Pytology: Towards the Calculation of Relevance on RDF Data

• Victor V. Barros Leal Federal University of Ceará (UFC)
• José Antônio F. de Macedo Federal University of Ceará (UFC)
• Lucas Peres Gaspar Federal University of Ceará (UFC)
• David Araújo Abreu Federal University of Ceará (UFC)

DOI: https://doi.org/10.5753/sbbd.2018.22235

Abstract

With the wide availability of RDF datasets on the Web, it becomes increasingly complex the manual analysis to understand the domains of ontologies and their levels of links. Therefore, a challenge is the semi-automatic identification of the relevant relations at the ontology, which are important to define the semantics of the data. This work presents a method to calculate relevance values to the predicates in an ontology by using topological analysis. We show the consolidation of this work with a tool named Pytology and the experimental results generated by using available datasets on the web.

Keywords: RDF data, ontology, relevance, topological analysis metrics

References

Abdi, H. (2007). The kendall rank correlation coefficient. Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks, CA, pages 508–510.

Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., and Ives, Z. (2007). Dbpedia: A nucleus for a web of open data. In The semantic web, pages 722–735. Springer.

Crubézy, M. and Musen, M. A. (2004). Ontologies in support of problem solving. In Handbook on ontologies, pages 321–341. Springer.

Elbassuoni, S. and Blanco, R. (2011). Keyword search over rdf graphs. In Proceedings of the 20th ACM international conference on Information and knowledge management, pages 237–242. ACM.

Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social networks, 1(3):215–239.

Marchionini, G. (2006). Exploratory search: from finding to understanding. Communications of the ACM, 49(4):41–46.

Mirizzi, R. and Di Noia, T. (2010). From exploratory search to web search and back. In Proceedings of the 3rd workshop on Ph. D. students in information and knowledge management, pages 39–46. ACM.

Musetti, A., Nuzzolese, A. G., Draicchio, F., Presutti, V., Blomqvist, E., Gangemi, A., and Ciancarini, P. (2012). Aemoo: Exploratory search based on knowledge patterns over the semantic web. Semantic Web Challenge, 136.

Roa-Valverde, A. J. and Sicilia, M.-A. (2014). A survey of approaches for ranking on the web of data. Information Retrieval, 17(4):295–325.

Sedgwick, P. (2012). Pearson’s correlation coefficient. BMJ: British Medical Journal (Online), 345.

Zar, J. H. (1998). Spearman rank correlation. Encyclopedia of Biostatistics.