chapter

Context representation in domain ontologies and its use for semantic integration of data

Author:

Guy PierraAuthors Info & Claims

Journal on data semantics X

January 2008

Pages 174 - 211

Published: 01 January 2008 Publication History

Abstract

The goal of this paper is to identify various aspects of context-awareness needed to facilitate semantics integration of data, and to discuss how this knowledge may be represented within ontologies. We first present a taxonomy of ontologies and we show how various kinds of ontologies may cooperate. Then, we compare ontologies and conceptual models. We claim that their main difference is the consensual nature of ontologies when conceptual models are specifically designed for one particular target system. Reaching consensus, in turn, needs specific models of which context dependency has been represented and minimized. We identify five principles for making ontologies less contextual than models and suitable for data integration and we show, as an example, how these principles have been implemented in the PLIB ontology model developed for industrial data integration. Finally, we suggest a road map for switching from conventional databases to ontology-based databases without waiting until standard ontologies are available in every domains.

References

[1]

Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The description logic handbook. Cambridge University Press, Cambridge (2003).

[2]

Bellatreche, L., Dung, N.X., Pierra, G., Dehainsala, H.: Contribution of ontologybased data modeling to automatic integration of electronic catalogues within engineering databases. Computers in Industry 57(8-9), 711-724 (2006).

Digital Library

[3]

Bernstein, P.A., Havely, A.Y., Pottinger, R.A.: A vision of managament of complex models. SIGMOD Record 29(4), 55-63 (2000).

Digital Library

[4]

Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L., Stuckenschmidt, H.: Contextualizing ontologies. Journal of Web Semantics 1(4), 325-343 (2004).

Digital Library

[5]

Calvanese, D., De Giacomo, G., Lenzerini, M., Nardi, D., Rosati, R.: A principled approach to data integration and reconciliation in data warehousing. In: DMDW 1999. Proceedings of the Intl. Workshop on Design and Management of DataWarehouses, Heidelberg, Germany (June 14-15, 1999).

[6]

Connolly, D., Stein, L., McGuinness, D.: Daml-ont initial release (2000), www.daml.org/2000/10/daml-ont.html

[7]

Dehainsala, H., Pierra, G., Bellatreche, L.: OntoDB: An ontology-based database for data intensive applications. In: Kotagiri, et al. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 497-508. Springer, Heidelberg (2007).

[8]

Elmagarmid, A., Rusinkiewicz, M.: Heterogeneous Autonomous Database Systems. Morgan Kaufmann, San Francisco (1999).

[9]

Everett, J.O., Bobrow, D.G., Stolle, R., Crouch, R.S., de Paiva, V., Condoravdi, C., van den Berg, M., Polanyi, L.: Making ontologies work for resolving redundancies across documents. Communication of ACM 45(2), 55-60 (2002).

Digital Library

[10]

Fankam, C., Aït-Ameur, Y., Pierra, G.: Exploitation of ontology languages for both persistence and reasoning purposes: Mapping PLIB, OWL and flight ontology models. In: WEBIST 2007. Proc. of Third International Conference on Web Information Systems and Technologies, pp. 254-262 (2007).

[11]

Fensel, D., van Harmelen, F., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F: Oil: an ontology infrastructure for the semantic web. IEEE Intelligent Systems 16(2), 38-45 (2001).

Digital Library

[12]

Goasdoué, F., Lattès, V., Rousset, M.C.: The use of carin language and algorithms for information integration: The picsel system. International Journal of Cooperative Information Systems (IJCIS) 9(4), 383-401 (2000).

[13]

Goh, C.H., Madnick, S.E., Siegel, M.: Context interchange: Overcoming the challenges of large-scale interoperable database systems in a dynamic environment. In: CIKM 1994. Proceedings of the Third International Conference on Information and Knowledge Management, pp. 337-346 (December 1994).

Digital Library

[14]

Goh, C.H., Bressan, S., Madnick, E., Siegel, M.D.: Context interchange: New features and formalisms for the intelligent integration of information. ACM Transactions on Information Systems 17(3), 270-293 (1999).

Digital Library

[15]

Gruber, T.: Toward principles for the design of ontologies used for knowledge sharing in formal ontology. In: Guarino, N., Poli, R. (eds.) Conceptual Analysis and Knowledge Representation, Kluwer Academic, Dordrecht (1993).

[16]

Guarino, N., Welty, C.A.: Evaluating ontological decisions with ontoclean. Communications of the ACM 45(2), 61-65 (2002).

Digital Library

[17]

Jean, S., Aït Ameur, Y., Pierra, G.: Querying ontology based databases using ontoql (an ontology query language). In: ODBASE, pp. 704-721 (2006).

Digital Library

[18]

Jean, S., Pierra, G., Ameur, Y.A.: Domain ontologies: A database-oriented analysis. In: Filipe, J., Cordeiro, J., Pedrosa, V. (eds.) WEBIST (Selected Papers). Lecture Notes in Business Information Processing, vol. 1, pp. 238-254. Springer, Heidelberg (2006).

[19]

Kashyap, V., Sheth, A.P.: Semantic and schematic similarities between database objects: A context-based approach. VLDB Journal 5(4), 276-304 (1996).

Digital Library

[20]

McGuinness, D.L., Harmelen, F.: OWL web ontology language overview. W3C Recommendation (February 10, 2004).

[21]

Mena, E., Kashyap, V., Illarramendi, A., Sheth, A.P.: Managing multiple information sources through ontologies: Relationship between vocabulary heterogeneity and loss of information. In: Proceedings of Third Workshop on Knowledge Representation Meets Databases (August 1996).

[22]

Minsky, M.: Matter, mind and models. International Federation of Information Processing Congress 1, 45-49 (1965).

[23]

Mitra, P., Wiederhold, G., Kersten, M.: A graph-oriented model for articulation of ontology interdependencies. In: Zaniolo, C., Grust, T., Scholl, M.H., Lockemann, P.C. (eds.) EDBT 2000. LNCS, vol. 1777, Springer, Heidelberg (2000).

[24]

Noy, N.F., McGuinness, D.L.: Ontology development: A guide to creating your first ontology. Technical report ksl-01-05 and stanford medical informatics technical report smi-2001-0880, stanford Knowledge Systems Laboratory (April 2001).

[25]

Patel-Schneider, P.F., Hayes, P., Horrocks, I.: OWL web ontology language semantics and abstract syntax. W3C Recommendation (February 2004).

[26]

Pierra, G.: An object oriented approach to ensure portability of cad standard parts libraries. In: Eurographics 1990. Proceedings of the European Computer Graphics Conference and Exhibition, pp. 205-214 (1990).

[27]

Pierra, G.: A multiple perspective object oriented model for engineering design. In: New Advances in Computer Aided Design & Computer Graphics, pp. 368-373. International Academic Publishers, Beijing (1993).

[28]

Pierra, G., Dehainsala, H., Aït-Ameur, Y., Bellatreche, L.: Base de données à base ontologique: principe et mise en oeuvre. Ingénierie des syst`emes d'information 10(2), 91-115 (2005).

[29]

Pierra, G., Sardet, E.: Proposal for a XML representation of the PLIB ontology model: Ontoml. Research Report RR 07-01, p. 188 (2007), http://www.lisi.ensma.fr/ftp/pub/documents/reports/2007/ 2007-LISI-2007-01.pdf

[30]

Schenck, D., Wilson, P.: Information modelling: The express way. Oxford University Press, Oxford (1994).

[31]

Sciore, E., Siegel, M., Rosenthal, A.: Using semantic values to facilitate interoperability among heterogeneous information systems. ACM Transactions on Database Systems 19(2), 254-290 (1994).

Digital Library

[32]

Stuckenschmidt, H., Vögele, T., Visser, U., Meyer, R.: Intelligent brokering of environmental information with the buster system. In: Proceedings of the 5th International Conference "Wirtschaftsinformatik", Physica-Verlag, pp. 15-20 (2001).

[33]

Wache, H., Vögele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Hübner, S.: Ontology-based integration of information - a survey of existing approaches. In: Proceedings of the International Workshop on Ontologies and Information Sharing, pp. 108-117 (August 2001).

[34]

Wiederhold, G.: Mediators in the architecture of future information systems. IEEE Computer 25(3), 38-49 (1992).

Digital Library

[35]

Ziegler, P., Dittrich, K.R.: User-specific semantic integration of heterogeneous data: The sirup approach. In: Bouzeghoub, M., Goble, C.A., Kashyap, V., Spaccapietra, S. (eds.) ICSNW 2004. LNCS, vol. 3226, pp. 44-64. Springer, Heidelberg (2004).

Cited By

Abdellaoui SNader FChalal R(2017)QDflowsJournal of Data and Information Quality10.1145/30641738:3-4(1-39)Online publication date: 30-Jun-2017
https://dl.acm.org/doi/10.1145/3064173
Méry DSawant RTarasyuk A(2015)Integrating Domain-Based Features into Event-BProceedings of the 5th International Conference on Model and Data Engineering - Volume 934410.1007/978-3-319-23781-7_8(89-102)Online publication date: 26-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-23781-7_8
Barkat O(2015)Extending Semantic Databases to Handle ContextProceedings of the 5th International Conference on Model and Data Engineering - Volume 934410.1007/978-3-319-23781-7_10(119-127)Online publication date: 26-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-23781-7_10
Show More Cited By

Index Terms

Context representation in domain ontologies and its use for semantic integration of data
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Data management systems
    1. Database design and models

Index terms have been assigned to the content through auto-classification.

Recommendations

Ontology alignment for semantic data integration through foundational ontologies
ER'12: Proceedings of the 2012 international conference on Advances in Conceptual Modeling

Ontology alignment is the process of finding corresponding entities with the same intended meaning in different ontologies. In scenarios where an ontology conceptually describes the contents of a data repository, this provides valuable information for ...
Semantic enrichment for medical ontologies

The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a ...
Domain Ontology Component-Based Semantic Information Integration
ETCS '09: Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 03

Research on architecture of domain ontology component-based information semantic representation and integration is studied. Domain ontology component, a "loosely coupled" approach in the use of ontology, is advocated. As a case study, a prototype for ...

Comments

Information & Contributors

Information

Published In

cover image Guide books

Journal on data semantics X

January 2008

263 pages

ISBN:3540776877

Editor:
Stefano Spaccapietra
Database Laboratory, EPFL, School of Computer and Communication Science, Lausanne, Switzerland

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2008

Qualifiers

Chapter

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
10
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Abdellaoui SNader FChalal R(2017)QDflowsJournal of Data and Information Quality10.1145/30641738:3-4(1-39)Online publication date: 30-Jun-2017
https://dl.acm.org/doi/10.1145/3064173
Méry DSawant RTarasyuk A(2015)Integrating Domain-Based Features into Event-BProceedings of the 5th International Conference on Model and Data Engineering - Volume 934410.1007/978-3-319-23781-7_8(89-102)Online publication date: 26-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-23781-7_8
Barkat O(2015)Extending Semantic Databases to Handle ContextProceedings of the 5th International Conference on Model and Data Engineering - Volume 934410.1007/978-3-319-23781-7_10(119-127)Online publication date: 26-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-23781-7_10
Boukhari IBellatreche LKhouri SWeippl EIndrawan-Santiago MSteinbauer MKotsis GKhalil I(2013)Efficient, Unified, and Intelligent User Requirement Collection and Analysis in Global EnterprisesProceedings of International Conference on Information Integration and Web-based Applications & Services10.1145/2539150.2539263(686-691)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1145/2539150.2539263
Bellatreche LCheikh SBreß SKerkad ABoukhorca ABoukhobza JDesai BLarriba-pey JBernardino J(2013)How to exploit the device diversity and database interaction to propose a generic cost model?Proceedings of the 17th International Database Engineering & Applications Symposium10.1145/2513591.2513660(142-147)Online publication date: 9-Oct-2013
https://dl.acm.org/doi/10.1145/2513591.2513660
Mbaiossoum BBellatreche LJean S(2013)Towards performance evaluation of semantic databases management systemsProceedings of the 29th British National conference on Big Data10.1007/978-3-642-39467-6_12(107-120)Online publication date: 8-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-39467-6_12
Talaei-Khoei ARay PParameshwaran NLewis L(2012)ReviewJournal of Network and Computer Applications10.1016/j.jnca.2011.06.01135:1(199-210)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1016/j.jnca.2011.06.011
Fankam C(2008)OntoDB2Proceedings of the 2008 EDBT Ph.D. workshop10.1145/1387150.1387154(21-27)Online publication date: 25-Mar-2008
https://dl.acm.org/doi/10.1145/1387150.1387154

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Chapter

Media

Figures

Other

Tables

View Table of Contents