Affiliations: [a] Department of Computer Science, Vrije Universiteit Amsterdam, The Netherlands. E-mails: [email protected], [email protected] | [b] Department of Organization Sciences, Vrije Universiteit Amsterdam, The Netherlands. E-mail: [email protected]
Note: [1] This is an extended version, by invitation, of a paper accepted at the 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018) (In Knowledge Engineering and Knowledge Management (2018) 147–162 Springer).
Abstract: 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.
Keywords: Entity resolution, data integration, network metrics
DOI: 10.3233/SW-200410
Journal: Semantic Web, vol. 12, no. 1, pp. 21-40, 2021
