Network metrics for assessing the quality of entity resolution between multiple datasets

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Matching entities between datasets is a crucial step for combining multiple datasets on the semantic web. A rich literature exists on different approaches to this entity resolution problem. However, much less work has been done on how to assess the quality of such entity links once they have been generated. Evaluation methods for link quality are typically limited to either comparison with a ground truth dataset (which is often not available), manual work (which is cumbersome and prone to error), or crowd sourcing (which is not always feasible, especially if expert knowledge is required). Furthermore, the problem of link evaluation is greatly exacerbated for links between more than two datasets, because the number of possible links grows rapidly with the number of datasets. In this paper, we propose a method to estimate the quality of entity links between multiple datasets. We exploit the fact that the links between entities from multiple datasets form a network, and we show how simple metrics on this network can reliably predict their quality. We verify our results in a large experimental study using six datasets from the domain of science, technology and innovation studies, for which we created a gold standard. This gold standard, available online, is an additional contribution of this paper. In addition, we evaluate our metric on a recently published gold standard to confirm our findings.

Original languageEnglish
Title of host publicationKnowledge Engineering and Knowledge Management
Subtitle of host publication21st International Conference, EKAW 2018, Nancy, France, November 12-16, Proceedings
EditorsAmedeo Napoli, Chiara Ghidini, Yannick Toussaint, Catherine Faron Zucker
Place of PublicationBasel
PublisherSpringer Nature Switzerland AG
Number of pages16
ISBN (Electronic)9783030036676
ISBN (Print)9783030036669
Publication statusPublished - 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Network metrics
  • Data Integration
  • Entity resolution
  • Data integration

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