relation to a knowledge base, and using Markov chain techniques to determine relevance, allowing for efficient data structures to store and query deep conceptual indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If query expansion techniques are used to improve search quality by substituting search terms with synonyms and performing independent searches, then the quality of returned results is improved, but the computational cost and time required for each search query increases significantly
Solution Approach 1:
The system pre-computes and stores conceptual associations, synonym relationships, and document-concept mappings in advance before actual search queries are executed. This preliminary indexing of conceptual relationships allows the search system to quickly retrieve pre-processed association data rather than performing computationally expensive synonym expansion and independent searches in real-time, thus maintaining high search quality while reducing query response time
Solution Approach 2:
The system merges multiple search results from different conceptual associations and synonym variations into a single unified result set. Instead of presenting separate results from each independent search, the system combines and ranks them together, eliminating redundant computations and providing comprehensive results more efficiently
2Measurement precision
If rich ontologies are used to enhance semantic search capabilities, then the depth and accuracy of conceptual understanding is improved, but the number of required searches increases from one to potentially tens of thousands
Solution Approach 1:
The system extracts and stores only the most relevant conceptual associations and relationships from rich ontologies during the indexing phase. Rather than attempting to traverse and search through the entire ontology structure during query processing, the system extracts pre-computed association data that captures essential semantic relationships, thereby maintaining conceptual understanding accuracy while dramatically reducing the complexity and number of searches required at query time
Solution Approach 2:
The system performs preliminary computation of conceptual relationships and stores them in optimized data structures before actual searches are executed. This pre-processing of ontology-based associations allows the system to leverage rich semantic knowledge without incurring the computational overhead of traversing complex ontology structures during real-time query processing
3Measurement precision
If latent semantic analysis techniques are used to project document representations to latent semantic space, then the ability to find conceptually related documents is improved, but the ability to leverage crowd-sourced data from websites like Wikipedia is lost
Solution Approach 1:
The system uses a universal conceptual framework that can process and integrate data from multiple diverse sources including traditional documents and crowd-sourced data from websites like Wikipedia. Rather than being limited to specific document formats or sources, the system extracts and standardizes conceptual information from various sources into a unified representation, enabling both conceptual document matching and effective leveraging of crowd-sourced data through the same mechanism
Data Source
AI summary
According to an aspect, searching, recommending, and exploring documents through conceptual associations includes a method for receiving a plurality of documents and extracting concepts from each of the documents. A degree of relation between each of the documents and concepts in a knowledge base is calculated. The method also includes, in response to receiving a query, determining one or more concepts from the query. For each of the concepts, a list of documents having a highest degree of relation to the concept is retrieved. The method also includes outputting a list that is responsive to the one or more retrieved lists.