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EvoRDF: evolving the exploration of ontology evolution

Published online by Cambridge University Press:  15 August 2018

Haridimos Kondylakis  and
Nikos Papadakis
Haridimos Kondylakis
Affiliation:
Computational BioMedicine Laboratory, FORTH-ICS, N. Plastira 100, Heraklion, Crete, Greece e-mail: kondylak@ics.forth.gr
Nikos Papadakis
Affiliation:
Department of Informatics Engineering, Technological Educational Institute of Crete, Estavromenos 71004, Heraklion, Crete, Greece e-mail: npapadak@cs.teicrete.gr
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Abstract

Ontologies are constantly evolving as new requirements daily occur and the modeling choices of the past should be updated or adapted. Exploring this evolution will enhance the understanding, augmenting the exploitation potential of the available ontologies. However, recent research focuses mostly on detecting changes between ontology versions, overloading end-users with hundreds or even thousands of changes between ontology versions, making it impossible to explore this evolution. To this direction, in this paper, we present EvoRDF, a novel framework for exploring ontology evolution using provenance queries. Our approach uses a high-level language of changes and effectively answers queries about when a specific resource was introduced and how—by which change operations. Even more, why queries can identify the sequence of changes that led to the creation of a specific resource in the latest ontology version or track the evolution of a specific resource from a past ontology version. The evaluation performed shows the feasibility of our solution and the great advantages gained.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e12
Copyright
© Cambridge University Press, 2018 

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References

Arndt, N., Naumann, P. & Marx, E. 2017. Exploring the evolution and provenance of Git versioned RDF data. MEPDaW/LDQ@ESWC, 12–27.Google Scholar
Avgoustaki, A., Flouris, G., Fundulaki, I. & Plexousakis, D. 2016. Provenance management for evolving RDF datasets. In ESWC, 575–592.Google Scholar
Benjelloun, O., Sarma, A. D., Halevy, A., Theobald, M. & Widom, J. 2008. Databases with uncertainty and lineage. The VLDB Journal 17, 243–264.Google Scholar
Buneman, P., Khanna, S. & Tan, W. C. 2001. Why and where: a characterization of data provenance. In ICDT, 316–330.Google Scholar
Brooke, J. 1996. SUS - a quick and dirty usability scale. In Usability Evaluation in Industry, Jordan, P. W., Thomas, B., McClelland, I. L. & Weerdmeester, B. (eds). CRC Press, 189–194.Google Scholar
Chiticariu, L. & Tan, W. C. 2006. Debugging schema mappings with routes. In VLDB, 79–90.Google Scholar
Curino, C., Moon, H., Deutsch, A. & Zaniolo, C. 2013. Automating the database schema evolution process. The VLDB Journal 22(1), 73–98.Google Scholar
De Nies, T., Magliacane, S., Verborgh, R., Coppens, S., Groth, P., Mannens, E. & Van de Walle, R. 2013. Git2prov: exposing version control system content as w3c prov. In Proceedings of the 2013th International Conference on Posters & Demonstrations Track, 1035, 125–128.Google Scholar
Doerr, M., Ore, C. E. & Stead, S. 2007. The CIDOC conceptual reference model: a new standard for knowledge sharing. Tutorials, Posters, Panels and Industrial Contributions at the ER 83, 51–56.Google Scholar
Flouris, G., Manakanatas, D., Kondylakis, H., Plexousakis, D. & Antoniou, G. 2008. Ontology change: classification and survey. Knowledge Engineering Review 23, 117–152.Google Scholar
Gene Ontology Consortium 2004. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research 32, D258–D261.Google Scholar
Graube, M., Hensel, S. & Urbas, L. 2014. R43ples: revisions for triples. In Proceedings of the 1st Workshop on Linked Data Quality Co-Located with 10th International Conference on Semantic Systems (SEMANTiCS).Google Scholar
Green, T. J., Karvounarakis, G. & Tannen, V. 2007. Provenance semirings. In ACM SIGMOD-SIGACT-SIGART PODS. ACM, 31–40.Google Scholar
ISO/IEC 42010:2007, 2007. Systems and software engineering – recommended practice for architectural description of software-intensive systems.Google Scholar
ISO/IEC DIS 25023, 2016. Systems and software engineering – systems and software quality requirements and evaluation (SQuaRE) – measurement of system and software product quality.Google Scholar
Kondylakis, H., Melidoni, D., Glykokokalos, G., Kalykakis, E., Lasithiotakis, M. E., Makridis, J., Moraitis, P., Panteri, A., Plevraki, M., Providakis, A., Skalidaki, M., Stefanos, A., Tampouratzis, M., Trivizakis, E., Zarvakis, F., Zervouraki, E. & Papadakis, N. 2017. EvoRDF: A framework for exploring ontology evolution. In ESWC (demos).Google Scholar
Kondylakis, H. & Plexousakis, D. 2014. Exploring RDF/S evolution using provenance queries. In EDBT/ICDT Workshops.Google Scholar
Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S. & Zhao, J. 2013. Prov-o: The PROV ontology. W3C recommendation 30.Google Scholar
Likert, R. 1932. A technique for the measurement of attitudes. Archives of Psychology 140, 1–55.Google Scholar
Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska, N., Miles, S., Missier, P., Myers, J., Plate, B., Simmhan, Y., Stephan, E. & van den Bussche, J. 2011. The open provenance model core specification (v1. 1). Future Generation Computer Systems 27(6), 743–756.Google Scholar
Noy, N. F., Chugh, A., Liu, W. & Musen, M. A. 2006. A framework for ontology evolution in collaborative environments. In ISWC, 544–558.Google Scholar
Papas, A., Troullinoy, G., Roussakis, G., Kondylakis, H. & Plexousakis, D. 2017. Exploring importance measures for summarizing RDF/S KBs. In ESWC.Google Scholar
Papavasileiou, V., Flouris, G., Fundulaki, I., Kotzinos, D. & Christophides, V. 2013. High-level change detection in RDF(S) KBs. ACM Transactions on Database Systems 38(1), 1:1–1:42.Google Scholar
Papavassiliou, V. 2010. Detecting deterministically high-level changes for RDF/S knowledge bases. Master’s Thesis, Computer Science Department, University of Crete.Google Scholar
Plessers, P. & Troyer, O. D. 2005. Ontology change detection using a version log. In ISWC, 578–592.Google Scholar
Plessers, P., Troyer, O. D. & Casteleyn, S. 2007. Understanding ontology evolution: a change detection approach. Web Semantics: Science, Services and Agents on the World Wide Web 5, 39–49.Google Scholar
RDF PrimerW3C Recommendation. 2004. http://www.w3.org/TR/rdf-primer/ Google Scholar
Rogozan, D. & Paquette, G. 2005. Managing ontology changes on the semantic web. In IEEE/WIC/ACM International Conference on Web Intelligence, 430–433.Google Scholar
Roussakis, Y., Chrysakis, I., Stefanidis, K. & Flouris, G. 2015. D2V: a tool for defining, detecting and visualizing changes on the data web. In ISWC (Posters & Demos).Google Scholar
Ruiz, J. E., Grau, B. C., Horrocks, I. & Berlanga, R. 2011. Supporting concurrent ontology development: Framework, algorithms and tool. Data & Knowledge Engineering 70(1), 146–164.Google Scholar
Sauro, J. R. L. 2009. Correlations among prototypical usability metrics: evidence for the construct of usability. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1609–1618. ACM.Google Scholar
Sauro, J. R. L. 2011. Measuring usability with the System Usability Scale (SUS). https://measuringu.com/sus/(Accessed April 2018).Google Scholar
Stardog. 2018. https://www.stardog.com/(Accessed April 2018).Google Scholar
Stefanidis, K., Flouris, G., Chrysakis, G. & Roussakis, Y. 2016. D2V – understanding the dynamics of evolving data: a case study in the life sciences. ERCIM News, 105.Google Scholar
Stojanovic, L. 2004. Methods and tools for ontology evolution. Phd, University of Karlsruhe.Google Scholar
Theoharis, Y. 2007. On graph features of semantic web schemas. IEEE Transactions on Knowledge and Data Engineering 20, 692–702.Google Scholar
Troullinou, T., Kondylakis, H., Daskalaki, E. & Plexousakis, D. 2014. RDF digest: efficient summarization of RDF/S KBs. In Extended Semantic Web Conference (ESWC).Google Scholar
Troullinou, T., Kondylakis, H., Daskalaki, E. & Plexousakis, D. 2015. RDF digest: ontology exploration using summaries. In International Semantic Web Conference (ISWC).Google Scholar
Troullinou, T., Kondylakis, H., Daskalaki, E. & Plexousakis, D. 2017. Ontology understanding without tears: the summarization approach. Semantic Web Journal 8(6), 797–815.Google Scholar
Troullinou, T., Roussakis, G., Kondylakis, H., Stefanidis, K. & Flouris, G. 2016. Understanding ontology evolution beyond deltas. In EDBT/ICDT Workshops.Google Scholar
Volkel, M., Winkler, W., Sure, Y., Kruk, S. R. & Synak, M. 2005. Semversion: a versioning system for RDF and ontologies. In ESWC.Google Scholar
Zablith, F., Antoniou, G., D’Aquin, M., Flouris, G., Kondylakis, H., Motta, E., Plexousakis, D. & Sabou, M. 2015. Ontology evolution: a process-centric survey. The Knowledge Engineering Review 30(1), 45–75.Google Scholar