Converting Approach from Database Relational Schema into Horn Clauses Predicate Logic

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The aim of this study is to build a transformation model capable to convert a database relational schema into a set of horn clauses predicate logic. Today, databases support trillion and trillion of data megabytes. They provide the main data sources for most of the decision-making systems. Now, organizations give more importance towards knowledge rather than information. But, knowledge cannot be extracted simply from databases. Usually, many complex procedures and techniques of data mining are required. Unlike databases, knowledge bases store knowledge (structured and unstructured information) in a computer-readable form. Moreover, the inference engine, supported by most of the knowledge bases, extracts new knowledge by simply implementing new rules. Based on Model Driven Architecture (MDA) approach and especially the models’ transformation, this paper presents a set of mappings which transform a database (metadata and previous data) to knowledgebase expressed into facts and rules. A prototype transformation system is designed and implemented based on UML (Unified Modeling Language) class diagram and ATL (Atlas Transformation Language) which is the tool used for the model transformation. The prototype shows that the output of the transformation process can be operated directly by a logic programming language such as Prolog.

     

     


  • Keywords


    Model Transformation; Model Driven Architecture; Unified Modeling Language; Meta-Model; Atlas Transformation Language.

  • References


      [1] Sagiroglu, S. and D. Sinanc, Big data: A review, 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, 2013, pp. 42-47, (2013). https://doi.org/10.1109/CTS.2013.6567202.

      [2] Mukerji, J. and J. Miller, Technical Guide to Model Driven Architecture: The MDA Guide v1.0.1. Technical report (2003).

      [3] Truyen, F., The Fast Guide to Model Driven Architecture, The Basics of Model Driven Architecture, WHITEPAPER, Cephas Consulting Corp. Architecture Oriented Services, January (2006).

      [4] Bezivin, J. and O. Gerbé, Towards a precise definition of the omg/mda framework, ASE’01, Automated Software Engineering, San Diego, USA, Nov 26 – 29, (2001).

      [5] Budinsky, F., D. Steinberg, R. Ellersick, T. Grose and E. Merks, Eclipse Modeling Framework: A Developer's Guide, Addison-Wesley Professional, (2004).

      [6] Dinh, T. L. A., O. Gerbé and H. Sahraoui, Un méta-métamodèle pour la gestion de modèles. In IDM 06 Actes des 2èmes Journées sur l’Ingénierie Dirigée par les Modèles, Lille, France, (2006).

      [7] Thi-Lan-anh, D., G. Olivier and S. Houari, Gestion de modèles : définitions, besoins et revue de littérature. In Premières Journées sur l’Ingénierie Dirigée par les Modèles, pages 1–15, Paris, France, 30 Juin- 1 Juillet (2005).

      [8] Czarnecki, K. and S. Helsen, Feature-based survey of model transformation approaches. IBM Systems Journal, 45(3):621–645 (2006). https://doi.org/10.1147/sj.453.0621.

      [9] QVT, Meta Object Facility (MOF) 2.0 Query/View/Transformation Specification Version 1.2, Needham, MA, formal/2015-02-01 edition, February (2015).

      [10] Jouault, F., F. Allilaire, J. Bézivin and L. Kurtev, ATL: A model transformation tool. Science of Computer Programming, 72:31 – 39, Special Issue on Second issue of experimental software and toolkits (EST) (2008). https://doi.org/10.1016/j.scico.2007.08.002.

      [11] Steinberg, D., F. Budinsky, M. Paternostro, E. Merks, R. Ellersick and T. J. Rose, EMF Eclipse Modeling Framework. The Eclipse Series. Boston, MA, 2nd edition (2009).

      [12] Arenas M., A. Bertails, E. Prud’hommeaux, J. Sequeda, A Direct Mapping of Relational Data to RDF, W3C Recommendation 2012, http://www.w3.org/TR/rdb-direct-mapping, (2012).

      [13] Pankowski, T., Using Data-to-Knowledge Exchange for Transforming Relational Databases to Knowledge Bases. In: Bikakis A., Giurca A. (eds) Rules on the Web: Research and Applications. RuleML 2012, LNCS,vol 7438, Springer, Berlin, Heidelberg, (2012). https://doi.org/10.1007/978-3-642-32689-9_21.

      [14] Dadjoo, M. and E. Kheirkhah, An Approach for Transforming of Relational Databases to OWL Ontology, International Journal of Web & Semantic Technology (IJWesT) Vol.6, No.1, January (2015). https://doi.org/10.5121/ijwest.2015.6102.

      [15] Gherabi, N., K. Addakiri and M. Bahaj, Mapping relational database into OWL Structure with data semantic Preservation, International Journal of Computer Science and Information Security, Vol. 10, No. 1, January (2012).

      [16] Guoqiang, Z. and J. Suling, Ontology-based knowledge extraction for relational database schema, 2009 Second International Symposium on Electronic Commerce and Security (2009).

      [17] Lei, C., Xu. Zhuoming and Ni. Lixian, WF2OML: A Modeling Language for Mapping Web Forms to Ontology, 2013 10th Web Information System and Application Conference, (2013).

      [18] Jeong, C.-H., S.P. Choi, S.-H. Shin, S. Lee, H. Jung, S.-Y. Kim and P. Kim, Creating Semantic Data from Relational Database. Proceeding of IEEE International Conference on Social Computing, Washington, D.C, (2013). https://doi.org/10.1109/SocialCom.2013.174.

      [19] Gui-hyun, B., K. Su-kyoung and A. Ki-hong, Framework for Automatically Construct Ontology Knowledge Base from Semi-structured Datasets, the 10th International Conference for Internet Technology and Secured Transactions (ICITST-2015).

      [20] Ishan, R., S. Anurag and C. Adesh, DB2KB: A model to transform database to knowledgebase, 2013 International Conference on Information Systems and Computer Networks (ISCON), (2013).

      [21] Strembeck, M. and U. Zdun, An approach for the systematic development of domain-specific languages, Software – Practice AND Experience, Vol. 39, Issue15, pp. 253-1292, October (2009). https://doi.org/10.1002/spe.936.

      [22] ATLAS group. LINA and INRIA Nantes, ATL: Atlas Transformation Language, ATL User Manual, version 0.7, February (2006).

      [23] JungHyen An and Young B. Park, Methodology for Automatic Ontology Generation Using Database Schema Information, Mobile Information Systems, vol. 2018, Article ID 1359174, 13 pages, (2018). https://doi.org/10.1155/2018/1359174.

      [24] Ahn J.-H. and Park Y. B., Rule extraction ontology generation from an adaptive IoT ecosystem database, in Proceedings of the International Conference on ICT Convergence, Jeju Island, Korea, October (2017). https://doi.org/10.1109/ICTC.2017.8190808.


 

View

Download

Article ID: 28791
 
DOI: 10.14419/ijet.v7i4.28791




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.