Semantic Graph Entity Extraction for Search Accuracy
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Solution Overview
Problem
Conventional search and recommendation systems fail to provide accurate results due to the exponential growth of content, lacking a semantic understanding of user inputs, and are overwhelmed by excess data, leading to consumer frustration and reduced content accessibility.
Innovation Solution
The implementation of a system using a semantic graph architecture with four distinct stages: pronoun resolution, candidate identification, semantic graph creation, and node scoring, which leverages machine learning to automatically determine the relevance of entities in text strings, providing enhanced search, recommendation, and discovery features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional statistics-driven models are used for entity extraction, then the system can process large amounts of data, but the search accuracy and semantic understanding deteriorate
Solution Approach 1:
The patent introduces semantic graphs as an intermediary structure between raw content data and search queries. The semantic graph creates weighted connections between entities based on their relationships, serving as a mediator that transforms unstructured content into structured semantic knowledge. This intermediary layer enables the system to process large amounts of content while maintaining high search accuracy by leveraging semantic relationships rather than relying solely on statistical matching.
Solution Approach 2:
The system dynamically adjusts the weighting parameters of entity connections in the semantic graph based on their relevance to user queries. By changing the weight parameters of semantic relationships, the system can prioritize more relevant entities and improve search accuracy. The machine learning model automatically determines these weights by analyzing the strength and type of semantic relationships between entities, allowing the system to adapt to different search contexts while handling large content volumes.
2Quantity of substance
If more content is added to the system, then the content availability increases, but the system complexity and data processing burden increase
Solution Approach 1:
The patent segments the large content corpus into discrete entities and their semantic relationships, organizing them into a structured graph format. Instead of processing all content uniformly, the system divides content into extractable entities (people, places, things, concepts) and their interconnections. This segmentation allows the system to manage large content volumes by working with individual entities and their relationships rather than treating content as an unmanageable mass, thereby reducing system complexity while maintaining content availability.
Solution Approach 2:
The system performs preliminary entity extraction and semantic relationship mapping during an offline preprocessing stage, creating the semantic graph structure before actual search operations. By conducting this complex processing work in advance, the system transforms unstructured content into a ready-to-query semantic graph format. This preliminary action eliminates the need to perform complex semantic analysis during real-time searches, significantly reducing online system complexity while preserving full content availability for future queries.
3Ease of manufacture
If traditional search techniques are used, then the system is simple to implement, but the recommendation quality and user satisfaction deteriorate
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically extracts entities, determines their semantic relationships, and constructs the semantic graph without requiring manual curation or complex configuration. The machine learning model autonomously processes content, identifies entities, infers relationships, and assigns weights to connections. This self-service approach maintains implementation simplicity by eliminating the need for manual knowledge graph construction while significantly improving recommendation quality through automated semantic understanding and entity relationship analysis.
Data Source
AI summary
Methods and systems for using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections for more relevant search results and recommendations. For example, by inferring relevant entities, metadata results are richer and more meaningful, enabling faster decision-making for the consumer and stronger viewership for the content owner.