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...
Weitere URL:	https://www.researchgate.net/publication/336993928...
Dokumenttyp:	Zeitschriftenartikel
Erscheinungsjahr:	2020
Titel einer Zeitschrift oder einer Reihe:	Knowledge and Information Systems
Band/Volume:	62
Heft/Issue:	6
Seitenbereich:	2169-2190
Ort der Veröffentlichung:	London
Verlag:	Springer
ISSN:	0219-1377 , 0219-3116
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Data Science (Paulheim 2018-)
Fachgebiet:	004 Informatik
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.