Natural Language Questions for the Web of Data

Yahya, Mohamed; Berberich, Klaus; Elbassuoni, Shady; Ramanath, Maya; Tresp, Volker; Weikum, Gerhard

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Natural Language Questions for the Web of Data

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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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引用

Yahya, M., Berberich, K., Elbassuoni, S., Ramanath, M., Tresp, V., & Weikum, G. (2012). Natural Language Questions for the Web of Data. In 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 379-390). Stroudsburg, PA: The Association for Computational Linguistics.

引用: https://hdl.handle.net/11858/00-001M-0000-0014-5914-5

要旨

The Linked Data initiative comprises structured databases in the Semantic-Web data model RDF. Exploring this heterogeneous data by structured query languages is tedious and error-prone even for skilled users. To ease the task, this paper presents a methodology for translating natural language questions into structured SPARQL queries over linked-data sources. Our method is based on an integer linear program to solve several disambiguation tasks jointly: the segmentation of questions into phrases; the mapping of phrases to semantic entities, classes, and relations; and the construction of SPARQL triple patterns. Our solution harnesses the rich type system provided by knowledge bases to constrain our semantic-coherence objective function. We present experiments on both the question translation and the resulting query answering.