Abstract
One of key problems in implementing a dynamic interface between human and agents is how to do semantic mapping from natural language questions to OWL. The paper views the task as a two-class classification problem. A pair of question variable and OWL element is a sample. Two classes of “Matched” and “Unmatched” explain two relations between the question variable and the OWL element in a given sample. Building appropriate semantic mapping is the same as classifying the sample to a “Matched” class by an effective machine learning method and a trained model. Two types of features of samples are selected. Syntactical features denote the syntactical structure of a given sample. Semantic features present multiple relations between the question variable and the OWL element in one sample. Preliminary experimental results show that the sum precision of the learning-based model is better than that of the constraints-based method.
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Gao, M., Liu, J., Zhong, N., Liu, C., Chen, F. (2007). A Learning-Based Model for Semantic Mapping from Natural Language Questions to OWL. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_84
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DOI: https://doi.org/10.1007/978-3-540-73451-2_84
Publisher Name: Springer, Berlin, Heidelberg
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