Jawad Makki
ABSTRACT
In this paper we propose an NLP-based Ontology Population approach for a Generic Structure instantiation from natural language texts, in the domain of Risk Management. The approach is semi-automatic and based on combined NLP techniques using domain expert intervention for control and validation. It relies on the predicative power of verbs in the instantiation process. It is not domain dependent since it heavily relies on linguistic knowledge.
We demonstrate the effectiveness of our method on the ontology of the PRIMA project (supported by the European community) and we populate this generic domain ontology via an available corpus. A first validation of the approach is done through an experiment with Chemical Fact Sheets from Environmental Protection Agency.
- Alquier A. M. & Tignol M. H., 2007. "Management de risques et intelligence ééconomique", Economica. ISBN: 2717852522.Google Scholar
- Ansoff H. I., 1990. Implanting Strategic Management, Practice Hall.Google Scholar
- T. Gruber. Towards principles for the design of ontologies used for knowledge sharing. Int. J. of Human and Computer Studies, 43:907--928, 1994. Google ScholarDigital Library
- R. Navigli and P. Velardi. Enriching a Formal Ontology with a Thesaurus: an Application in the Cultural Heritage Domain. In Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge - OLP 2006, pp. 1--9, Sydney, Australia, July 2006Google Scholar
- Hillson, D. 2005 "Describing probability: The limitations of natural language." Proceedings of EMEA, Edinburgh, UK.Google Scholar
- Huang, C. F. Risk 2007 "Analysis with Information Described in Natural Language ". In Computational Science, Proceedings of ICCS2007, Lecture Notes In Computer Science, Springer Verlag. Google ScholarDigital Library
- Liang T, Shih PK 2005 Empirical Textual Mining to Protein Entities Recognition from PubMed Corpus, Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain. Lecture Notes in Computer Science. Springer Verlag. Pp 56--66. Google ScholarDigital Library
- Navarro B., Martínez-Barco P. and M. Palomar, 2005. "Semantic Annotation of a Natural Language Corpus for Knowledge Extraction" 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain. Lecture Notes in Computer Science. Springer Verlag. Google ScholarDigital Library
- TreeTagger: a language independent part-of-speech tagger http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTaggerGoogle Scholar
- Miller, G. and McDonnell, J. S. 2003. "WordNet 2.0." A Lexical Database for English, Princeton University's Cognitive Science Laboratory. http://WordNet.princeton.eduGoogle Scholar
- S. Kim, H. Alani, W. Hall, P. Lewis, D. Millard, N. Shadbolt and M. Weal. Artequakt: Generating Tailored Biographies from Automatically Annotated Fragments from the Web. In Proceedings of Workshop on Semantic Authoring, Annotation & Knowledge Markup (SAAKM'02), the 15th European Conference on Artificial Intelligence, (ECAI'02), pp. 1--6, Lyon, France 2002.Google Scholar
- Alani H., Sanghee K., Millard E. D., Weal J. M., Lewis P. H., Hall W., and Shadbolt N., Automatic Extraction of Knowledge from Web Documents, In: Proceeding of (HLT03), 2003.Google Scholar
- Alani H., Sanghee K., Millard E. D., Weal J. M., Lewis P. H., Hall W., and Shadbolt N., Web based Knowledge Extraction and Consolidation for Automatic Ontology Instantiation, In: Proceedings of the Workshop on Knowledge Markup and Semantic Annotation at the Second International Conference on Knowledge Capture (K-CAP 2003), Florida, USA, 2003.Google Scholar
- H. Alani, S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis and N. R. Shadbolt (2003), "Automatic Ontology-Based Knowledge Extraction from Web Documents", IEEE Intelligent Systems, 18(1), pp. 14--21. Google ScholarDigital Library
- F. M. Suchanek, G. Ifrim and G, Weikum. LEILA: Learning to Extract Information by Linguistic Analysis. In Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge - OLP 2006, pp. 18--25, Sydney, Australia, July 2006.Google Scholar
- P. Buitelaar, P. Cimiano, S. Racioppa and M. Siegel (2006), "Ontology-based Information Extraction with SOBA", In Proceedings of the International Conference on Language Resources and Evaluation, pp. 2321--2324. ELRA, May 2006.Google Scholar
- M. Shamsfard, and A. Abdollahzadeh Barforoush. The state of the art in ontology learning: a framework for comparison. The Knowledge Engineering Review (2003), 18: 293--316 doi:10.1017/S0269888903000687 Google ScholarDigital Library
- Cunningham, H. and Bontcheva, K. Knowledge Management and Human Language: Crossing the Chasm. Journal of Knowledge Management, 9(5), 2005Google ScholarCross Ref
Index Terms
- An NLP-based ontology population for a risk management generic structure
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