Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. However, the KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale, thus decreasing the performance of answer selection. To tackle this problem, we first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm and then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them. Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs, and an Enhanced Bilateral Multi-Perspective Matching model is proposed to capture local interactions. Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method. The experimental results on multiple benchmark datasets have demonstrated the effectiveness of our approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Answer SelectionGraph Question AnsweringInformation RetrievalLearning-To-RankQuestion AnsweringRetrievalSimilar Papers 제목 키워드 기반
Interpretable Question Answering with Knowledge Graphs
This paper presents a question answering system that operates exclusively on a knowledge graph retrieval without relying on retrieval augmented generation (RAG) with large language models (LLMs). Instead, a small paraphr…
Question AnsweringKnowledge GraphsAgent-centric Information Access
As large language models (LLMs) become more specialized, we envision a future where millions of expert LLMs exist, each trained on proprietary data and excelling in specific domains. In such a system, answering a query r…
RetrievalRetrieval-augmented GenerationLiteSemRAG: Lightweight LLM-Free Semantic-Aware Graph Retrieval for Robust RAG
Graph-based Retrieval-Augmented Generation (RAG) has shown great potential for improving multi-level reasoning and structured evidence aggregation. However, existing graph-based RAG frameworks heavily rely on exploiting …
TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation
Graph-based Retrieval-augmented generation (RAG) has become a widely studied approach for improving the reasoning, accuracy, and factuality of Large Language Models (LLMs). However, many existing graph-based RAG systems …
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding
Semantic querying in complex 3D scenes through free-form language presents a significant challenge. Existing 3D scene understanding methods use large-scale training data and CLIP to align text queries with 3D semantic fe…
FormGraph GenerationRelational ReasoningScene Understanding