Bottom-up Discovery of Context-aware Quality Constraints for Heterogeneous Knowledge Graphs

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

As knowledge graphs are getting increasingly adopted, the question of how to maintain the validity and accuracy of our knowledge becomes ever more relevant. We introduce context-aware constraints as a means to help preserve knowledge
integrity. Context-aware constraints offer a more fine-grained control of the domain onto which we impose restrictions. We also introduce a bottom-up anytime algorithm to discover context-aware constraint directly from heterogeneous knowledge graphs---graphs made up from entities and literals of various (data) types which are linked using various relations. Our method is embarrassingly parallel and can exploit prior knowledge in the form of schemas to reduce computation time. We demonstrate our method on three different datasets and evaluate its effectiveness by letting experts on knowledge validation and management assess candidate constraints in a real-world knowledge validation use case. Our results show that overall, context-aware constraints are to an extent useful for knowledge validation tasks, and that the majority of the generated constraints are well balanced with respect to complexity.
Original languageEnglish
Title of host publicationProceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Subtitle of host publicationVolume 1: KDIR
PublisherSciTePress
Pages81-92
Number of pages11
Volume1
ISBN (Electronic)9789897584749
DOIs
Publication statusPublished - Nov 2020
Event12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2020 -
Duration: 2 Nov 20204 Nov 2020

Publication series

NameProceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Conference

Conference12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2020
Abbreviated titleIC3K.KDIR
Period2/11/204/11/20

Keywords

  • Knowledge Graph
  • Quality Control
  • Frequent pattern mining
  • asset management
  • Rule Learning

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