DBkWik: extracting and integrating knowledge from thousands of Wikis

DOI:	https://doi.org/10.1007/s10115-019-01415-5
URL:	http://link.springer.com/article/10.1007/s10115-01...
Additional URL:	https://www.researchgate.net/publication/336993928...
Document Type:	Article
Year of publication:	2020
The title of a journal, publication series:	Knowledge and Information Systems
Volume:	62
Issue number:	6
Page range:	2169-2190
Place of publication:	London
Publishing house:	Springer
ISSN:	0219-1377 , 0219-3116
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
Subject:	004 Computer science, internet
Abstract:	Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain Wikis for specific topics, which are often complementary to the information contained in Wikipedia, and thus DBpedia and YAGO. Extracting these Wikis with the DBpedia extraction framework is possible, but results in many isolated knowledge graphs. In this paper, we show how to create one consolidated knowledge graph, called DBkWik, from thousands of Wikis. We perform entity resolution and schema matching, and show that the resulting large-scale knowledge graph is complementary to DBpedia. Furthermore, we discuss the potential use of DBkWik as a benchmark for knowledge graph matching.