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Collective entity resolution in relational data (2006)
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| Venue: | ACM Transactions on Knowledge Discovery from Data (TKDD |
| Citations: | 146 - 12 self |
BibTeX
@ARTICLE{Bhattacharya06collectiveentity,
author = {Indrajit Bhattacharya and Lise Getoor},
title = {Collective entity resolution in relational data},
journal = {ACM Transactions on Knowledge Discovery from Data (TKDD},
year = {2006},
volume = {1},
pages = {2007}
}
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Abstract
Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.
Keyphrases
collective entity resolution relational data underlying entity relational information entity resolution efficient implementation different entity multiple reference entity resolution accuracy attribute-based algorithm imprecise reference knowledge extraction additional relational information many database entity resolution performance domain entity multiple real-world database real-world entity data characteristic collective entity resolution algorithm data redundancy database reference attribute-based baseline detailed experiment entity resolution quality collective relational resolution different relational similarity measure query processing pairwise similarity novel relational clustering algorithm
