Abstract
The rapid growth of the Semantic Web offers a wealth of semantic knowledge in the form of Linked Data and ontologies, which can be considered as large knowledge graphs of marked up Web data. However, much of this knowledge is only available in English, affecting effective information access in the multilingual Web. A particular challenge arises from the vocabulary gap resulting from the difference in the query and the data languages. In this paper, we present an approach to perform cross-lingual natural language queries on Linked Data. Our method includes three components: entity identification, linguistic analysis, and semantic relatedness. We use Cross-Lingual Explicit Semantic Analysis to overcome the language gap between the queries and data. The experimental results are evaluated against 50 German natural language queries. We show that an approach using a cross-lingual similarity and relatedness measure outperforms other systems that use automatic translation. We also discuss the queries that can be handled by our approach.
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Aggarwal, N., Polajnar, T., Buitelaar, P. (2013). Cross-Lingual Natural Language Querying over the Web of Data. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_13
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DOI: https://doi.org/10.1007/978-3-642-38824-8_13
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