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Open AccessFeature PaperArticle

Language-Agnostic Relation Extraction from Abstracts in Wikis

Data and Web Science Group, University of Mannheim, Mannheim 68131, Germany
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Information 2018, 9(4), 75; https://doi.org/10.3390/info9040075
Received: 5 February 2018 / Revised: 16 March 2018 / Accepted: 28 March 2018 / Published: 29 March 2018
(This article belongs to the Special Issue Towards the Multilingual Web of Data)
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. View Full-Text
Keywords: relation extraction; knowledge graphs; Wikipedia; DBpedia; DBkWik; Wiki farms relation extraction; knowledge graphs; Wikipedia; DBpedia; DBkWik; Wiki farms
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Heist, N.; Hertling, S.; Paulheim, H. Language-Agnostic Relation Extraction from Abstracts in Wikis. Information 2018, 9, 75.

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