Semantic Interest Network for Social Media Retrieval
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Solution Overview
Problem
Conventional search engines face challenges in accurately identifying and providing relevant information to users due to the vast volume of digital content and the mismatch between search query terms and content terms, often resulting in irrelevant results.
Innovation Solution
The system utilizes user context information and semantic networks to generate an interest network, filtering and ranking information based on semantically relevant concepts, which are identified and scored to provide users with targeted content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional search engines retrieve information based on keyword matching, then the volume of retrieved information is large, but the relevance and accuracy of results deteriorate
Solution Approach 1:
The patent introduces semantic networks and interest networks as intermediary structures between user queries and content databases. These semantic networks map relationships between concepts and entities, allowing the system to translate keyword searches into semantically meaningful queries that retrieve more relevant results while maintaining manageable volumes.
Solution Approach 2:
The system changes the parameter of information retrieval from simple keyword matching to semantic similarity scoring. By calculating semantic proximity between query terms and content based on structured knowledge representations, the system improves result relevance without proportionally increasing the volume of retrieved information.
2Measurement precision
If the system uses semantic networks and interest networks to filter and rank information, then the relevance of retrieved information improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent pre-computes and stores semantic networks, concept hierarchies, and entity relationships in structured formats before actual information retrieval operations. This preliminary structuring of knowledge allows the system to perform semantic matching during queries without conducting full semantic analysis in real-time, thereby reducing computational complexity while maintaining high relevance.
Solution Approach 2:
The system segments the large-scale semantic processing into manageable components: concept extraction, relationship mapping, interest network construction, and query matching. Each component operates independently on specific data structures, making the overall complex system more tractable and efficient than monolithic semantic processing.
3Adaptability or versatility
If the system generates personalized interest networks for each user, then the tailoring of content to user interests improves, but the processing time and computational resources increase
Solution Approach 1:
The patent creates a universal interest network structure that can serve multiple users with different preferences. Rather than building completely separate semantic models for each user, the system uses a common framework that is customized through user-specific parameters and weights, reducing redundant processing while maintaining personalization.
Solution Approach 2:
The system performs partial semantic processing by focusing on the most relevant portions of the knowledge base for each user query based on their interest profile. Instead of analyzing the entire semantic network for every query, it selectively processes only the subsets of concepts and relationships most likely to match user interests, significantly reducing processing time.
Data Source
AI summary
Disclosed is a system and method for performing a semantic operation on a social network. In an embodiment, the method comprises receiving a social network user context associated with a user of the social network; generating, through a semantic operation, an interest network based on the user context information; and filtering, ranking or augmenting, using at least one processor executing stored program instructions, a retrieval of information related to the social network based on the interest network; wherein the interest network comprises concepts represented by a data structure associated with the concepts in the interest network. In another embodiment, the method further comprises representing the interest network as an interest graph. In yet another embodiment, the semantic operation is a synthesis operation or retrieval operation performed on a knowledge representation.