Learning expressive linkage rules from sparse data

Petrovski, Petar ; Bizer, Christian

DOI: https://doi.org/10.3233/SW-190356
URL: https://content.iospress.com/articles/semantic-web...
Additional URL: https://www.researchgate.net/publication/333441601...
Document Type: Article
Year of publication: 2020
The title of a journal, publication series: Semantic Web
Volume: 11
Issue number: 3
Page range: 549-567
Place of publication: Amsterdam
Publishing house: IOS Press
ISSN: 1570-0844 , 2210-4968
Publication language: English
Institution: School of Business Informatics and Mathematics > Information Systems V: Web-based Systems (Bizer 2012-)
Subject: 004 Computer science, internet
Keywords (English): Entity resolution , sparse data , linkage rules , genetic programming, link discovery
Abstract: A central problem in the context of the Web of Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. There exists a large body of research on entity resolution. Interestingly, most of the existing research focuses on entity resolution on dense data, meaning data that does not contain too many missing values. This paper sets a different focus and explores learning expressive linkage rules from as well as applying these rules to sparse data, i.e. data exhibiting a large amount of missing values. Sparse data is a common challenge in application domains such as e-commerce, online hotel booking, or online recruiting. We propose and compare three entity resolution methods that employ genetic programming to learn expressive linkage rules from sparse data. First, we introduce the GenLinkGL algorithm which learns groups of matching rules and applies specific rules out of these groups depending on which values are missing from a pair of records. Next, we propose GenLinkSA, which employs selective aggregation operators within rules. These operators exclude misleading similarity scores (which result from missing values) from the aggregations, but on the other hand also penalize the uncertainty that results from missing values. Finally, we introduce GenLinkComb, an algorithm which combines the central ideas of the previous two into one integrated method. We evaluate all methods using six benchmark datasets: three of them are e-commerce product datasets, the other datasets describe restaurants, movies, and drugs. We show improvements of up to 16% F-measure compared to handwritten rules, on average 12% F-measure improvement compared to the original GenLink algorithm, 15% compared to EAGLE, 8% compared to FEBRL, and 5% compared to CoSum-P.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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