一种基于迭代的关系模型到本体模型的模式匹配方法
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摘要
语义网的飞速发展,使得各领域出现了以本体这种形式来表达的知识模型.但在实际的语义网应用中,常常面临本体实例匮乏的问题.将现有关系型数据源中的数据转化为本体实例是一种有效的解决办法,这需要利用关系模型到本体模型的模式匹配技术来建立数据源和本体之间的映射关系.除此之外,关系模型到本体模型的模式匹配还被广泛用于数据集成、数据语义标注、基于本体的数据访问等领域中.现有的研究工作往往会综合使用多种模式匹配算法,计算异构数据模式中元素对的综合相似度,辅助人工建立数据源到本体的映射关系.现有的工作针对单一模式匹配算法准确率不高的问题,试图通过综合多种模式匹配算法的结果来进行调和.然而,这种方法当多种匹配算法同时出现不准时,难以得出更加准确的最终匹配结果.对单一模式匹配算法匹配不准的成因进行深入的分析,认为数据源的本地化特征是导致这一现象的重要因素,并提出了一种迭代优化的模式匹配方案.该方案利用在模式匹配过程中已经得到匹配的元素对,对单一模式匹配算法进行优化,经过优化后的算法能够更好地兼容数据源的本地化特征,从而显著提升准确率.在"餐饮信息管理"领域的一个实际案例上开展实验,模式匹配效果显著高于传统方法,其中,F值超过传统方法 50.1%.
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.
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.
引文
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[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.