[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Query answering DL-lite knowledge bases from hidden datasets

  • Published:
Annals of Mathematics and Artificial Intelligence Aims and scope Submit manuscript

Abstract

Unifying access to data using structured knowledge is the main problem studied in ontology-based data access (OBDA). Data are often provided by several information sources, and this has led to a number of methods that merge them in order to get a unified point of view. Existing merging approaches assume that the content of datasets is known and available. However, in several applications, it might be impossible to know the full content of datasets beforehand. This paper investigates several query answering strategies from multiple datasets without knowing their content in advance. We study those strategies from different points of view: productivity, logical properties and computational complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Baader, F., Calvanese, D., McGuinness, D., Daniele, N., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)

    MATH  Google Scholar 

  2. Lenzerini, M.: Ontology-Based data management. In: Proceedings of the 6th Alberto Mendelzon International Workshop on Foundations of Data Management, vol. 866, pp 12–15. ACM, Glasgow (2011)

  3. Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. J. Data Semantics 4900, 133–173 (2008)

    MATH  Google Scholar 

  4. Rodriguez-Muro, M., Kontchakov, R., Zakharyaschev, M.: Ontology-based data access: Ontop of databases. In: 12Th International Semantic Web Conference, Volume 8218, pp 558–573. Springer, Sydney (2013)

  5. Benferhat, S., Bouraoui, Z., Lagrue, S., Rossit, J.: Min-based assertional merging approach for prioritized DL-lite knowledge bases. In: 8Th International Scalable Uncertainty Management Conference, vol. 8720, pp 8–21. Springer, Oxford (2014)

  6. Wang, Z., Wang, K., Jin, Y., Qi, G.: Ontomerge a system for merging DL-lite ontologies. In: CEUR Workshop Proceedings, vol. 969, pp 16–27 (2014)

  7. Benferhat, S., Bouraoui, Z., Chau, M., Lagrue, S., Rossit, J.: A polynomial algorithm for merging lightweight ontologies in possibility theory under incommensurability assumption. In: Proceedings of the 9th International Conference on Agents and Artificial Intelligence, vol. 2, pp 415–422. SciTe Press, Porto (2017)

  8. Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable Reasoning and Efficient Query Answering in Description logics: The DL-lite Family. J. Autom. Reason. 39, 385–429 (2007)

    Article  MathSciNet  Google Scholar 

  9. Baget, J.F., Benferhat, S., Bouraoui, Z., Croitoru, M., Mugnier, M., Papini, O., Rocher, S., Tabia, K.: Inconsistency-tolerant query answering: rationality properties and computational complexity analysis. In: 15Th European Conference of Logics in Artificial Intelligence, vol. 10021, pp 64–80. Larnaca, Cyprus (2016)

  10. Kraus, S., Lehmann, D.J., Magidor, M.: Nonmonotonic Reasoning, Preferential Models and Cumulative Logics, CoRR - Computing Research Repository, Volume cs.AI/0202021 (2002)

  11. Artale, A., Calvanese, D., Kontchakov, R., Zakharyaschev, M.: The DL-lite Family and Relations, coRR - Computing Research Repository, Volume arXiv:abs/1401.3487 (2014)

  12. Flouris, G., Huang, Z., Pan, J.Z., Plexousakis, D., Wache, H.: Inconsistencies, negations and changes in ontologies. In: Proceedings, the Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, pp 1295–1300. AAAI Press, Boston (2006)

  13. Nonfjall, H., Larsen, H.L.: Detection of potential inconsistencies in knowledge bases. Int. J. Intel. Syst. 7, 81–96 (1992)

    Article  Google Scholar 

  14. Calvanese, D., Kharlamov, E., Nutt, W., Zheleznyakov, D.: Evolution of DL-lite knowledge bases. In: 9Th International Semantic Web Conference, pp 112–128. Springer, Shanghai (2010)

  15. Hamdi, G., Omri, M.N., Papini, O., Benferhat, S., Bouraoui, Z.: Querying DL-lite knowledge bases from hidden datasets. In: International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida, USA (2018)

  16. Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence Journal 32, 57–95 (1987)

    Article  MathSciNet  Google Scholar 

  17. Benferhat, S., Cayrol, C., Dubois, D., Lang, J., Prade, H.: Inconsistency management and prioritized Syntax-Based entailment. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp 640–647. Morgan Kaufmann, Chambery (1993)

  18. Gardenfors, P., Makinson, D.: Nonmonotonic inference based on expectations. Artificial Intelligence Journal 65, 197–245 (1994)

    Article  MathSciNet  Google Scholar 

  19. Everaere, P., Konieczny, S., Marquis, P.: Disjunctive merging: Quota and Gmin merging operators. Artificial Intelligence Journal 174, 824–849 (2010)

    Article  MathSciNet  Google Scholar 

  20. Konieczny, S., Pérez, R.P.: On the frontier between arbitration and majority. In: Proceedings of the Eights International Conference on Principles and Knowledge Representation and Reasoning, pp 109–120. Toulouse, Morgan Kaufmann (2002)

  21. Jinxin, L., Mendelzon, A.O.: Knowledge base merging by majority. In: Dynamic Worlds from the Frame Problem to Knowledge Management, pp 195–218. Springer Netherlands, Toulouse (2002)

  22. Revesz, P.Z.: On the semantics of arbitration. Int. J. Autom. Comput. 7, 133–160 (1997)

    MathSciNet  MATH  Google Scholar 

  23. Baral, C., Kraus, S., Minker, J.: Combining multiple knowledge bases. IEEE Trans. Knowl. Data Eng. 3, 208–220 (1991)

    Article  Google Scholar 

  24. Bloch, I., Hunter, A., Appriou, A., Ayoun, A., Benferhat, S., Besnard, P., Cholvy, L., Cooke, R.M., Cuppens, F., Dubois, D., Fargier, H., Grabisch, M., Kruse, R., Lang, J., Moral, S., Prade, H., Saffiotti, A., Smets, P., Sossai, C.: Fusion: General concepts and characteristics. Int. J. Intel. Syst. 16, 1107–1134 (2001)

    Article  Google Scholar 

  25. Benferhat, S., Bouraoui, Z., Loukil, Z.: Min-Based fusion of possibilistic DL-lite knowledge bases. In: International Conferences on Web Intelligence, pp 23–28. IEEE Computer Society, Atlanta (2013)

  26. Moguillansky, M., Falappa, M.A.: A Non-Monotonic description logics model for merging terminologies, inteligencia artificial. Revista Iberoamericana de Inteligencia Artificial 11, 77–88 (2007)

    Google Scholar 

  27. Halevy, A.Y.: Answering queries using views: a survey. Very Large Data Bases Journal 10, 270–294 (2001)

    Article  Google Scholar 

  28. Pottinger, R., Halevy, A.Y.: Minicon: A scalable algorithm for answering queries using views. Very Large Data Bases Journal 10, 182–198 (2001)

    Article  Google Scholar 

  29. Pottinger, R., Levy, A.Y.: A scalable algorithm for answering queries using views. In: Proceedings of 26th International Conference on Very Large Data Bases, pp 484–495. Morgan Kaufmann, Cairo (2000)

  30. Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: Proceedings of the Eighteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp 68–79. ACM Press, Philadelphia (1999)

  31. Bertossi, L.E.: Database Repairing and Consistent Query Answering. Morgan & Claypool Publishers (2011)

  32. Chomicki, J.: Consistent query answering: five easy pieces. In: 11Th International Conference of Database Theory, pp 1–17. Springer, Barcelona (2007)

  33. Decker, H.: Basic causes for the inconsistency tolerance of query answering and integrity checking. In: Database and Expert Systems Applications, DEXA, International Workshops, pp 318–322. IEEE Computer Society, Bilbao (2010)

  34. ten Cate, B., Halpert, R.L., Kolaitis, P.G.: Practical query answering in data exchange under Inconsistency-Tolerant semantics. In: Proceedings of the 19th International Conference on Extending Database Technology, pp 233–244. OpenProceedings.org, Bordeaux (2016)

  35. Baral, C., Kraus, S., Minker, J., Subrahmanian, V.S.: Combining knowledge bases consisting of first order theories. In: 6Th International Symposium of Methodologies for Intelligent Systems ISMIS, pp 92–101. Springer, Charlotte (1991)

  36. Nebel, B.: Base revision operations and schemes: semantics, representation and complexity. In: ECAI, pp. 341–345 (1994)

  37. Benferhat, S., Dubois, D., Prade, H.: Some syntactic approaches to the handling of inconsistent knowledge bases: a comparative study part 1: the flat case. Stud. Logica. 58, 17–45 (1997)

    Article  Google Scholar 

  38. Lembo, D., Lenzerini, M., Rosati, R., Ruzzi, M., Savo, D.F.: Inconsistency-tolerant semantics for description logics. In: Fourth International Conference of Web Reasoning and Rule Systems RR, pp 103–117. Springer, Bressanone/Brixen (2010)

  39. Lembo, D., Lenzerini, M., Rosati, R., Ruzzi, M., Savo, D.F.: Inconsistency-tolerant query answering in ontology-based data access. J. Web Semantics 33, 3–29 (2015)

    Article  Google Scholar 

  40. Bienvenu, M., Rosati, R.: Tractable approximations of consistent query answering for robust ontology-based data access. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pp 775–781. IJCAI/AAAI, Beijing (2013)

  41. Benferhat, S., Bouraoui, Z., Croitoru, M., Papini, O., Tabia, K.: Non-Objection Inference for Inconsistency-Tolerant query answering. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence IJCAI, pp 3684–3690. IJCAI/AAAI Press, New York (2016)

  42. Baget, J., Benferhat, S., Bouraoui, Z., Croitoru, M., Mugnier, M., Papini, O., Rocher, S., Tabia, K.: A general Modifier-Based framework for Inconsistency-Tolerant query answering. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Fifteenth International Conference KR, pp 513–516. AAAI Press, Cape Town (2016)

  43. Grau, B.C., Motik, B.: Reasoning over Ontologies with Hidden content: The Import-by-Query Approach, coRR - Computing Research Repository. Volume arXiv:abs/1401.5853 (2014)

  44. Fokoue, A., Meneguzzi, F., Sensoy, M., Pan, J.Z.: Querying linked ontological data through distributed summarization. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence. AAAI Press, Toronto (2012)

  45. Boughammoura, R., Omri, M.N.: Querying deep web data bases without accessing to data. In: 13Th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp 597–603. IEEE, Guilin (2017)

  46. Boughammoura, R., Omri, M.N., Hlaoua, L.: Information retrieval from deep web based on visual query interpretation. Int. J. Inform. Res. Rev. IJIRR 2, 45–59 (2012)

    Google Scholar 

  47. Boughammoura, R., Hlaoua, L., Omri, M.N.: G-Form: a Collaborative Design Approach to Regard Deep Web Form as Galaxy of Concepts. In: 12Th International Conference of Cooperative Design, Visualization, and Engineering, pp 170–174. Springer, Mallorca (2015)

Download references

Acknowledgments

This work has received support from the European Project H2020 Marie Sklodowska-Curie Actions (MSCA), Research and Innovation Staff Exchange (RISE): Aniage project (High Dimensional Heterogeneous Data based Animation Techniques for Southeast Asian Intangible Cultural Heritage Digital Content), project number 691215. The third author also received support from the AAP A2U QUID (Querying heterogeneous Data) project.

Author information

Authors and Affiliations

  1. MARS Research Laboratory, University of Sousse, Sousse, Tunisia

    Ghassen Hamdi & Mohamed Nazih Omri

  2. CRIL., University of Artois, Lens, France

    Salem Benferhat & Zied Bouraoui

  3. Aix-Marseille Université, Université de Toulon, LIS, CNRS, Marseille, France

    Odile Papini

Authors
  1. Ghassen Hamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Nazih Omri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salem Benferhat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zied Bouraoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Odile Papini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ghassen Hamdi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hamdi, G., Omri, M.N., Benferhat, S. et al. Query answering DL-lite knowledge bases from hidden datasets. Ann Math Artif Intell 89, 271–299 (2021). https://doi.org/10.1007/s10472-020-09714-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10472-020-09714-2

Keywords

Mathematics Subject Classification (2010)

Search

Navigation