Abstract
The lightweight description logic (DL-lite) represents one of the most important logic specially dedicated to applications that handle large volumes of data. Managing inconsistency issues, in order to effectively query inconsistent DL-Lite knowledge bases, is a topical issue. Since assertions (ABoxes) come from a variety of sources with varying degrees of reliability, there is confusion in hierarchical knowledge bases. As a consequence, the inclusion of new axioms is a main factor that causes inconsistency in this type of knowledge base. Often, it is too expensive to manually verify and validate all assertions. In this article, we study the problem of inconsistencies in the DL-Lite family and we propose a new algorithm to resolve the inconsistencies in prioritized knowledge bases. We carried out an experimental study to analyze and compare the results obtained by our proposed algorithm, in the framework of this work, and the main algorithms studied in the literature. The results obtained show that our algorithm is more productive than the others, compared to standard performance measures, namely precision, recall and F-measure.
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References
Artale, A., Calvanese, D., Kontchakov, R., Zakharyaschev, M.: The DL-lite family and relations. Comput. Res. Reposit. (CoRR) Volume abs/1401.3487 (2014)
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)
Hamdi, G., Omri, M.N., Benferhat, S., Bouraoui, Z., Papini, O.: Query answering DL-lite knowledge bases from hidden datasets. Ann. Math. Artif. Intell. 89, 271–299 (2021)
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 (2018)
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)
Bertossi, L.E.: Database Repairing and Consistent Query Answering, Morgan & Claypool Publishers, San Rafael (2011)
Benferhat, S., Dubois, D., Prade, H.: How to infer from inconsistent beliefs without revising? In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1449–1457, Morgan Kaufmann, Montréal Québec (1995)
Staworko, S., Chomicki, J., Marcinkowski, J.: Prioritized repairing and consistent query answering in relational databases. Ann. Math. Artif. Intell. 64, 209–246 (2012)
Du, J., Qi, G., Shen, Y.: Weight-based consistent query answering over inconsistent over inconsistent SHIQ knowledge base. Knowl. Inf. Syst. 34, 335–371 (2013)
Hamdi, G., Telli, A., Omri, M.N.: Querying of several DL-Lite knowledge bases from various information sources-based polynomial response unification approach. J. King Saud Univ. Comput. Inf. Sci. (2020)
Telli, A., Hamdi, G., Omri, M.N.: Lexicographic repair under querying prioritised dl-lite knowledge bases. Sci. J. King Faisal Univ. Basic Appl. Sci. 22 (2021)
Baral, C., Kraus, S., Minker, J.: Combining multiple knowledge bases. IEEE Trans. Knowl. Data Eng. 3, 208–220 (1991)
Benferhat, S., Bouraoui, Z., Tabia, K.: How to select one preferred assertional-based repair from inconsistent and prioritized DL-Lite knowledge bases? In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 1450–1456 (2015)
Telli, A., Benferhat, S., Bourahla, M., Bouraoui, Z., Tabia, K.: Polynomial algorithms for computing a single preferred assertional-based repair. Kunstliche Intelligenz 31, 15–30 (2017)
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)
Boughammoura, R., Omri, M., Hlaoua, L.: Information retrieval from deep web based on visual query interpretation. Int. J. Inf. Res. Rev. 2, 45–59 (2012)
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, Spain (2015). https://doi.org/10.1007/978-3-319-24132-6_20
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Hamdi, G., Omri, M.N. (2021). Towards New Model for Handling Inconsistency Issues in DL-Lite Knowledge Bases. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12924. Springer, Cham. https://doi.org/10.1007/978-3-030-86475-0_10
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