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ThinkMind // SEMAPRO 2015, The Ninth International Conference on Advances in Semantic Processing // View article semapro_2015_3_40_30057


Information Extraction from Unstructured Texts by Means of Syntactic Dependencies and Constituent Trees

Authors:
Raoul Schönhof
Axel Tenschert
Alexey Cheptsov

Keywords: Knowledge Representation; Legal Systems; Ontology; OWL; Big Data; Reasoning; DreamCloud Project

Abstract:
This paper presents a technology of automated knowledge extraction from unstructured text corpora by leveraging computer linguistic tools and cross-fertilizing them with the semantic ontologies techniques. In our approach, the quality of information (e.g., in form of OWL ontologies) that is derived by semantic analysis techniques from large domain-specific text corpora can be considerably improved by incorporating linguistic analysis tools that help gain a deeper insight into the grammatical structure of the analysed texts and thus allow the reasoning engines to cover a much wider set of rules and patterns, also positively impacting the performance. The novelty of our approach lies in a possibility of its application to the domains that require a very high quality of the knowledge extraction and analysis, such as reasoning for legacy data collections. We propose a system architecture for the implementation of our approach and illustrate its use on a practical use case for legislative and regulatory information analysis.

Pages: 70 to 75

Copyright: Copyright (c) IARIA, 2015

Publication date: July 19, 2015

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-420-6

Location: Nice, France

Dates: from July 19, 2015 to July 24, 2015

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