Database Semantic Interoperability based on Information Flow Theory and Formal Concept Analysis

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Guanghui Yang 1,* Junkang Feng 1

1. Database and Semantic Web Research Group, School of Computing, University of the West of Scotland, UK

* Corresponding author.


Received: 25 Sep. 2011 / Revised: 13 Feb. 2012 / Accepted: 8 Mar. 2012 / Published: 8 Jul. 2012

Index Terms

Database Interoperability, Semantic, Information Flow, Formal Concept Analysis


As databases become widely used, there is a growing need to translate information between multiple databases. Semantic interoperability and integration has been a long standing challenge for the database community and has now become a prominent area of database research. In this paper, we aim to answer the question how semantic interoperability between two databases can be achieved by using Formal Concept Analysis (FCA for short) and Information Flow (IF for short) theories. For our purposes, firstly we discover knowledge from different databases by using FCA, and then align what is discovered by using IF and FCA. The development of FCA has led to some software systems such as TOSCANA and TUPLEWARE, which can be used as a tool for discovering knowledge in databases. A prototype based on the IF and FCA has been developed. Our method is tested and verified by using this prototype and TUPLEWARE.

Cite This Paper

Guanghui Yang, Junkang Feng, "Database Semantic Interoperability based on Information Flow Theory and Formal Concept Analysis", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.7, pp.33-42, 2012. DOI:10.5815/ijitcs.2012.07.05


[1]Lakshmanan L.V. S., F. Sadri and I. N. Subramanian, “Schema SQL – A Language for Interoperability in Relational Multi – database Systems,” In proceedings of the International Conference on Very Large Data Bases, 1996, pp.239-250.

[2]Batini C., M. Lenzerini and S.B. Navathe, “A Comparative Analysis of Methodologies for Database Schema Integration,” In ACM Computing Surveys, Vol.18, No.4, 1986, PP.323-364.

[3]McLoed D., and A. Si, “The Design and Experimental Evaluation of an Information Discovery Mechanism for Networks of Autonomous Database Systems,” In proceedings of the IEEE international Conference in data engineering, 1995, pp. 15-24

[4]Doan, A. and A. Y. Halevy (2005). Semantic-integration Research in the Database Community. AI Magazine, pages 83--94.

[5]Li, W.; Clifton, C.; and Liu, S. 2000. Database integration using neural network: implementation and experience. Knowledge and Information Systems 2(1):73{96.

[6]Doan A., and A. Y. Halevy, 2004. Semantic Integration Research in the Database Community: A Brief Survey. American Association for Artificial Intelligence

[7]Hernandez, M. and S. Stolfo (1995). The Merge/Purge Problem for Large Databases. In SIGMOD Conference, 127-138.

[8]Tejada, S., C. Knoblock, and S. Minton (2002). Learning Domain-independent String Transformation Weights for High Accuracy Object Identification. In Proc. Eighth SIGKDD International Conference (KDD-2002).

[9]Bilenko, M. and R. Mooney (2003). Adaptive Duplicate Detection using Learnable String Similarity Measures. In KDD Conference.

[10]Drew P., R. King, D. McLeod, M. Rusinkiewicz, and A. Silberschatz. Report of the workshop on semantic heterogeneity and interoperation in multidatabase systems. SIGMOD Record, pages 47-56, September 1993.

[11]Schorlemmer, M. and Y. Kalfoglou (2003). Using information flow theory to enable semantic interoperability, In Proc. Sixth Catalan Conference on Artificial Intelligence (CCIA '03), Palma de Mallorca, Spain.

[12]Barwise, J. and Seligman, J. (1997) Information Flow: the Logic of Distributed Systems, Cambridge University Press, Cambridge.

[13]Kent, R. E. (2002a.) The IFF Approach to Semantic Integration. Presentation at the Boeing Mini-Workshop on Semantic Integration, 7 November 2002.

[14]Kent, R. E. (2002b). Distributed Conceptual Structures. In: Proceedings of the 6th International Workshop on Relational Methods in Computer Science (RelMiCS 6). Lecture Notes in Computer Science 2561. Springer, Berlin.

[15]Kalfoglou, Y. and M. Schorlemmer (2003b). IF-Map: an ontology mapping method based on Information Flow theory, Journal on Data Semantics 1, LNCS 2800, pp.:98-127, Springer, ISBN: 3-540-20407-5.

[16]Schorlemmer, M. and Y. Kalfoglou (2010). The Informantion Folw Approach to Ontology-based Semantic Alignment. In Theory and Applications of Ontology: Computer Applications, R. Poli, M. Healy, and A. Kameas, Eds. Springer. 

[17]Wille, R. (1982). Restructuring lattice theory: an Approach based on Hierarchies of Concepts. In I. Rival (Ed.), Ordered sets. Reidel, Dordrecht-Boston, 445-470.

[18]Priss, U. (2005a). Formal Concept Analysis in Information Science. Annual Review of Information Science and Technology. Vol 40.

[19]Kalfoglou, Y., Dasmahapatra, S., & Chen-Burger, Y. (2004). FCA in Knowledge Technologies: Experiences and Opportunities. In P. Eklund (Ed.), Concept Lattices: Second International Conference on Formal Concept Analysis, LNCS 2961. Berlin: Springer, 252-260.

[20]Godin, R., Gecsei, J., & Pichet, C. (1989). Design of Browsing Interface for Information Retrieval. In N. J. Belkin, & C. J. van Rijsbergen (Eds.), Proc. SIGIR ’89, 32-39.

[21]Wille, R. (1992). Concept Lattices and Conceptual Knowledge Systems. Computers & Mathematics with Applications, 23, 493-515.

[22]Wille, R. (1997a). Conceptual Graphs and Formal Concept Analysis. In D.Lukose, H. Delugach, M. Keeler, L. Searle, & J. F. Sowa (Eds.), Conceptual Structures: Fulfilling Peirce’s Dream. Proc. ICCS’97. LNAI 1257. Berlin:Springer, 290-303.

[23]Prediger, S. and Wille, R. (1999). The Lattice of Concept Graphs of a Relationally Scaled Context. In W. Tepfenhart, & W. Cyre (Eds.), Conceptual Structures: Standards and Practices. Proceedings of the 7th International Conference.

[24]Steumme, G. R. Wille, U. Wille: Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods. In: J. M. Zytkow, M. Quafofou (eds.): 'Principles of Data Mining and Knowledge Discovery. Proc. of the 2nd European Symposium on PKDD'98, Lecture Notes in Artificial Intelligence 1510, Springer, Heidelberg 1998, 450-458.