An ontology-based Method for Discovering Specific Concepts from texts via Knowledge Completion

Abstract : Heterogeneity in user's queries and data sources can easily cause problems in perceiving sufficient information to form correct answers. In this paper, we address this issue when data sources are unstructured short texts describing only key characteristics of concerned individuals but when keywords in user's queries are customized concepts. To bridge the gap between texts and user's concepts, we propose an ontology-based approach, named Saupodoc, to discover formal definitions of specific concepts via population of property assertions. Property assertions are extracted from texts but the texts under our consideration are incomplete, i.e. information about the target concepts is missing. To solve this problem, we further propose a method to extract property assertions by exploiting LOD datasets to deal with missing and multiple values. Experiments have been carried out in two application domains, whose results show a clear benefit of Saupodoc over well-known classifiers.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01280576
Contributor : Chantal Reynaud <>
Submitted on : Monday, February 29, 2016 - 5:44:15 PM
Last modification on : Wednesday, November 14, 2018 - 12:52:02 PM

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  • HAL Id : hal-01280576, version 1

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Céline Alec, Chantal Reynaud-Delaître, Brigitte Safar. An ontology-based Method for Discovering Specific Concepts from texts via Knowledge Completion. KESA included in ALLDATA 2016: The Second International Conference on Big Data, Small Data, Linked Data and Open Data, Feb 2016, Lisbonne, Portugal. pp.96-101. ⟨hal-01280576⟩

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