Constructing Ontology-Based Petroleum Exploration Database for Knowledge Discovery

Article Preview

Abstract:

Volumes of petroleum resources data are archived in different companies. Complex geospatial heterogeneous data structures complicate the accessibility and presentation of data in petroleum industries. Objectives of the current research are to integrate the data from different sources and connect them intelligently and semantically. Petroleum exploration databases provide a central focus for scientific communities as well as providing useful resources to aide research. However, such resources require constant curation and often become outdated and discontinued. In this research work, we develop an ontology-based system for managing petroleum exploration data that addresses the issues of data integration and information sharing. Ontology approach ensures petroleum data validity that will support petroleum knowledge discovery process.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

975-980

Citation:

Online since:

January 2010

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2010 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] T.R. Gruber. A translation approach to portable ontology specifications. Knowledge Acquisition. Vol. 5 (1993), pp.199-220.

DOI: 10.1006/knac.1993.1008

Google Scholar

[2] M. Uschold and M. Grüninger. Ontologies: Principles, Methods and Applications. Knowledge Engineering Review. Vol. 11 (1996), pp.93-155.

Google Scholar

[3] R. Carnap. Empiricism, semantics, and ontology, in: The Philosophy of Science: An Historical Anthology, edited by T. McGrew, M. Alspector-Kelly and F. Allhoff, chapter, 5, John Wiley and Sons Publishers (2009).

Google Scholar

[4] B. Dunwoodie. What is an Ontology? And Why We Need Them. Proceedings of the 10th International Protégé Conference (2007).

Google Scholar

[5] R. Jasper and M. Uschold. A Framework for understanding and classifying ontology applications. Proceedings of the IJCAI-99 ontology workshop (1999), pp.1-20.

Google Scholar

[6] N.F. Noy and D.L. McGuinness. Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report (2001).

Google Scholar

[7] A.D. Diehl, J.A. Lee, R.H. Scheuermann and J.A. Blake. Ontology development for biological systems: immunology. Bioinformatics. Vol. 23 (2007), pp.913-915.

DOI: 10.1093/bioinformatics/btm029

Google Scholar

[8] S.L. Nimmagadda and H. Dreher. Mapping and Modeling of Oil and Gas Relational Data Objects for Warehouse Development and Efficient Data Mining. IEEE International Conference on Industrial Informatics (2006), pp.1201-1206.

DOI: 10.1109/indin.2006.275809

Google Scholar

[9] S. Bechhofer, F. van Harmelen, J. Hendler, I. Horrocks, and et al. OWL Web Ontology Language Reference. W3C Recommendation (2004).

Google Scholar

[10] B. Cuenca-Grau, I. Horrocks, B. Motik, B. Parsia, and et al. OWL 2: The next step for OWL. Journal of Web Semantics. Vol. 6 (2008), pp.309-322.

DOI: 10.1016/j.websem.2008.05.001

Google Scholar

[11] J.H. Gennari, M.A. Musen, R.W. Fergerson, and et al. The evolution of Protégé: an environment for knowledge-based systems development. International Journal of Human-Computer Studies. Vol. 58 (2003), pp.89-123.

DOI: 10.1016/s1071-5819(02)00127-1

Google Scholar

[12] The Protégé Ontology Editor and Knowledge Acquisition System. Information on http: /protege. stanford. edu.

Google Scholar

[13] I. Horrocks and P.F. Patel-Schneider. FaCT and DLP. Automated Reasoning with Analytic Tableaux and Related Methods (1998), pp.27-30.

DOI: 10.1007/3-540-69778-0_5

Google Scholar

[14] V. Haarslev, and R. Moller. Description of the RACER System and its Applications. In Proceedings of the International Workshop on Description Logics (2001), pp.1-3.

Google Scholar