No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph
원문 초록
Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting and most approaches currently use a pipeline of processing steps connecting a sequence of components. In this article, we analyse and micro evaluate the behaviour of 29 available QA components for DBpedia knowledge graph that were released by the research community since 2010. As a result, we provide a perspective on collective failure cases, suggest characteristics of QA components that prevent them from performing better and provide future challenges and research directions for the field.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs
iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.