PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs

Ying Su, Jipeng Zhang, Yangqiu Song, Tong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is well acknowledged that incorporating explicit knowledge graphs (KGs) can benefit question answering. Existing approaches typically follow a grounding-reasoning pipeline in which entity nodes are first grounded for the query (question and candidate answers), and then a reasoning module reasons over the matched multi-hop subgraph for answer prediction. Although the pipeline largely alleviates the issue of extracting essential information from giant KGs, efficiency is still an open challenge when scaling up hops in grounding the subgraphs. In this paper, we target at finding semantically related entity nodes in the subgraph to improve the efficiency of graph reasoning with KG. We propose a grounding-pruning-reasoning pipeline to prune noisy nodes, remarkably reducing the computation cost and memory usage while also obtaining decent subgraph representation. In detail, the pruning module first scores concept nodes based on the dependency distance between matched spans and then prunes the nodes according to score ranks. To facilitate the evaluation of pruned subgraphs, we also propose a graph attention network (GAT) based module to reason with the subgraph data. Experimental results on CommonsenseQA and OpenBookQA demonstrate the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationStarSEM 2024 - 13th Joint Conference on Lexical and Computational Semantics, Proceedings of the Conference
EditorsDanushka Bollegala, Danushka Bollegala, Vered Shwartz
PublisherAssociation for Computational Linguistics (ACL)
Pages360-371
Number of pages12
ISBN (Electronic)9798891761063
DOIs
StatePublished - 2024
Externally publishedYes
Event13th Joint Conference on Lexical and Computational Semantics, StarSEM 2024 - Mexico City, Mexico
Duration: Jun 20 2024Jun 21 2024

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference13th Joint Conference on Lexical and Computational Semantics, StarSEM 2024
Country/TerritoryMexico
CityMexico City
Period6/20/246/21/24

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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