Efficient Knowledge Graph Validation via Cross-Graph Representation Learning

Yaqing Wang, Fenglong Ma, Jing Gao

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

Abstract

Recent advances in information extraction have motivated the automatic construction of huge Knowledge Graphs (KGs) by mining from large-scale text corpus. However, noisy facts are unavoidably introduced into KGs that could be caused by automatic extraction. To validate the correctness of facts (i.e., triplets) inside a KG, one possible approach is to map the triplets into vector representations by capturing the semantic meanings of facts. Although many representation learning approaches have been developed for knowledge graphs, these methods are not effective for validation. They usually assume that facts are correct, and thus may overfit noisy facts and fail to detect such facts. Towards effective KG validation, we propose to leverage an external human-curated KG as auxiliary information source to help detect the errors in a target KG. The external KG is built upon human-curated knowledge repositories and tends to have high precision. On the other hand, although the target KG built by information extraction from texts has low precision, it can cover new or domain-specific facts that are not in any human-curated repositories. To leverage external KG for validation, one intuitive approach is to find matched triplets between the target KG and the external KG. However, this approach can only apply to the small portion of triplets that are covered by both external and target KGs and is not useful for the validation of majority of the triplets. To tackle this challenging task, we propose a cross-graph representation learning framework, i.e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently. This is achieved by embedding triplets based on their semantic meanings, drawing cross-KG negative samples and estimating a confidence score for each triplet based on its degree of correctness. We evaluate the proposed framework on datasets across different domains. Experimental results show that the proposed framework achieves the best performance compared with the state-of-the-art methods on large-scale KGs.

Original languageEnglish (US)
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1595-1604
Number of pages10
ISBN (Electronic)9781450368599
DOIs
StatePublished - Oct 19 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: Oct 19 2020Oct 23 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
CountryIreland
CityVirtual, Online
Period10/19/2010/23/20

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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