Abstract
In recent years, the question on Automatic Ontology Merging (AOM) become challenging to address for the researchers. Our research and development for the Disjoint Knowledge Perservation based Automatic Ontology Merging (DKP-AOM) is a milestone in the same direction. This paper provides a more specific discussion about disjoint knowledge axioms in DKP-AOM and makes an assessment of our merge algorithm that looks-up within disjoint partitions of concept hierarchies of ontologies. The significant use of disjoint knowledge is corroborated by testing conference and vertebrate ontologies. The results reveal that disjoint knowledge axioms help identifying initial inaccurate mappings and remove ambiguity when the concept with same symbolic identifier has a different meaning in different local ontologies in the process of ontology merging. Disjoint axioms separate the knowledge in distinct chunks and enable concept matching within the boundaries of sub-hierarchies of the entire ontology concept hierarchy. While finding matches between concepts of ontologies, disjoint partitions with the disjoint knowledge about concepts in source ontologies minimize the search space and reduce the runtime complexity of ontology merging. We also discuss encouraging results obtained by our DKP-AOM system within the OAEI 2015 campaign.
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Microsoft Translate API: https://code.google.com/p/microsoft-translator-java-api/
Conference track: http://oaei.ontologymatching.org/2015/conference/eval.html
Conference ontologies: http://oaei.ontologymatching.org/2015/conference/index.html
References
Anjum, N., Harding, J., Young, B., & Case, K. (2010). Gap analysis of ontology mapping tools and techniques. In Enterprise interoperability IV (pp. 303–312). Berlin: Springer.
Baumeister, J., & Seipel, D. (2005). Smelly owls-design anomalies in ontologies. In FLAIRS conference (Vol. 215).
Brank, J., Grobelnik, M., & Mladenic, D. (2005). A survey of ontology evaluation techniques. In Proceedings of the conference on data mining and data warehouses (SiKDD 2005) (pp. 166–170).
Chalupsky, H. (2000). Ontomorph: a translation system for symbolic knowledge. In KR (pp. 471–482).
El Jerroudi, Z., & Ziegler, J. (2008). imerge: interactive ontology merging. In Proceedings of the international conference on knowledge engineering and knowledge management (EKAW 2008) (p. 52).
Euzenat, J., Shvaiko, P., et al. (2007). Ontology matching (Vol. 333). Berlin: Springer.
Euzenat, J., Meilicke, C., Stuckenschmidt, H., Shvaiko, P., & Trojahn, C. (2011). Ontology alignment evaluation initiative: six years of experience. In Journal on data semantics XV (pp. 158–192). Berlin: Springer.
Fahad, M. (2015). Dkp-aom: results for oaei 2015. arXiv preprint arXiv:151001659.
Fahad, M. (2017). Merging of axiomatic definitions of concepts in the complex owl ontologies. Artificial Intelligence Review, 47(2), 181–215. https://doi.org/10.1007/s10462-016-9479-5.
Fahad, M., & Qadir, M.A., (2008). A framework for ontology evaluation. ICCS Supplement, 354, 149–158.
Fahad, M., Moalla, N., & Bouras, A. (2011). Towards ensuring satisfiability of merged ontology. Procedia CS, 4, 2216–2225.
Fahad, M., Moalla, N., & Bouras, A. (2012). Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system. Journal of Intelligent Information Systems, 39(2), 535–557.
Jiménez-Ruiz, E., & Grau, B.C. (2011). Logmap: logic-based and scalable ontology matching. In The Semantic Web–ISWC 2011 (pp. 273–288). Berlin: Springer.
Jiménez-Ruiz, E., Grau, B.C., Sattler, U., Schneider, T., & Berlanga, R. (2008). Safe and economic re-use of ontologies: a logic-based methodology and tool support. Berlin: Springer.
Kim, J., Jang, M., Ha, Y.G., Sohn, J.C., & Lee, S.J. (2005). Moa: owl ontology merging and alignment tool for the semantic web. In Innovations in applied artificial intelligence (pp. 722–731). Berlin: Springer.
Klein, M. (2001). Combining and relating ontologies: an analysis of problems and solutions. In IJCAI-2001 Workshop on ontologies and info sharing (pp. 53–62).
Kotis, K., Vouros, G.A., & Stergiou, K. (2006). Towards automatic merging of domain ontologies: the hcone-merge approach. Web Semantics: Science, Services and Agents on the World Wide Web, 4(1), 60–79.
Maiz, N., Fahad, M., Boussaid, O., & Bentayeb, F. (2010). Automatic ontology merging by hierarchical clustering and inference mechanisms. In Proceedings of I-KNOW (pp. 1–3).
McGuinness, D.L., Fikes, R., Rice, J., & Wilder, S. (2000). An environment for merging and testing large ontologies. In KR (pp. 483–493).
Mitra, P., & Wiederhold, G. (2002). Resolving terminological heterogeneity in ontologies. In Proceedings of the ECAI workshop on ontologies and semantic interoperability.
Noy, N.F., & Musen, M.A. (2003). The prompt suite: interactive tools for ontology merging and mapping. International Journal of Human-Computer Studies, 59 (6), 983–1024.
Qadir, M.A., & Noshairwan, W. (2007). Warnings for disjoint knowledge omission in ontologies. In Second international conference on internet and web applications and services, 2007. ICIW’07 (pp. 45–45). IEEE.
Qadir, M.A., Fahad, M., & Noshairwan, M.W. (2007). On conceptualization mismatches between ontologies. In IEEE international conference on granular computing, 2007. GRC 2007 (pp. 275–275). IEEE.
Raunich, S., & Rahm, E. (2011). Atom: automatic target-driven ontology merging. In 2011 IEEE 27th international conference on data engineering (ICDE) (pp. 1276–1279). IEEE.
Shvaiko, P., & Euzenat, J. (2013). Ontology matching: state of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering, 25(1), 158–176.
Stumme, G., & Maedche, A. (2001). Fca-merge: bottom-up merging of ontologies. In IJCAI (Vol. 1, pp. 225–230).
Völker, J., Vrandečić, D., Sure, Y., & Hotho, A. (2007). Learning disjointness. In The semantic web: research and applications (pp. 175–189). Berlin: Springer.
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Fahad, M. Toward analyzing impact of disjoint axioms for merging heterogeneous ontologies. J Intell Inf Syst 51, 49–70 (2018). https://doi.org/10.1007/s10844-017-0490-3
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DOI: https://doi.org/10.1007/s10844-017-0490-3