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Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query results

  • Ibrahim Dellal
  • Stéphane JeanEmail author
  • Allel Hadjali
  • Brice Chardin
  • Mickaël Baron
Regular Paper
  • 73 Downloads

Abstract

Several large uncertain knowledge bases (KBs) are available on the Web where facts are associated with a certainty degree. When querying these uncertain KBs, users seek high-quality results, i.e., results that have a certainty degree greater than a given threshold \(\alpha \). However, as they usually have only a partial knowledge of the KB contents, their queries may be failing i.e., they return no result for the desired certainty level. To prevent this frustrating situation, instead of returning an empty set of answers, our approach explains the reasons of the failure with a set of \(\alpha \)minimal failing subqueries (\(\alpha \)MFSs) and computes alternative relaxed queries, called \(\alpha \)maXimal succeeding subqueries (\(\alpha \)XSSs), that are as close as possible to the initial failing query. Moreover, as the user may not always be able to provide an appropriate threshold \(\alpha \), we propose three algorithms to compute the \(\alpha \)MFSs and \(\alpha \)XSSs for other thresholds, which also constitutes a relevant feedback for the user. Multiple experiments with the WatDiv benchmark show the relevance of our algorithms compared to a baseline method.

Keywords

Uncertain knowledge bases RDF quad SPARQL queries Empty answers Named graph Reification Quadstore 

Notes

References

  1. 1.
    Rodríguez M, Goldberg S, Wang DZ (2016) Sigmakb: multiple probabilistic knowledge base fusion. Proc VLDB Endow 9(13):1577–1580CrossRefGoogle Scholar
  2. 2.
    Hoffart J, Suchanek FM, Berberich K, Weikum G (2013) YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif Intell 194:28–61MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER Jr, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: AAAI, vol 5, p 3Google Scholar
  4. 4.
    Dong X, Gabrilovich E, Heitz G, Horn W, Lao N, Murphy K, Strohmann T, Sun S, Zhang W (2014) Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD’14, pp 601–610Google Scholar
  5. 5.
    Wu W, Li H, Wang H, Zhu KQ (2012) Probase: a probabilistic taxonomy for text understanding. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data. ACM, pp 481–492Google Scholar
  6. 6.
    Harris S, Garlik AS (2013) Sparql 1.1 query language (march 2013). W3C RecommendationGoogle Scholar
  7. 7.
    Hartig O (2009) Querying trust in RDF data with tSPARQL. In: ESWC 2009Google Scholar
  8. 8.
    Tomaszuk D, Pak K, Rybiński H (2013) Trust in RDF graphs. In: ADBIS’13Google Scholar
  9. 9.
    Saleem M, Ali MI, Hogan A, Mehmood Q, Ngomo AN (2015) LSQ: the linked SPARQL queries dataset. In: ISWC’15, pp 261–269Google Scholar
  10. 10.
    Mottin D, Marascu A, Roy SB, Das G, Palpanas T, Velegrakis Y (2013) A probabilistic optimization framework for the empty-answer problem. Proc VLDB Endow 6(14):1762–1773CrossRefGoogle Scholar
  11. 11.
    Godfrey P (1997) Minimization in cooperative response to failing database queries. Int J Coop Inf Syst 6(2):95–149CrossRefGoogle Scholar
  12. 12.
    Fokou G, Jean S, Hadjali A, Baron M (2017) Handling failing RDF queries: from diagnosis to relaxation. Knowl Inf Syst (KAIS) 50(1):167–195CrossRefGoogle Scholar
  13. 13.
    Erling O, Mikhailov I (2009) RDF support in the virtuoso DBMS. In: Pellegrini T, Auer S, Tochtermann K, Schaffert S (eds) Networked knowledge—networked media. Springer, Berlin, pp 7–24CrossRefGoogle Scholar
  14. 14.
    Dellal I, Jean S, Hadjali A, Chardin B, Baron M (2017) On addressing the empty answer problem in uncertain knowledge bases. In: Benslimane D, Damiani E, Grosky WI, Hameurlain A, Sheth A, Wagner RR (eds) Database and expert systems applications. Springer International Publishing, pp 120–129Google Scholar
  15. 15.
    Pérez J, Arenas M, Gutierrez C (2009) Semantics and complexity of SPARQL. ACM Trans Database Syst (TODS) 34(3):16:1–16:45CrossRefGoogle Scholar
  16. 16.
    Mannila H, Toivonen H (1997) Levelwise search and borders of theories in knowledge discovery. Data Min Knowl Discov 1(3):241–258CrossRefGoogle Scholar
  17. 17.
    Aluç G, Hartig O, Özsu MT, Daudjee K (2014) Diversified stress testing of RDF data management systems. In: ISWC’14, pp 197–212Google Scholar
  18. 18.
    Gallego MA, Fernández JD, Martínez-Prieto MA, de la Fuente P (2011) An empirical study of real-world SPARQL queries. In: Proceedings of the USEWOD workshop co-located with WWW’11Google Scholar
  19. 19.
    Carothers G (ed) (2014) Rdf 1.1 n-quads. W3C RecommendationGoogle Scholar
  20. 20.
    Schreiber G, Raimond Y (eds) (2014) Rdf 1.1 primer. W3C recommendationGoogle Scholar
  21. 21.
    Sahoo SS, Nguyen V, Bodenreider O, Parikh P, Minning T, Sheth AP (2011) A unified framework for managing provenance information in translational research. BMC Bioinform 12:461CrossRefGoogle Scholar
  22. 22.
    Schueler B, Sizov S, Staab S, Tran DT (2008) Querying for meta knowledge. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 625–634Google Scholar
  23. 23.
    Straccia U, Lopes N, Lukacsy G, Polleres A (2010) A general framework for representing and reasoning with annotated semantic web data. In: AAAIGoogle Scholar
  24. 24.
    Galárraga L, Teflioudi C, Hose K, Suchanek FM (2015) Fast rule mining in ontological knowledge bases with AMIE+. VLDB J 24(6):707–730CrossRefGoogle Scholar
  25. 25.
    Campinas S (2014) Live SPARQL auto-completion. In: ISWC’14 (posters and demos), pp 477–480Google Scholar
  26. 26.
    Pham M, Passing L, Erling O, Boncz PA (2015) Deriving an emergent relational schema from RDF data. In: WWW’15, pp 864–874Google Scholar
  27. 27.
    Hurtado CA, Poulovassilis A, Wood PT (2009) Ranking approximate answers to semantic web queries. In: ESWC’09, pp 263–277Google Scholar
  28. 28.
    Huang H, Liu C, Zhou X (2012) Approximating query answering on RDF databases. J World Wide Web Internet Web Inf Syst (WWW) 15(1):89–114CrossRefGoogle Scholar
  29. 29.
    Fokou G, Jean S, Hadjali A (2014) Endowing semantic query languages with advanced relaxation capabilities. In: ISMIS’14, pp 512–517Google Scholar
  30. 30.
    Calí A, Frosini R, Poulovassilis A, Wood P (2014) Flexible querying for SPARQL. In: ODBASE’14, pp 473–490Google Scholar
  31. 31.
    Hogan A, Mellotte M, Powell G, Stampouli D (2012) Towards fuzzy query-relaxation for RDF. In: ESWC’12, pp 687–702Google Scholar
  32. 32.
    Elbassuoni S, Ramanath M, Weikum G (2011) Query relaxation for entity-relationship search. In: ESWC’11, pp 62–76Google Scholar
  33. 33.
    Dolog P, Stuckenschmidt H, Wache H, Diederich J (2009) Relaxing RDF queries based on user and domain preferences. J Intell Inf Syst (JIIS) 33(3):239–260CrossRefGoogle Scholar
  34. 34.
    Fokou G, Jean S, HadjAli A, Baron M (2016) RDF query relaxation strategies based on failure causes. In: ESWC’16, pp 439–454Google Scholar
  35. 35.
    Reddy KB, Kumar PS (2013) Efficient trust-based approximate SPARQL querying of the web of linked data. In: Uncertainty reasoning for the semantic web II. Springer, pp 315–330Google Scholar
  36. 36.
    Jannach D (2009) Fast computation of query relaxations for knowledge-based recommenders. AI Commun 22(4):235–248MathSciNetzbMATHGoogle Scholar
  37. 37.
    Pivert O, Smits G (2015) How to efficiently diagnose and repair fuzzy database queries that fail. In: Fifty years of fuzzy logic and its applications, studies in fuzziness and soft computing. Springer, pp 499–517Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Ibrahim Dellal
    • 1
  • Stéphane Jean
    • 1
    Email author
  • Allel Hadjali
    • 1
  • Brice Chardin
    • 1
  • Mickaël Baron
    • 1
  1. 1.LIAS/ISAE-ENSMAUniversity of PoitiersFuturoscope CedexFrance

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