Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next wave of web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This survey is presented in two parts. The first part reviews the state-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as well as the problems facing such research. The second complementary survey is dedicated to ontology mapping and ontology ‘evolving’. Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impediment to ontology learning and applicability. Further research is encouraged to find appropriate and efficient ways to detect or identify relations through semi-automatic and automatic means.
Get full access to this article
View all access options for this article.
References
1.
[1] T.R. Gruber , A translation approach to portable ontology specifications , Knowledge Acquisition5 (1993) 199-220 .
2.
[2] C.H. Hwang , Incompletely and imprecisely speaking: using dynamic ontologies for representing and retrieving information . In: Proceedings of the 6th International Workshop on Knowledge Representation meets Databases (KRDB’99), Linköping, Sweden, 29-30 July 1999.
3.
[3] D. Fensel , J. Hendler , H. Lieberman and W. Wahlster (eds), Semantic WebTechnolog y ( MIT Press, Boston, MA , 2002).
4.
[4] J.-U. Kietz , A. Maedche and R. Volz , Extracting a domain-specific ontology Learning from a corporate intranet . In: Second ‘Learning Language In Logic’ LLL Workshop, co-located with the International Conference in Grammere Inference (ICGI’2000) and Conference on Natural Language Learning (CoNLL’2000), Lisbon, Portugal, 13-14 September 2000.
5.
[5] T. Berners-Lee , J. Hendler and O. Lassila , The Semantic Web . Scientific AmericanMay (2001) 216-218 .
6.
[6] D. Fensel , B. Omelayenko , Y. Ding , E. Schulten , G. Botquin , M. Brown and A. Flett , Product data integration in B2B e-commerce , IEEE Intelligent Systems16(4) (2001) 54-59 .
7.
[7] T. Berners-Lee and M. Fischetti , Weaving the Web ( Harper, San Francisco, CA , 1999).
8.
[8] D. Fensel , Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce ( Springer, Berlin , 2001).
9.
[9] M. Uschold , Creating, integrating and maintaining local and global ontologies . In: Proceedings of the First Workshop on Ontology Learning (OL-2000) in conjunction with the 14th European Conference on Artificial Intelligence (ECAI 2000), August 2001, Berlin.
10.
[10] M. Fernandez-Lopez , Overview of methodologies for building ontologies . In: Proceedings of IJCAI-99 Workshop on Ontologies and Problem-Solving Methods: Lessons Learned and Future Trends, in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, August 1999.
11.
[11] J. McCarthy , Notes on formalizing context . In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (AAAI, 1993).
12.
[12] N. Guarino , Some ontological principles for designing upper level lexical resources . In: Proceedings of the First International Conference on Lexical Resources and Evaluation, Granada, 28-30 May 1998.
13.
[13] N.B. Cocchiarella , Formal ontology. In: H. Burkhardt and B. Smith (eds), Handbook of Metaphysics and Ontology ( Philosophia, Munich , 1991), pp. 640-647.
14.
[14] M. Uschold and M. Gruninger , Ontologies: principles, methods, and applications , Knowledge Engineering Review11(2) (1996) 93-155 .
15.
[15] A. Gomez-Perez , N. Juristo and J. Pazos , Evaluation and assessment of knowledge sharing technology. In: N.J. Mars (ed.), Towards very Large Knowledge Bases - Knowledge Building and Knowledge Sharing ( IOS Press: Amsterdam , 1995), pp. 289-296.
16.
[16] J.R. Reich , Ontological design patterns for the integration of molecular biological information . In: Proceedings of the German Conference on Bioinformatics GCB’99, 4-6 October, Hannover, 1999, pp. 156-166 .
17.
[17] G.A. Miller , WORDNET: a lexical database for English . Communications of ACM (11) (1995) 39-41 .
18.
[18] E. Riloff and J. Shepherd , A corpus-based approach for building semantic lexicons . In: Proceedings of International Symposium on Cooperative Database Systems for Advanced Applications, 1999.
19.
[19] J. Jannink and G. Wiederhold , Ontology maintenance with an algebraic methodology: a case study . In: Proceedings of AAAI Workshop on Ontology Management, July 1999.
20.
[20] J. Jannink , Thesaurus entry extraction from an on-line dictionary . In: Proceedings of Fusion ‘99, Sunnyvale, CA, July 1999.
21.
[21] L. Page and S. Brin , The anatomy of a large-scale hyper-textual web search engine . In: Proceedings of the 7th Annual World Wide Web Conference, 1998.
22.
[22] S. Brin , Extracting patterns and relations from the world wide web . In: WebDB Workshop at EDBT ‘98, 1998.
23.
[23] A. Maedche and S. Staab , Discovering conceptual relations from text . In: ECAI 2000. Proceedings of the 14th European Conference on Artificial Intelligence (IOS Press, Amsterdam, 2000).
24.
[24] A. Maedche and S. Staab , Mining ontologies from text . In: R. Dieng and O. Corby (eds), EKAW-2000 - 12th International Conference on Knowledge Engineering and Knowledge Management, Juan-les-Pins, France, 2-6 October (LNAI, Springer, Berlin, 2000).
25.
[25] G. Neumann , R. Backofen , J. Baur , M. Becker and C. Braun , An information extraction core system for real world German text processing . In: ANLP’97 - Proceedings of the Conference on Applied Natural Language Processing, Washington, DC, pp. 208-215 .
26.
[26] R. Srikant and R. Agrawal , Mining generalized association rules . In: Proceedings of VLDB’951995, pp. 407-419 .
27.
[27] A. Wagner , Enriching a lexical semantic net with selectional perferences by means of statistical corpus analysis . In: Proceedings of the First Workshop on Ontology Learning (OL-2000) in conjunction with the 14th European Conference on Artificial Intelligence (ECAI 2000), Berlin, August 2000.
28.
[28] N. Abe and H. Li , Learning word association norms using tree cut pair models . In: Proceedings of 13th International Conference on Machine Learning, 1996.
29.
[29] G. Chalendar and B. Grau , SVETLAN: a system to classify nouns in context . In: Proceedings of the First Workshop on Ontology Learning (OL-2000) in conjunction with the 14th European Conference on Artificial Intelligence (ECAI 2000), Berlin, August 2000.
30.
[30] G. Bisson , C. Nedellec and D. Canamero , Designing clustering methods for ontology building: the Mo’K workbench . In: Proceedings of the First Workshop on Ontology Learning (OL-2000) in conjunction with the 14th European Conference on Artificial Intelligence (ECAI 2000), Berlin, August 2000.
31.
[31] D. Faure and T. Poibeau , First experiments of using semantic knowledge learned by ASIUM for information extraction task using INTEX . In: Proceedings of the First Workshop on Ontology Learning (OL-2000) in conjunction with the 14th European Conference on Artificial Intelligence (ECAI 2000), Berlin, August 2000.
32.
[32] P. Constant , Reducing the Complexity of Encoding Rule-based Grammars. December 1996.
33.
[33] A. Todirascu , F. Beuvron , D. Galea and F. Rousselot , Using description logics for ontology extraction . In: Proceedings of the First Workshop on Ontology Learning (OL-2000) in conjunction with the 14th European Conference on Artificial Intelligence (ECAI 2000), Berlin, August 2000.
34.
[34] C.A. Thompson and R.J. Mooney , Semantic Lexicon Acquisition for Learning Parsers, Unpublished Technical Note, January 1997.
35.
[35] N. Lavrac and S. Dzeroski (eds), Inductive Logic Programming: Techniques and Applications ( Ellis Horwood, Chichester , 1994).
36.
[36] B. Kwasnik , The role of classification in knowledge representation and discovery . Library Trends48 (1999) 22-47 .
37.
[37] P. Weinstein , Ontology-based metadata: transforms the MARC legacy . In: F. Akscyn and F.M. Shipman (eds), Digital Libraries 98, Third ACM Conference on Digital Libraries (New York: ACM Press, 1998), 254-263 .
38.
[38] J. Qin and S. Paling , Converting a controlled vocabulary into an ontology: the case of GEM . Information Research6(2) (2000). Available at: http:%5CInformationR.net/ir/6-2/infres62.html
39.
[39] S. Borgo , N. Guarino , C. Masolo and G. Vetere , Using a large linguistic ontology for internet-based retrieval of object-oriented components . In: Proceedings of 1997 Conference on Software Engineering and Knowledge Engineering, Madrid (Knowledge Systems Institute, Snokie, IL, 1997).
40.
[40] T. Yamaguchi , Constructing domain ontologies based on concept drift analysis . In: Proceedings of IJCAI-99 Workshop on Ontologies and Problem-Solving Methods: Lessons Learned and Future Trends, in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, August 1999.
41.
[41] V. Kashyap , Design and creation of ontologies for environmental information retrieval . In: Proceedings of Twelfth Workshop on Knowledge Acquisition, Modeling and Management, Alberta, October, 1999.
