Question Answering Biographic Information and Social Network Powered by the Semantic Web

Peter Adolphs, Xiwen Cheng, Tina Klüwer, Hans Uszkoreit, Feiyu Xu

In: Nicoletta Calzolari , Khalid Choukri , Bente Maegaard , Joseph Mariani , Jan Odjik , Stelios Piperidis , Mike Rosner , Daniel Tapias (Hrsg.). Proceedings of the 7th International Conference on Language Resources and Evaluation. International Conference on Language Resources and Evaluation (LREC-2010) 7th May 19-21 Valletta Malta ISBN 2-9517408-6-7 European Language Resources Association (ELRA) 5/2010.


After several years of development, the vision of the Semantic Web is gradually becoming reality. Large data repositories have been created and offer semantic information in a machine-processable form for various domains. Semantic Web data can be published on the Web, gathered automatically, and reasoned about. All these developments open interesting perspectives for building a new class of domain-specific, broad-coverage information systems that overcome a long-standing bottleneck of AI systems, the notoriously incomplete knowledge base. We present a system that shows how the wealth of information in the Semantic Web can be interfaced with humans once again, using natural language for querying and answering rather than technical formalisms. Whereas current Question Answering systems typically select snippets from Web documents retrieved by a search engine, we utilize Semantic Web data, which allows us to provide natural-language answers that are tailored to the current dialog context. Furthermore, we show how to use natural language processing technologies to acquire new data and enrich existing data in a Semantic Web framework. Our system has acquired a rich biographic data resource by combining existing Semantic Web resources, which are discovered from semi-structured textual data in Web pages, with information extracted from free natural language texts.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence