Iterative-based Relational Model to Ontology Schema Matching Approach
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National Key Research and Development Program of China (2017YFB1002002); National Natural Science Foundation of China (61772045)

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    Abstract:

    The rapid development of the semantic web makes the various fields in smart city have emerged in the form of ontology to express the knowledge model. However, in the practical semantic Web application, it is often faced with the problem of lack of ontology instance. It is an extremely effective solution to transform the data in the existing relational data source into ontology instance, which requires the use of the relational model to the ontology model matching technology to establish the mapping between the data source and the ontology. In addition, the schema matching to the ontology model is widely used in data integration, data semantic annotation, ontology-based data access, and other fields. The existing related work tends to use a variety of schema matching algorithms to calculate the similarity of element pairs in heterogeneous data patterns. However, when multiple matching algorithms fail at the same time, it is difficult to obtain a more accurate final matching result. In this study, the weekness of the matching of the single schema matching algorithm are analyzed deeply, the localization feature of the data source is an important factor leading to this phenomenon, and an iterative optimization schema matching scheme is proposed. The scheme uses the matched element pairs from matching process to optimize the single schema matching algorithm. The optimized algorithm can be better compatible with the localization features of the data source, with much higher accuracy, and more matching elements can be obtained. The process continues to iterate until the end of the match. In this study, experiments are carried out through a practical case in the fields of "food information management" which have shown that the proposed approach significantly outperforms state-of-the-art method by increasing up to 50.1% of F-measure.

    Reference
    [1] Gagnon, M. Ontology-based integration of data sources. In:Proc. of the Int'l Conf. on Information Fusion IEEE Xplore. 2007. 1-8.
    [2] Wache H, et al. Ontology-based integration of information-A survey of existing approaches. In:Proc. of the IJCAI-01 Workshop:Ontologies and Information Sharing, Vol.2001. 2001.
    [3] Papapanagiotou P, et al. RONTO:Relational to ontology schema matching. AIS Sigsemis Bulletin, 2006,3(3-4):32-36.
    [4] Madhavan J, Bernstein PA, Rahm E. Generic schema matching with cupid. In:Proc. of the Int'l Conf. on Very Large Data Bases Morgan Kaufmann Publishers Inc. 2001. 49-58.
    [5] Rahm, Erhard, Bernstein PA. A survey of approaches to automatic schema matching. The VLDB Journal, 2001,10(4):334-350.
    [6] Bernstein PA, Madhavan J, Rahm E. Generic schemRONa matching, ten years later. Proc. of the VLDB Endowment, 2011, 4(11):695-701.
    [7] Jiménez-Ruiz E, et al. BootOX:Practical mapping of RDBs to OWL 2. In:Proc. of the Int'l Semantic Web Conf. Springer Int'l Publishing, 2015.
    [8] Santoso HA, Haw SC, Abdul-Mehdi ZT. Ontology extraction from relational database:Concept hierarchy as background knowledge. Knowledge-Based Systems, 2011,24(3):457-464.
    [9] Aumueller D, et al. Schema and ontology matching with COMA++. In:Proc. of the 2005 ACM SIGMOD Int'l Conf. on Management of Data. ACM Press, 2005.
    [10] Shvaiko P, Euzenat J. A survey of schema-based matching approaches. Journal on Data Semantics IV. Berlin, Heidelberg:Springer-Verlag, 2005. 146-171.
    [11] Liu C, Wang JW, Han YB. Mashroom+:An interactive data mashup approach with uncertainty handling. Journal of Grid Computing, 2014,12(2):221-244.
    [12] Melnik S, Garcia-Molina H, Rahm E. Similarity flooding:A versatile graph matching algorithm and its application to schema matching. In:Proc. of the 18th Int'l Conf. on Data Engineering. IEEE, 2002. 117-128.
    [13] Euzenat J, Valtchev P. Similarity-based ontology alignment in OWL-lite. In:Proc. of the European Conf. on Artificial Intelligence (ECAI). 2004. 333-337.
    [14] Doan AH, Madhavan J, Domingos P, Halevy AY. Learning to map between ontologies on the semantic Web. In:Proc. of the WWW. 2002. 662-673.
    [15] Li WS, Clifton C, Liu SY. Database integration using neural networks:Implementation and experiences. Knowledge and Information Systems, 2000,2(1):73-96.
    [16] Doan AH, Domingos P, Halevy A. Reconciling schemas of disparate data sources:A machine-learning approach. In:Proc. of the ACM SIGMOD Conf. 2001. 509-520.
    [17] Li WS, Clifton C. SEMINT:A tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data & Knowledge Engineering, 2000,33(1):49-84.
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王丰,王亚沙,赵俊峰,崔达.一种基于迭代的关系模型到本体模型的模式匹配方法.软件学报,2019,30(5):1510-1521

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History
  • Received:August 31,2018
  • Revised:October 31,2018
  • Online: May 08,2019
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