Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs

Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, Zhimeng Jiang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Knowledge graphs (KGs) have been widely used to enhance complex question answering (QA). To understand complex questions, existing studies employ language models (LMs) to encode contexts. Despite the simplicity, they neglect the latent relational information among question concepts and answers in KGs. While question concepts ubiquitously present hyponymy at the semantic level, e.g., mammals and animals, this feature is identically reflected in the hierarchical relations in KGs, e.g., a_type_of. Therefore, we are motivated to explore comprehensive reasoning by the hierarchical structures in KGs to help understand questions. However, it is non-trivial to reason over tree-like structures compared with chained paths. Moreover, identifying appropriate hierarchies relies on expertise. To this end, we propose HamQA, a novel Hierarchy-aware multi-hop Question Answering framework on knowledge graphs, to effectively align the mutual hierarchical information between question contexts and KGs. The entire learning is conducted in Hyperbolic space, inspired by its advantages of embedding hierarchical structures. Specifically, (i) we design a context-aware graph attentive network to capture context information. (ii) Hierarchical structures are continuously preserved in KGs by minimizing the Hyperbolic geodesic distances. The comprehensive reasoning is conducted to jointly train both components and provide a top-ranked candidate as an optimal answer. We achieve a higher ranking than the state-of-the-art multi-hop baselines on the official OpenBookQA leaderboard with an accuracy of 85%.

Original languageEnglish
Title of host publicationACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
PublisherAssociation for Computing Machinery, Inc
Pages2519-2527
Number of pages9
ISBN (Electronic)9781450394161
DOIs
Publication statusPublished - 30 Apr 2023
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023

Publication series

NameACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period30/04/234/05/23

Keywords

  • graph neural networks
  • knowledge graphs
  • multi-hop question answering

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Fingerprint

Dive into the research topics of 'Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs'. Together they form a unique fingerprint.

Cite this