Abstract
The emergence of uncertainty as an inherent aspect of RDF and linked data has spurred a number of works of both theoretical and practical interest These works aim to incorporate such information in a meaningful way in the computation of queries. In this paper, we propose a framework of query evaluation in the presence of uncertainty, based on probabilistic automata, which are simple yet efficient computational models. We showcase this method on relevant examples, where we show how to construct and exploit the convenient properties of such automata to evaluate RDF queries with adjustable cutoff. Finally, we present some directions for further investigation on this particular line of research, taking into account possible generalizations of this work.
Keywords
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SPARQL 1.1 Query Language. Technical report, W3C (2013), http://www.w3.org/TR/sparql11-query
Akbarinia, R., Valduriez, P., Verger, G.: Efficient evaluation of SUM queries over probabilistic data. IEEE Trans. Knowl. Data Eng. 25(4), 764–775 (2013)
Baier, C., Grösser, M., Bertrand, N.: Probabilistic \(\omega \)-automata. J. ACM 59(1), 1–52 (2012)
Barceló, P., Libkin, L., Reutter, J.L.: Querying regular graph patterns. J. ACM (JACM) 61(1), 8 (2014)
Dalvi, N., Suciu, D.: Efficient query evaluation on probabilistic databases. The VLDB J.- Int. J. Very Large Data Bases 16(4), 523–544 (2007)
Fang, H., Zhang, X.: pSPARQL: a querying language for probabilistic RDF. In: Proceedings of ISWC Posters and Demos (2016)
Fernandez, M., Suciu, D.: Optimizing regular path expressions using graph schemas. In: Proceedings of the 14th International Conference on Data Engineering, pp. 14–23. IEEE (1998)
Giannakis, K., Andronikos, T.: Querying linked data and Büchi automata. In: 2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 110–114. IEEE (2014)
Giannakis, K., Theocharopoulou, G., Papalitsas, C., Andronikos, T., Vlamos, P.: Associating \(\omega \)-automata to path queries on Webs of Linked Data. Eng. Appl. Artif. Intell. 51, 115–123 (2016)
Hartig, O.: An overview on execution strategies for Linked Data queries. Datenbank-Spektrum 13(2), 89–99 (2013)
Hua, M., Pei, J.: Probabilistic path queries in road networks: traffic uncertainty aware path selection. In: Proceedings of the 13th International Conference on Extending Database Technology, pp. 347–358. ACM (2010)
Huang, H., Liu, C.: Query evaluation on probabilistic RDF databases. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 307–320. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04409-0_32
Khan, A., Chen, L.: On uncertain graphs modeling and queries. Proc. VLDB Endowment 8(12), 2042–2043 (2015)
Krompaß, D., Nickel, M., Tresp, V.: Querying factorized probabilistic triple databases. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 114–129. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11915-1_8
Lian, X., Chen, L., Wang, G.: Quality-aware subgraph matching over inconsistent probabilistic graph databases. IEEE Trans. Knowl. Data Eng. 28(6), 1560–1574 (2016)
Marshall, M.S., Boyce, R., Deus, H.F., Zhao, J., Willighagen, E.L., Samwald, M., Pichler, E., Hajagos, J., Prud’hommeaux, E., Stephens, S.: Emerging practices for mapping and linking life sciences data using RDF-a case series. Web Semant. Sci. Serv. Agents World Wide Web 14, 2–13 (2012)
Paz, A.: Introduction to probabilistic automata. Academic Press Inc., Orlando (1971)
Rabin, M.O.: Probabilistic automata. Inf. Control 6(3), 230–245 (1963)
Reynolds, D.: Position paper: uncertainty reasoning for linked data. In: Workshop, vol. 14 (2014)
Schoenfisch, J.: Querying probabilistic ontologies with SPARQL. In: Proceedings GI-Edition, vol. 232, pp. 2245–2256 (2014)
Sistla, A.P., Hu, T., Chowdhry, V.: Similarity based retrieval from sequence databases using automata as queries. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 237–244. ACM (2002)
Theocharopoulou, G., Giannakis, K.: Web mining to create semantic content: a case study for the environment. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Karatzas, K., Sioutas, S. (eds.) AIAI 2012. IFIP AICT, vol. 382, pp. 411–420. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33412-2_42
Wang, X., Ling, J., Wang, J., Wang, K., Feng, Z.: Answering provenance-aware regular path queries on RDF graphs using an automata-based algorithm. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 395–396. ACM (2014)
Zhang, X., Feng, Z., Wang, X., Rao, G., Wu, W.: Context-free path queries on RDF graphs. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 632–648. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46523-4_38
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Andronikos, T., Singh, A., Giannakis, K., Sioutas, S. (2018). Computing Probabilistic Queries in the Presence of Uncertainty via Probabilistic Automata. In: Alistarh, D., Delis, A., Pallis, G. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2017. Lecture Notes in Computer Science(), vol 10739. Springer, Cham. https://doi.org/10.1007/978-3-319-74875-7_7
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DOI: https://doi.org/10.1007/978-3-319-74875-7_7
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