PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs

Ying Su, Jipeng Zhang, Yangqiu Song, Tong Zhang

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.
Anthology ID:
2024.starsem-1.29
Volume:
Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Danushka Bollegala, Vered Shwartz
Venue:
*SEM
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
360–371
Language:
URL:
https://aclanthology.org/2024.starsem-1.29
DOI:
Bibkey:
Cite (ACL):
Ying Su, Jipeng Zhang, Yangqiu Song, and Tong Zhang. 2024. PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs. In Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024), pages 360–371, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs (Su et al., *SEM 2024)
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PDF:
https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.starsem-1.29.pdf
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