An Approach for Automatic Ontology Enrichment from Texts
Abstract
The automatic ontology enrichment consists of automatic knowledge extraction from texts related to a domain of discourse in the aim to enrich automatically an initial ontology of the same domain. However, the passage, from a plain text to an enriched ontology requires a number of steps. In this paper, we present a three steps ontology enrichment approach. In the first step, we apply natural language processing techniques to obtain tagged sentences. The second step allows us to reduce each extracted sentence to an SVO (Subject, Verb, and Object) sentence, supposed to preserve main information carried by the original sentence(s) from which it is extracted. Finally, in the third step, we proceed to enrich an initial ontology built manually by adding extracted terms in the generated SVO as new concepts or instances of concepts and new relations. To validate our approach, we have used “Phytotherapy" domain because of the availability of related texts on the WWW and also because its usefulness for pharmaceutical industry. The first results obtained, after experiments on a set of different texts, testify the performance of the proposed approach.
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T. R. Gruber (1993). “A Translation Approach to Portable Ontologies”.Knowledge Acquisition, 5(2):199 – 220
V.T. Nguyen (2012). “Méthode d’extraction d’informations géographiques à des fins d’enrichissement d’une ontologie de domaine”
Drymonas,E., Zervanou,K. and Petrakis,E.G. (2010). “Unsupervised ontology acquisition from plain texts: the ontogain system. In: NLDB. Springer, Cardiff, United Kingdom,
A.C. Mazari , H. Aliane and Z. Alimazighi. (2012) “Automatic construction of ontology from Arabic texts” ICWIT, pp. 193-202
N. Astrakhantsev, D. Fedorenko, D. Turdakov (2014), “Automatic Enrichment of Informal Ontology by Analyzing Domain-Specific Texts Collection”. Materials of International Conference “Dialog”, vol. 13, no. 20, pp. 29–42
F. Amardeilh, P. Laublet, J. L. Minel(2005), “Document Annotation and Ontology Population from Lingusitic Extraction”, Proceedings of Third International Conference on Knowledge Capture.
Yarushkina, N.; Filippov, A.; Moshkin, V.; Egorov, Y(2018). Building a Domain Ontology in the Process of Linguistic Analysis of Text Resources. Preprints 2018, 2018020001 (doi: 10.20944/preprints201802.0001.v1)
P. Buitelaar, D.Olejnik, M. Sintek (2004),“ A Protege Plug-in for Ontology Extraction from Text Based on Linguistic Analysis, In Proceedings of the 1st European SemanticWeb Symposium (ESWS).
H. Knublauch, R.Fergerson,.NF, Noy, M.A Musen (2004), “ The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications”, McIlraith, S.A., Plexousakis, D., Harmelen, F. van (Eds.), The Semantic Web – ISWC, Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 229–243.
A. Maedche, E.Maedche, S. Staab, N.Stojanovic, Y. Sure, R.Studer(2001), “ Semantic portAL - The SEAL approach”, in: Spinning the Semantic Web. MIT Press, pp. 317–359.
Ivana Lukšová,(2013) “Ontology Enrichment Based on Unstructured Text Data ”, Master Thesis, Prague
C. Fellbaum (1992), “WordNet: An Electronic Lexical Database”, MIT Press.
P. Velardi, P. Fabriani, M.Missikoff (2001), “Using Text Processing Techniques to Automatically Enrich a Domain Ontology”, in Proceedings of the International Conference on Formal Ontology in Information Systems, FOIS ’01.ACM, New York, NY, USA, pp. 270–284. doi:10.1145/505168.505194
C. Faria, I. Serra, and R. Girardi(2014), “A domain- independent process for automatic ontology population from text, Elsevier , Journal of Science of Computer Programming vol.95 pp 26–43.
G. Petasis, V. Karkaletsis, G. Paliouras, A. Krithara and E. Zavitsanos(2011), Ontology Population and Enrichment: State of the Art, Berlin/ Heidelberg, pp 134-166, Springer.
Z. Sellami(2012), “Gestion dynamique d'ontologies à partir de textes par systèmes multi agents adaptatifs », thesis Paul Sabatier University.
A. Gomez Pérez, D. Manzano Macho(2005), “An overview of methods and tools for ontology learning from texts”, The Knowledge Engineering Review, Vol. 19:3, 187–212, Cambridge University Press doi:10.1017/S0269888905000251.
G.Petasis, V.Karkaletsis, G.Paliouras, A.Krithara, E.Zavitsanos(2011), “Ontology Population and Enrichment: State of the Art”, Knowledge-driven multimedia information extraction and ontology evolution, 134-166, Springer, Berlin, Heidelberg
D.Jurafsky, J.H. Martin (2018), “ The Representation of Sentence Meaning”, https://web.stanford.edu/~jurafsky/.
D. Jurafsky, J.H. Martin(2018) , “ Computing with Word Senses”, https://web.stanford.edu/~jurafsky/.
M.Shardlow(2014) , “A Survey of Automated Text Simplification “ (IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Natural Language Processing
M. Smith, A. Burton, T. Falkenberg(2014), “ World Health Organization : Traditional Medicine Strategy 2014-2023”,
N. Sheena, M.J Smitha, J. Shelbi(2016), “Automatic Extraction of Hypernym & Meronym Relations in English Sentences Using Dependency Parser”, 6th International Conference On Advances In Computing & Communications, ICACC 2016, Cochin, India
M. Khodak, A. Risteski, C. Fellbaum, S. Arora (2017), “Automated WordNet Construction Using Word Embeddings”, Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 12–23, Valencia, Spain
https://www.ef.com/ca/english-resources/english-grammar
N. Hernandez (2006). “Ontologies de domaine pour la modélisation du contexte en recherche d’information”. Thèse de Doctorat à l’Université Paul Sabatier France
F. Rousselot et P. Frath, (2002). “Terminologie et Intelligence Artificielle”. 12èmes rencontres linguistiques, Presses Universitaires de Caen.
A. Imsombut and J. Kajornrit (2017), “Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances” Journal of Reviews on Global Economics, 6, 375-379
M.N. Asim, M. Wasim, M.U.G Khan, W. Mahmoud and H. Abbasi (2018), “A survey of ontology learning techniques and applications” . Database, 1–24. doi: 10.1093/database/bay101
W. Wong (2011). “Ontology Learning from Text: A Look Back and into the Future”. Article in ACM Computing Surveys • doi: 10.1145/2333112.2333115
DOI: https://doi.org/10.31449/inf.v45i1.2586
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