Abstract
Information integration has a long history since humans started using and collecting information. But, it has been a strong focus of IT research since many recent years. It deals with providing a unified and transparent access to a collection of heterogeneous data sources. In information integration, the formulization of a global schema is a difficult task that manages multiple, autonomous and heterogeneous data sources. This paper presents a semantic system named OntMed for an ontology-based data integration of heterogeneous data sources to achieve interoperability between them. Our system is based on the quality criteria (consistency, completeness and conciseness) for building the reliable analysis contexts to provide an accurate unified view of data to the end user. The generation of an error-free global analysis context with the semantic validation of initial mappings generates accuracy, and provides the means to access and exchange information in semantically sound manner. In addition, data integration in this way becomes more practical for dynamic situations and helps decision makers to work within a more consistent and reliable virtual data warehouse.
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References
Bernstein, P.A., Haas, L.M.: Information integration in the enterprise. Commun. ACM (CACM) 51(1), 72–79 (2008)
Ziegler, P., Dittrich, K.R.: Three Decades of Data Integration - All Problems Solved?, WCC, pp. 3–12 (2004)
IDC, Worldwide Data Integration and Access Software, 2008–2012, Forecast. Doc No. 211636, Apr. (2008)
Wache, H., et al.: Ontology-based integration of information - a survey of existing approaches. In: Proceedings of the IJCAI-01 Workshop on Ontologies and Information Sharing (2001)
Arens, Y., Knoblock, C.A., Hsu, C.: Query Processing in the SIMS Information Mediator. In The AAAI Press (1996)
Mena, E., Kashyap, V., Sheth, A.P., Illarramendi, A.: OBSERVER: an approach for query processing in global information systems based on interoperation across pre-existing ontologies. In: Proceedings of the 1st IFCIS International Conference on Cooperative Information Systems (CoopIS 1996), pp. 14–25 (1996)
Cruz, I.F., Xiao, H.: Using a layered approach for interoperability on the semantic web. In: Proceedings of the 4th International Conference on Web Information Systems Engineering (WISE), pp. 221–232, Rome, Italy (2003)
Lenzerini, M.: Data Integration: A Theoretical Perspective. PODS, pp. 233–246 (2002)
Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology, ACM SIGMOD Record 26(1), 65–74 (1997)
Inmon, W.H.: Building the Data Warehouse. John Wiley & Sons Inc, New York, USA (1992)
Kimball, R.: The operational data warehouse. DBMS 11(1), 14–16 (1998)
Fahad, M., Qadir, M.A.: A framework for ontology evaluation. 16th ICCS Supplement Proceeding, 354, pp.149–158, France (2008)
Maiz, N., Fahad, M., Boussaid, O., Tayab, F.B.: Automatic ontology merging by hierarchical clustering and inference mechanisms. In: proceedings of 10th International Conference on knowledge Management and Knowledge Technologies (I-Know’10), Sept 1–3, Messe Congress Graz, Austria (2010)
Upadhyaya, S.R., Kumar, P.S.: ERONTO: a tool for extracting ontologies from extended E/R diagrams. ACM Symposium on Applied Computing (2005)
Xu, Z., Zhang, S., Dong, Y.: Mapping between relational database schema and OWL ontology for Deep Annotation. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI’06), IEEE (2006)
Fahad, M., Moalla, N., Bouras, A.: Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system. J. Intell. Inf. Syst. (JIIS) 39(2), 535–557 (2012)
Gómez-Pérez, A.: Evaluating ontologies: cases of study. IEEE Intell. Syst. and their Appl. 16(3), 391–409 (2001)
Gomez-Perez, A., Fernández-López, M., Corcho, O.: Ontological engineering: with examples from the areas of knowledge management. E-Commerce and the Semantic Web. Springer, London (2004). https://doi.org/10.1007/b97353
Baumeister, J., Seipel, D.S.: Smelly owls–design anomalies in ontologies. In: 18th Intl. Florida AI Research Society Conference, pp. 251–220. AAAI Press (2005)
Fahad, M.: Initial results for ontology matching workshop 2015, DKP-AOM: Results for OAEI 2015. In: CEUR Workshop Proceedings 1766, pp. 82–96. 5 (2015). http://oaei.ontologymatching.org/2015/conference/index.html
Cheatham, M., Dragisic, Z., Euzenat, J., Faria, D., Ferrara, A., et al.: Results of the ontology alignment evaluation initiative 2015. 10th ISWC Workshop on Ontology Matching (OM), Oct, Bethlehem, United States. pp. 60–115 (2015)
Solimando, A., Jiménez-Ruiz, E., Guerrini, G.: Detecting and correcting conservativity principle violations in ontology-to-ontology mappings. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 1–16. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11915-1_1
Solimando, A., Jimenez-Ruiz, E., Pinkel, C.: Evaluating ontology alignment systems in query answering tasks. Poster paper at International Semantic Web Conference (ISWC) (2014)
Kaur, P., Kaur, P.: New approach of computing data cubes in data warehousing. Int. J. Inf. Comp. Technol. 4(14), 1411–1417 (2014)
Chen, Z., Zhao, T.: A cube model approach for data warehouse. Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering, pp. 846–849 (2015)
Liu, Y., Sung, S.Y., Xiong, H.: A cubic-wise balance approach for privacy preservation in data cubes. Inf. Sci. 176, 1215–1240 (2006)
Alejandro, G.T., Marotta, A.: An Overview of Data Warehouse Design Approaches and Techniques (2001)
Huang, S., Chou, T., Seng, J.: Data warehouse enhancement: a semantic cube model approach. Inf. Sci. 177(11), 2238–2254 (2007)
Sobral, T., Galvão, T., Borges, J.: An ontology-based approach to knowledge-assisted integration and visualization of urban mobility data. Expert Systems with Applications, 150 (2020)
Belitz-Hellwich, W.: An Ontology-Based Platform for Information Integration; Supporting Sustainable Smart Transportation Infrastructure (2023). https://www.diva-portal.org/smash/get/diva2:1737543/FULLTEXT01.pdf
Haw, S.-C., May, J.-W., Subramaniam, S.: Mapping relational databases to ontology representation: a review. In: ICDTE’17, pp. 54–55 (2017)
Kharlamov, E., Hovland, D., Jimenez-Rui, E., et al.: Ontology based data access in statoil. J. Web Semantics 44, 3–36 (2017)
Can, O., Unalir, M.: Revisiting ontology based access control: the case for ontology based data access. In: Proceedings of the 8th International Conference on Information Systems Security and Privacy (ICISSP), pp. 515–518 (2022)
Acknowledgement
The research depicted in this paper is funded by the French National Research Agency (ANR), project ANR-19-CE23–0005 BI4people (Business Intelligence for the people).
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Fahad, M. (2023). Ontology-Based Mediation with Quality Criteria. In: El Ayachi, R., Fakir, M., Baslam, M. (eds) Business Intelligence. CBI 2023. Lecture Notes in Business Information Processing, vol 484 . Springer, Cham. https://doi.org/10.1007/978-3-031-37872-0_6
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