@inproceedings{18350b2e7d724b17a09fbf8cda4e1371,
title = "A learning-based model for semantic mapping from natural language questions to OWL",
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.",
author = "Mingxia Gao and Jiming LIU and Ning Zhong and Chunnian Liu and Furong Chen",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Conference on Rough Sets and Intelligent Systems Paradigms, RSEISP 2007 ; Conference date: 28-06-2007 Through 30-06-2007",
year = "2007",
doi = "10.1007/978-3-540-73451-2_84",
language = "English",
isbn = "9783540734505",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "803--812",
booktitle = "Rough Sets and Intelligent Systems Paradigms - International Conference, RSEISP 2007, Proceedings",
address = "Germany",
}