Abstract
Comparison table is an efficient tool for comparing a small number of entities for decision making to analyze the main similarities and differences. The manual choice of their comparison features remains a complex and tedious task. This paper presents \(\textsc { Versus}\), which is the first automatic method for generating comparison tables from knowledge bases of the Semantic Web. For this purpose, we introduce the contextual reference level to evaluate whether a feature is relevant to compare a set of entities. This measure relies on contexts that are sets of entities similar to the compared entities. Its principle is to favor the features whose values for the compared entities are reference (or frequent) in these contexts. We show how to select these contexts and how to efficiently evaluate the contextual reference level from a public SPARQL endpoint limited by a fair-use policy. Using our publicly available benchmark based on Wikidata, the experiments show the interest of the contextual reference level for identifying the features deemed relevant by users with high precision and recall. In addition, the proposed optimizations significantly reduce the execution time and the number of required queries.
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The Typewriter font denotes the literals from Wikidata that are used as illustrations.
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References
Anyanwu, K., Maduko, A., Sheth, A.: SemRank: ranking complex relationship search results on the semantic web. In: Proceedings of the 14th International Conference on World Wide Web, pp. 117–127 (2005)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52
d’Amato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. arXiv preprint arXiv:0911.5043 (2009)
Dessi, A., Atzori, M.: A machine-learning approach to ranking RDF properties. Future Gener. Comput. Syst. 54, 366–377 (2016)
Feddoul, L., Schindler, S., Löffler, F.: Automatic facet generation and selection over knowledge graphs. In: Acosta, M., Cudré-Mauroux, P., Maleshkova, M., Pellegrini, T., Sack, H., Sure-Vetter, Y. (eds.) SEMANTiCS 2019. LNCS, vol. 11702, pp. 310–325. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33220-4_23
Hahn, R., et al.: Faceted Wikipedia search. In: Abramowicz, W., Tolksdorf, R. (eds.) BIS 2010. LNBIP, vol. 47, pp. 1–11. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12814-1_1
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics, pp. 159–174 (1977)
Oren, E., Delbru, R., Decker, S.: Extending faceted navigation for RDF data. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 559–572. Springer, Heidelberg (2006). https://doi.org/10.1007/11926078_40
Petrova, A., Sherkhonov, E., Cuenca Grau, B., Horrocks, I.: Entity comparison in RDF graphs. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 526–541. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_31
Razniewski, S., Suchanek, F., Nutt, W.: But what do we actually know? In: Proceedings of the 5th Workshop on Automated Knowledge Base Construction, pp. 40–44 (2016)
Sáez, T., Hogan, A.: Automatically generating Wikipedia info-boxes from Wikidata. In: Companion Proceedings of the Web Conference 2018, pp. 1823–1830 (2018)
Soulet, A., Suchanek, F.M.: Anytime large-scale analytics of linked open data. In: Ghidini, C., Hartig, O., Maleshkova, M., Svátek, V., Cruz, I., Hogan, A., Song, J., Lefrançois, M., Gandon, F. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 576–592. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_33
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)
Tversky, A.: Features of similarity. Psychol. Revi. 84(4), 327 (1977)
Tzitzikas, Y., Manolis, N., Papadakos, P.: Faceted exploration of RDF/S datasets: a survey. J. Intell. Inf. Syst. 48(2), 329–364 (2017)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
Wu, F., Weld, D.S.: Automatically refining the Wikipedia infobox ontology. In: Proceedings of the 17th International Conference on World Wide Web, pp. 635–644 (2008)
Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for linked data: survey. Semantic Web 7(1), 63–93 (2016)
Acknowledgments
We thank the evaluators for the time they took to annotate the features. This work was partially supported by the grant ANR-18-CE38-0009 (“SESAME”).
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Giacometti, A., Markhoff, B., Soulet, A. (2021). Comparison Table Generation from Knowledge Bases. In: Verborgh, R., et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_11
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