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Conference Paper

Deep Answers for Naturally Asked Questions on the Web of Data

MPS-Authors
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Yahya,  Mohamed
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Berberich,  Klaus
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Elbassuoni,  Shady
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Ramanath,  Maya
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Yahya, M., Berberich, K., Elbassuoni, S., Ramanath, M., Tresp, V., & Weikum, G. (2012). Deep Answers for Naturally Asked Questions on the Web of Data. In A. Mille, F. Gandon, J. Misselis, M. Rabinovich, & S. Staab (Eds.), WWW'12 (pp. 445-449). New York, NY: ACM.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-5F6F-F
Abstract
We present DEANNA, a framework for natural language question answering over structured knowledge bases. Given a natural language question, DEANNA translates questions into a structured SPARQL query that can be evaluated over knowledge bases such as Yago, Dbpedia, Freebase, or other Linked Data sources. DEANNA analyzes questions and maps verbal phrases to relations and noun phrases to either individual entities or semantic classes. Importantly, it judiciously generates variables for target entities or classes to express joins between multiple triple patterns. We leverage the semantic type system for entities and use constraints in jointly mapping the constituents of the question to relations, classes, and entities. We demonstrate the capabilities and interface of DEANNA, which allows advanced users to influence the translation process and to see how the different components interact to produce the final result.