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Two is bigger (and better) than one: the wikipedia bitaxonomy project.
- In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics,
, 2014
"... Abstract We present WiBi, an approach to the automatic creation of a bitaxonomy for Wikipedia, that is, an integrated taxonomy of Wikipage pages and categories. We leverage the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments sho ..."
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Abstract We present WiBi, an approach to the automatic creation of a bitaxonomy for Wikipedia, that is, an integrated taxonomy of Wikipage pages and categories. We leverage the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments show higher quality and coverage than state-of-the-art resources like DBpedia, YAGO, MENTA, WikiNet and WikiTaxonomy. WiBi is available at http://wibitaxonomy.org.
People on Drugs: Credibility of User Statements in Health Communities
"... Online health communities are a valuable source of infor-mation for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for auto-matically establishing the credibility of user-generated med-ical statem ..."
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Online health communities are a valuable source of infor-mation for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for auto-matically establishing the credibility of user-generated med-ical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from ex-pert sources. To this end we introduce a probabilistic graphi-cal model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs—this being one of the problems where large scale non-expert data has the po-tential to complement expert medical knowledge. We show that our method can reliably extract side-effects and fil-ter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.
DOI 10.1186/s12859-015-0549-5 RESEARCH ARTICLE Open Access
"... KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences ..."
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KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences
