Hypergraph Transformer for Knowledge Graph Reasoning
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Solution Overview
Problem
Conventional knowledge-based question answering models face challenges in encoding high-order semantics and multi-hop relations in knowledge graphs, especially under weak supervision, leading to inefficiencies in data collection and reasoning processes.
Innovation Solution
A reasoning method based on a structural attention mechanism using a hypergraph transformer model that encodes high-order semantics through hypergraphs and applies attention mechanisms to improve multi-hop reasoning, allowing for strong reasoning without explicit supervision by focusing on correct reason evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional models use human-supervised reasoning processes for knowledge-based question answering, then reasoning accuracy is improved, but data collection time and cost increase excessively
Solution Approach 1:
The model performs self-reasoning through structured attention mechanisms and hypergraph transformations, automatically generating reasoning paths without human supervision. The system serves itself by internally processing multi-hop reasoning tasks through attention-weighted knowledge graph traversals, eliminating the need for manually annotated reasoning processes while maintaining accuracy.
2Device complexity
If conventional models encode knowledge graph relations without hypergraph structures, then model complexity is reduced, but ability to capture high-order semantics and multi-hop relations deteriorates
Solution Approach 1:
The patent transforms traditional graph structures into hypergraph structures, adding a higher-dimensional representation capability. Hyperedges in the knowledge graph enable simultaneous connection of multiple entities, capturing high-order relations that go beyond pairwise interactions. This dimensional elevation allows the model to represent complex multi-hop reasoning paths and semantic associations that conventional graph structures cannot encode effectively.
3Reliability
If attention mechanisms are applied to all knowledge graph edges, then reasoning accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The attention mechanism is applied selectively to locally relevant edges and hyperedges identified through structured attention scoring. Rather than uniformly processing all knowledge graph edges, the model dynamically assigns attention weights to specific local regions of the graph based on query relevance. This localized attention application maintains reasoning accuracy while significantly reducing computational overhead by focusing resources on critical reasoning paths.
Data Source
AI summary
Disclosed herein are a reasoning method based on a structural attention mechanism for knowledge-based question answering and a computing apparatus for performing the reasoning method. The reasoning method includes: recognizing one or more entities in a query including content and a question, and linking the recognized entities to a knowledge base; constructing a question hypergraph and a query-aware knowledge hypergraph by performing a multi-hop graph walk on a question graph and the knowledge base; and inferring a correct answer to the question by applying as attention mechanism to a query hyperedge and a knowledge hyperedge included in the question hypergraph and the query-aware knowledge hypergraph, respectively.