Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T01:15:44.177Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic ontology creation using adaptation

Published online by Cambridge University Press:  19 May 2009

Valerie Cross  and
Vishal Bathija
Valerie Cross
Affiliation:
Computer Science and Systems Analysis Department, Miami University, Oxford, Ohio, USA
Vishal Bathija
Affiliation:
Planetsoft Holdings, Burlington, Massachusetts, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Ontologies are an emerging means of knowledge representation to improve information organization and management, and they are becoming more prevalent in the domain of engineering design. The task of creating new ontologies manually is not only tedious and cumbersome but also time consuming and expensive. Research aimed at addressing these problems in creating ontologies has investigated methods of automating ontology reuse mainly by extracting smaller application ontologies from larger, more general purpose ontologies. Motivated by the wide variety of existing learning algorithms, this paper describes a new approach focused on the reuse of domain-specific ontologies. The approach integrates existing software tools for natural language processing with new algorithms for pruning concepts not relevant to the new domain and extending the pruned ontology by adding relevant concepts. The approach is assessed experimentally by automatically adapting a design rationale ontology for the software engineering domain to a new one for the related domain of engineering design. The experiment produced an ontology that exhibits comparable quality to previous attempts to automate ontology creation as measured by standard content performance metrics such as coverage, accuracy, precision, and recall. However, further analysis of the ontology suggests that the automated approach should be augmented with recommendations presented to a domain expert who monitors the pruning and extending processes in order to improve the structure of the ontology.

Keywords

Design RationaleNatural Language Processing ToolsOntology EvaluationOntology LearningOntology Reuse
Type
Regular Articles
Information
AI EDAM , Volume 24 , Issue 1: Design Computing and Cognition , February 2010 , pp. 127 - 141
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bathija, V. (2006). An adaptation methodolgy for reusing ontologies. Masters thesis. Miami University, Oxford, OH.Google Scholar
Bontas, E.P., Mochol, M., & Tolksdorf, R. (2005). Case studies on ontology reuse. Proc. 5th Int. Conf. Knowledge Management (I'Know '05), Graz, Austria.Google Scholar
Brank, J., Grobelnik, M., & Mladenic, D. (2005). A survey of ontology evaluation techniques. Proc. 8th Int. Multiconf. Information Society IS-2005.Google Scholar
Buitelaar, P., Cimiano, P., & Magnini, B., Eds. (2005). Ontology learning from text: methods, evaluation and applications. In Frontiers in Artificial Intelligence and Applications, Vol. 123. Amsterdam: IOS Press.Google Scholar
Buitelaar, P., Olejnik, D., & Sintek, M. (2004). Protégé plug-in for ontology extraction from text based on linguistic analysis. Proc. 1st European Semantic Web Symp., The Semantic Web: Research and Applications, pp. 31–44. Heraklion, Crete.CrossRefGoogle Scholar
Burge, J., & Brown, D.C. (2008). Software engineering using RATionale. Journal of Systems and Software 81(3), 395–413.CrossRefGoogle Scholar
Burge, J., Cross, V., Kiper, J., Maynard-Zhang, P., & Cornford, S. (2006). Enhanced design checking involving constraints, collaboration, and assumptions. Proc. Design Computing and Cognition ’06, pp. 655–674. Eindhoven, The Netherlands.Google Scholar
Burge, J.E. (2005). Software engineering using design RATionale, PhD dissertation. Worcester Polytechnic Institute.Google Scholar
Caralt, J.C. (2004). Ontology-driven information systems: pruning and refactoring of ontologies. Proc. 7th Int. Conf. Unified Modeling Language, Lisbon, Portugal.Google Scholar
Declerck, T. (2002). A set of tools for integrating linguistic and non-linguistic information. Proc. SAAKM Workshop, ECAI, Lyon.Google Scholar
Ding, Y., Lonsdale, D.W., Embley, D.W., Hepp, M., & Xu, L. (2007). Generating ontologies via language components and ontology reuse. Proc. 12th Int. Conf. Applications of Natural Language to Information Systems, NLDB 2007, pp. 131–142.CrossRefGoogle Scholar
Eris, O., Hansen, P.H.K., Mabogunje, A., & Leifer, L. (1999). Toward a pragmatic ontology for product development projects in small teams. Proc. Int. Conf. Engineering Design, pp. 1645–1950. Munich: Technische Universität München.Google Scholar
Fowler, D.W., Sleeman, D., Wills, G., Lyon, T., & Knott, D. (2004). The Designers’ Workbench: using ontologies and constraints for configuration. Proc. 24th SGAI Int. Conf. Innovative Techniques and Applications of AI, pp. 209–221.Google Scholar
Gómez-Pérez, A., & Manzano-Macho, D. (2005). An overview of methods and tools for ontology learning from texts. Knowledge Engineering Review 19, 187–212.CrossRefGoogle Scholar
Gruber, T.R. (1993). A translation approach to portable ontologies. Knowledge Acquisition 5, 199–220.CrossRefGoogle Scholar
Haase, P., van Harmelen, F., Huang, Z., Stuckenschmidt, H., & Sure, Y. (2005). A framework for handling inconsistencies in changing ontologies. Proc. Int. Semantic Web Conf., ISWC 2005, pp. 353–367.CrossRefGoogle Scholar
Jaccard, P. (1908). Nouvelles recherches sur la distribution florale. Bulletin de la Societe de Vaud des Sciences Naturelles 44, 223.Google Scholar
Japikse, R., Langdon, P.M., & Wallace, K.M. (2003). Structuring engineering design information using a model of how engineers' intuitively structure design information, Proc. 14th Int. Conf. Engineering Design, ICED ’03, pp. 433–434.Google Scholar
Jones, K.S. (1972). A statistical interpretation of terms specificity and its application retrieval. Journal of Documentation 28, 11–20.CrossRefGoogle Scholar
Kietz, J.U., Maedche, A., & Volz, R. (2000). A method for semi-automatic ontology acquisition from a corporate intranet. EKAW ’00 Workshop on Ontologies and Texts, CEUR Workshop Proc., pp. 51:4.1–4.14. Accessed at http://CEUR-WS.org/Vol-51/Google Scholar
Kitamura, Y., Kashiwase, M., Fuse, M., & Mizoguchi, R. (2004). Deployment of an ontological framework of functional design knowledge. Advanced Engineering Informatics 18(2), 115–127.CrossRefGoogle Scholar
Lee, J. (1997). Design rationale systems: understanding the issues. IEEE Expert 12(3), 78–85.CrossRefGoogle Scholar
Lin, J., Fox, M.S., & Bilgic, T.A. (1996). Requirement ontology for engineering design. Concurrent Engineering: Research and Applications 4(3), 279–291.Google Scholar
Lonsdale, D., Ding, Y., Embley, D.W., & Melby, A. (2002). Peppering knowledge sources with SALT; boosting conceptual content for ontology generation. Proc. AAAI Workshop on Semantic Web Meets Language Resources, Edmonton, Alberta, Canada.Google Scholar
Maedche, A. (2002). Ontology Learning for the Semantic Web. Boston: Kluwer Academic.CrossRefGoogle Scholar
Miller, G., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K.J. (1990). Introduction to Wordnet: an on-line lexical database. International Journal of Lexicography 3(4), 235–244.CrossRefGoogle Scholar
Navigli, R. (2002). Automatically extending, pruning and trimming general purpose ontologies. Proc. Int. Conf. Systems, Man and Cybernetics, pp. 631–635.CrossRefGoogle Scholar
Navigli, R., & Velardi, P. (2004). Learning domain ontologies from document warehouses and dedicated Web sites. Computational Linguistics 30(2), 151–179.CrossRefGoogle Scholar
Novacek, V., Laera, L., & Handschuh, S. (2007). Semi-automatic integration of learned ontologies into a collaborative framework. Proc. IWOD/European Semantic Web Conf., pp. 13–26, Innsbruck.Google Scholar
Noy, N., & Musen, M.A. (2004). Specifying ontology views by traversal. Proc. Int. Semantic Web Conf., pp. 713–725.CrossRefGoogle Scholar
O'Brien, P., & Abidi, S.S.R. (2006). Modeling intelligent ontology evolution using biological evolutionary processes. IEEE Int. Conf. Engineering of Intelligent Systems, Islamabad.Google Scholar
Ovchinnikova, E., & Kühnberger, K.-U. (2007). Automatic ontology extension: resolving inconsistencies. In Ontologies and Text Technology: Approaches to Extract Semantic Knowledge From Syntactic Information (Mönnich, U., & Kühnberger, K.-U., Eds.), Vol. 2007–1, pp. 93–98. Accessed at http://www.cogsci.uni-osnabrueck.de/cogsci/de/m.ikwPublications.php on March 6, 2009.Google Scholar
Sabou, M. (2005). Learning Web service ontologies: an automatic extraction method and its evaluation. In Ontology Learning and Population (Buitelaar, P., Cimiano, P., & Magnini, B., Eds.). Amsterdam: IOS Press.Google Scholar
Song, M.H., Lim, S.Y., Park, S.B., Kang, D.J., & Lee, S.J. (2005). Ontology-based automatic classification of Web pages. International Journal of Lateral Computing 1(1), 57–62.Google Scholar
Spyns, P., & Reinberger, M. (2005). Lexically evaluating ontology triples generated automatically from texts. Proc. 2nd European Semantic Web Conf., Semantic Web: Research and Applications, pp. 563–577, Heraklion, Crete, May 29–June 1.Google Scholar
Stuckenschmidt, H., & Klein, M. (2004). Structure-based partitioning of large concept hierarchies. Proc. 3rd Int. Semantic Web Conf., pp. 289–303.CrossRefGoogle Scholar
Volz, R., Studer, R., Maedche, A., & Lauser, B. (2003). Pruning-based identification of domain ontologies. Journal of Universal Computer Science 9, 520–529.Google Scholar
Zipf, G.K. (1949). Human Behaviors and the Principle of Least Effort. Cambridge, MA: Addison–Wesley.Google Scholar