FCA based ontology development for data integration

A formal and semi-automated method is proposed to support ontology integration.The method is designed to deal with data exhibiting implicit and ambiguous information.Case studies have been carried out on several non-trivial industrial datasets.Resultant ontologies better fit and respect underlying knowledge structure of the domain. Data is a valuable asset to our society. Effective use of data can enhance productivity of business and create economic benefit to customers. However with data growing at unprecedented rates, organisations are struggling to take full advantage of available data. One main reason for this is that data is usually originated from disparate sources. This can result in data heterogeneity, and prevent data from being digested easily. Among other techniques developed, ontology based approaches is one promising method for overcoming heterogeneity and improving data interoperability. This paper contributes a formal and semi-automated approach for ontology development based on Formal Concept Analysis (FCA), with the aim to integrate data that exhibits implicit and ambiguous information. A case study has been carried out on several non-trivial industrial datasets, and our experimental results demonstrate that proposed method offers an effective mechanism that enables organisations to interrogate and curate heterogeneous data, and to create the knowledge that meets the need of business. Display Omitted

[1]  Maurizio Lenzerini,et al.  Data integration: a theoretical perspective , 2002, PODS.

[2]  Pedro M. Domingos,et al.  Ontology Matching: A Machine Learning Approach , 2004, Handbook on Ontologies.

[3]  Mark A. Musen,et al.  PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment , 2000, AAAI/IAAI.

[4]  Ngoc Thanh Nguyen,et al.  A Multi-attribute and Multi-valued Model for Fuzzy Ontology Integrationon Instance Level , 2012, ACIIDS.

[5]  Alon Y. Halevy,et al.  Principles of Data Integration , 2012 .

[6]  Jian Liu,et al.  Data integration in fuzzy XML documents , 2014, Inf. Sci..

[7]  Arshdeep Bahga,et al.  Healthcare Data Integration and Informatics in the Cloud , 2015, Computer.

[8]  Petko Valtchev,et al.  Galicia : an open platform for lattices , 2003 .

[9]  R. Studer,et al.  Semantic Web Technologies: Trends and Research in Ontology-based Systems , 2006 .

[10]  Anthony G. Cohn,et al.  Utility Ontology Development with Formal Concept Analysis , 2008, FOIS.

[11]  Robert M. Colomb,et al.  A Model Driven Approach for Building OWL DL and OWL Full Ontologies , 2006, SEMWEB.

[12]  Felix Naumann,et al.  Attribute classification using feature analysis , 2002, Proceedings 18th International Conference on Data Engineering.

[13]  Hans-Ulrich Prokosch,et al.  Ontology-Based Data Integration between Clinical and Research Systems , 2015, PloS one.

[14]  Erhard Rahm,et al.  COMA - A System for Flexible Combination of Schema Matching Approaches , 2002, VLDB.

[15]  Pedro M. Domingos,et al.  Reconciling schemas of disparate data sources: a machine-learning approach , 2001, SIGMOD '01.

[16]  Houari Sahraoui,et al.  Merging conceptual hierarchies using concept lattices , 2004 .

[17]  Shiu-Li Huang,et al.  Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing , 2012, Inf. Process. Manag..

[18]  Sarvapali D. Ramchurn,et al.  Trust in multi-agent systems , 2004, The Knowledge Engineering Review.

[19]  Gerd Stumme,et al.  Formal Concept Analysis: foundations and applications , 2005 .

[20]  Michael F. Worboys,et al.  An algebraic approach to automated geospatial information fusion , 2005, Int. J. Geogr. Inf. Sci..

[21]  Ngoc Thanh Nguyen,et al.  Local Neighbor Enrichment for Ontology Integration , 2012, ACIIDS.

[22]  Hernán Astudillo,et al.  MAnaging SPEcialization/Generalization HIerarchies , 2002, OOIS Workshops.

[23]  Christoph Tempich,et al.  Ontology Engineering Methodologies , 2006 .

[24]  Max J. Egenhofer,et al.  Determining Semantic Similarity among Entity Classes from Different Ontologies , 2003, IEEE Trans. Knowl. Data Eng..

[25]  Michael Uschold,et al.  Ontologies and semantics for seamless connectivity , 2004, SGMD.

[26]  Steffen Staab,et al.  On How to Perform a Gold Standard Based Evaluation of Ontology Learning , 2006, SEMWEB.

[27]  Torben Bach Pedersen,et al.  On-demand multidimensional data integration: toward a semantic foundation for cloud intelligence , 2011, The Journal of Supercomputing.

[28]  Natalya F. Noy,et al.  Semantic integration: a survey of ontology-based approaches , 2004, SGMD.

[29]  Ngoc Thanh Nguyen.  Conflicts of Ontologies - Classification and Consensus-Based Methods for Resolving , 2006, KES.

[30]  Yorick Wilks,et al.  Data Driven Ontology Evaluation , 2004, LREC.

[31]  Yannis Kalfoglou,et al.  Ontology mapping: the state of the art , 2003, The Knowledge Engineering Review.

[32]  Hong Xia.  Semantic Web Ontology Integration Based on Formal Concept Analysis , 2013, ICRA 2013.

[33]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[34]  Jayant Madhavan,et al.  Composing Mappings Among Data Sources , 2003, VLDB.

[35]  H. Sofia Pinto,et al.  Ontologies: How can They be Built? , 2004, Knowledge and Information Systems.

[36]  Yong Tang,et al.  Feature-based approaches to semantic similarity assessment of concepts using Wikipedia , 2015, Inf. Process. Manag..

[37]  Rudolf Wille,et al.  Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts , 2009, ICFCA.

[38]  Timothy W. Simpson,et al.  A Methodology for Product Family Ontology Development Using Formal Concept Analysis and Web Ontology Language , 2006, J. Comput. Inf. Sci. Eng..

[39]  QingTuan Wang,et al.  Construction of Ontology Information System Based on Formal Concept Analysis , 2011, CSISE.

[40]  Erhard Rahm,et al.  Generic Schema Matching with Cupid , 2001, VLDB.

[41]  Anna Formica,et al.  Ontology-based concept similarity in Formal Concept Analysis , 2006, Inf. Sci..

[42]  Xin Bai,et al.  Development of Ontology-Based Information System Using Formal Concept Analysis and Association Rules , 2011, CSISE.

[43]  Erhard Rahm,et al.  A survey of approaches to automatic schema matching , 2001, The VLDB Journal.

[44]  Rung Ching Chen,et al.  Merging domain ontologies based on the WordNet system and Fuzzy Formal Concept Analysis techniques , 2011, Appl. Soft Comput..

[45]  Philipp Cimiano,et al.  A Machine Learning Approach to Multilingual and Cross-Lingual Ontology Matching , 2011, SEMWEB.

[46]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[47]  Michael F. Worboys,et al.  An algebraic approach to automated information fusion , 2005 .

[48]  Gerd Stumme,et al.  FCA-MERGE: Bottom-Up Merging of Ontologies , 2001, IJCAI.

[49]  Jeff Z. Pan,et al.  DartWiki: A Semantic Wiki for Ontology-Based Knowledge Integration in the Biomedical Domain , 2012 .

[50]  Alon Y. Halevy,et al.  Introduction to the special issue on semantic integration , 2004, SGMD.

[51]  Anthony G. Cohn,et al.  Utility data integration and knowledge representation in the UK: the VISTA project , 2013 .

[52]  Xinguang Peng,et al.  The Research and Implementation of Heterogeneous Data Integration under Ontology Mapping Mechanism , 2011, WISM.

[53]  Xia Wang,et al.  Ontology Mapping based on Rough Formal Concept Analysis , 2006, Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services (AICT-ICIW'06).

[54]  Ladjel Bellatreche,et al.  Ontologies and Functional Dependencies for Data Integration and Reconciliation , 2011, ER Workshops.

[55]  Ngoc Thanh Nguyen,et al.  A METHOD FOR ONTOLOGY CONFLICT RESOLUTION AND INTEGRATION ON RELATION LEVEL , 2007, Cybern. Syst..

[56]  Steven Guan,et al.  Tree-structure Based Ontology Integration , 2011, J. Inf. Sci..

[57]  Steffen Staab,et al.  Measuring Similarity between Ontologies , 2002, EKAW.

[58]  GeunSik Jo,et al.  Enhancing performance and accuracy of ontology integration by propagating priorly matchable concepts , 2012, Neurocomputing.

[59]  Soo Mi Yang.  Efficient Ontology Integration Model for Better Inference in Context Aware Computing , 2011 .

[60]  Anthony G. Cohn,et al.  Semantic integration for mapping the underworld , 2008, Geoinformatics.