Hypergraph Search for Coherent Entity Retrieval
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Solution Overview
Problem
Existing search technologies struggle to efficiently process and retrieve coherently related entities from large datasets, particularly in extracting meaningful relationships between multiple entities.
Innovation Solution
The method generates a knowledge graph from identified entities, where nodes represent entities and edges represent pair-wise relationships with edge scores quantifying coherence. A hypergraph is then generated from the knowledge graph, with hyperedges representing relationships between multiple entities and carrying hyperedge scores that quantify coherence between these entities. This framework allows for efficient search and retrieval of coherently related entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search technologies are used to retrieve entities from large datasets, then the search process is simple and fast, but the ability to extract coherently related entities and meaningful relationships between multiple entities is poor
Solution Approach 1:
The patent segments the search problem into two distinct phases: (1) constructing a knowledge graph that captures pairwise relationships between entities, and (2) generating a hypergraph from the knowledge graph to capture higher-order relationships. This segmentation allows each phase to be optimized independently, improving coherence without overwhelming complexity
Solution Approach 2:
The patent transitions from traditional pairwise relationships (2D) to hypergraph relationships that connect multiple entities simultaneously (higher dimensions). By representing relationships between multiple entities as hyperedges in a hypergraph, the system captures complex coherent relationships that cannot be expressed through simple pairwise connections
2Adaptability or versatility
If knowledge graphs with pair-wise relationships are used, then the structure is simple and easy to construct, but the ability to represent relationships between multiple entities is limited
Solution Approach 1:
The patent implements a nested structure where a hypergraph is constructed from an existing knowledge graph. The knowledge graph (with nodes and edges) is nested within the hypergraph framework, allowing the system to retain the simplicity of pairwise relationships while adding the versatility of multi-entity relationships through hyperedges that can encompass multiple nodes
3Measurement precision
If hyperedges representing multiple entity relationships are introduced, then the coherence measurement is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing pairwise relationship scores in the knowledge graph phase. These pre-computed scores are then reused when constructing hyperedges and calculating hyperedge scores, avoiding redundant computations and reducing the overall computational burden of measuring coherence for multiple entities
Data Source
AI summary
Provided are techniques for performing a search using a hypergraph. Entities are identified. A knowledge graph using the entities is generated, wherein nodes of the knowledge graph represent the entities and edges between the nodes represent pair-wise relationships, and wherein each of the edges carries an edge score that quantifies a degree of coherence between a pair of the entities. A hypergraph using the knowledge graph is generated, wherein nodes of the hypergraph represent the entities and hyperedges represent relationships between multiple entities, and wherein each of the hyperedges carries a hyperedge score that quantifies a degree of coherence between the multiple entities. A search request is received. A search result is generated using the hypergraph, wherein the search result comprises a set of coherently related entities. The search result is returned.