Language-agnostic relation extraction from abstracts in Wikis

Heist, Nicolas ; Hertling, Sven ; Paulheim, Heiko

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DOI:	https://doi.org/10.3390/info9040075
URL:	http://www.mdpi.com/2078-2489/9/4/75
URN:	urn:nbn:de:bsz:180-madoc-443641
Dokumenttyp:	Zeitschriftenartikel
Erscheinungsjahr:	2018
Titel einer Zeitschrift oder einer Reihe:	Information
Band/Volume:	9
Heft/Issue:	4
Seitenbereich:	75
Ort der Veröffentlichung:	Basel
Verlag:	MDPI Publ.
ISSN:	2078-2489
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Data Science (Paulheim 2018-)
Bereits vorhandene Lizenz:	Creative Commons Namensnennung 4.0 International (CC BY 4.0)
Fachgebiet:	004 Informatik
Abstract:	Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extraction from text, using the data in the knowledge graph as training data, i.e., using distant supervision. While most existing approaches use language-specific methods (usually for English), we present a language-agnostic approach that exploits background knowledge from the graph instead of language-specific techniques and builds machine learning models only from language-independent features. We demonstrate the extraction of relations from Wikipedia abstracts, using the twelve largest language editions of Wikipedia. From those, we can extract 1.6 M new relations in DBpedia at a level of precision of 95%, using a RandomForest classifier trained only on language-independent features. We furthermore investigate the similarity of models for different languages and show an exemplary geographical breakdown of the information extracted. In a second series of experiments, we show how the approach can be transferred to DBkWik, a knowledge graph extracted from thousands of Wikis. We discuss the challenges and first results of extracting relations from a larger set of Wikis, using a less formalized knowledge graph.
Zusätzliche Informationen:	Online-Ressource