Assessing the Quality of a Knowledge Graph via Link Prediction Tasks

Ricky Zhu, Alan Bundy, Fangrong Wang, Xue Li, Kuwabena Nuamah, Lei Xu, Stefano Mauceri, Jeff Z Pan

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

Abstract / Description of output

Knowledge Graph (KG) Construction is the prerequisite for all other KG research and applications. Researchers and engineers have proposed various approaches to build KGs for their use cases. However, how can we know whether our constructed KG is good or bad? Is it correct and complete? Is it consistent and robust? In this paper, we propose a method called LP-Measure to assess the quality of a KG via a link prediction tasks, without using a gold standard or other human labour. Though theoretically, the LP-Measure can only assess consistency and redundancy, instead of the more desirable correctness and completeness, empirical evidence shows that this measurement method can quantitatively distinguish the good KGs from the bad ones, even in terms of incorrectness and incompleteness. Compared with the most commonly used manual assessment, our LP-Measure is an automated evaluation, which saves time and human labour.
Original languageEnglish
Title of host publicationNLPIR '23: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval
PublisherAssociation for Computing Machinery
Pages1-10
Number of pages10
Publication statusAccepted/In press - 12 Nov 2023
Event7th International Conference on Natural Language Processing and Information Retrieval - Seoul, Korea, Republic of
Duration: 15 Dec 202317 Dec 2023
Conference number: 7
http://www.nlpir.net/index.html

Conference

Conference7th International Conference on Natural Language Processing and Information Retrieval
Abbreviated titleNLPIR 2023
Country/TerritoryKorea, Republic of
CitySeoul
Period15/12/2317/12/23
Internet address

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