Semantic Network Concept Synthesis for Ambiguous Search Queries
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Solution Overview
Problem
The challenge lies in determining and presenting relevant information to users from a large set of digital content, particularly when search queries contain terms that appear infrequently or have ambiguous meanings, leading to overwhelming irrelevant results.
Innovation Solution
The method involves using semantic networks to identify and synthesize concepts relevant to user context information, scoring these concepts based on relevance measures like generation certainty, concept productivity, Jaccard, statistical coherence, and cosine similarity, and selecting them to provide targeted information to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search engines are used to retrieve information from large digital content sets, then the quantity of available information is vast, but the relevance and accuracy of search results deteriorate due to infrequent or ambiguous terms
Solution Approach 1:
The patent introduces semantic networks as an intermediary layer between user queries and digital content. Instead of direct keyword matching, the system translates queries into semantic concepts within a knowledge graph, enabling indirect retrieval that handles infrequent and ambiguous terms more effectively. This mediator resolves the contradiction by maintaining comprehensive content access while improving search relevance through semantic understanding.
Solution Approach 2:
The system changes the retrieval parameters from literal keyword frequency to semantic concept relevance. By transforming search queries into semantic representations and using multiple relevance measures (generation certainty, concept productivity, Jaccard, statistical coherence, cosine similarity), the system can accurately retrieve information even when exact keyword matches are rare or ambiguous, thus improving precision without sacrificing content volume.
2Measurement precision
If semantic networks with multiple relevance measures are implemented, then information retrieval accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the information retrieval system into distinct functional modules: semantic network construction, concept synthesis, relevance measurement, and information selection. Each module handles a specific aspect of the retrieval process independently, making the overall complex system more manageable and maintainable while achieving high accuracy through coordinated operation of these segmented components.
Solution Approach 2:
The semantic network serves multiple functions simultaneously: it represents knowledge, enables concept synthesis, provides relevance measurement benchmarks, and guides information selection. By making the semantic network multi-functional, the system achieves high retrieval accuracy without proportionally increasing complexity, as one structure performs multiple critical roles.
3Measurement precision
If multiple relevance measures are used to score concepts, then the quality of selected information improves, but computational resources and time consumption increase
Solution Approach 1:
The system performs preliminary computation by pre-calculating and storing semantic representations, concept relationships, and relevance metrics in the semantic network before actual queries are received. This preliminary preparation allows the system to quickly retrieve and score concepts during real queries without performing all computations from scratch, thus reducing response time while maintaining high selection quality through multiple relevance measures.
Data Source
AI summary
Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information. The method further comprises obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept, wherein the first concept in a semantic network is represented by a data structure storing any data associated with a node in the semantic network.