La logique DL-Lite possibiliste standard, issue de la logique des descriptions DL-Lite, permet de modéliser l’incertitude sur les données dans les ontologies formelles. Dans cet article, nous proposons une extension de la logique DL-Lite possibiliste pour le cas où les données incertaines sont en outre partiellement pré-ordonnées. Nous supposons que les axiomes de la base terminologique (TBox) sont complètement certains et ne peuvent être remis en cause en présence d’informations contradictoires. Cependant, les faits de la base assertionnelle (ABox) peuvent être incertains et peuvent être ignorés ou affaiblis s’ils sont inconsistants avec les axiomes de la TBox. Nous définissons une méthode efficace pour résoudre les inconsistances dans la base ABox, où l’incertitude est représentée par des poids symboliques, qui sont attachés aux assertions et ordonnés selon un ordre partiel. L’idée consiste à calculer une seule réparation pour la base ABox pondérée et partiellement pré-ordonnée. Pour ce faire, nous considérons toutes les extensions du pré-ordre partiel défini sur les assertions, ce qui génère autant de bases compatibles avec la base ABox initiale. Ensuite, nous calculons la réparation possibiliste associée à chacune des bases compatibles. Enfin, nous obtenons une seule réparation pour la base initiale à partir de l’intersection des réparations possibilistes. Nous établissons les propriétés calculatoires intéressantes de notre méthode grâce à une caractérisation équivalente basée sur la notion d’assertions -acceptées. En substance, il s’agit des assertions strictement prioritaires à au moins une assertion de chaque conflit de la base ABox initiale. Nous montrons que le calcul de la réparation possibiliste d’une ontologie DL-Lite partiellement pré-ordonnée s’effectue en un temps polynomial par rapport à la taille de la base ABox.
Standard possibilistic DL-Lite, derived from the lightweight Description Logic DL-Lite, allows to represent uncertainty over data pieces in formal ontologies. In this paper, we propose an extension of standard possibilistic DL-Lite to the case where the data is uncertain but also partially preordered. We assume that the axioms of the terminological base (TBox) are fully certain and that they cannot be questioned when the available information is contradictory. However, the ground facts of the assertional base (ABox) may be uncertain and may be ignored or weakened if they are inconsistent with the TBox axioms. We define a tractable method for resolving inconsistency in the ABox, where uncertainty is represented by symbolic weights that are attached to the assertions and ordered according to a partial order. The idea consists in computing a single repair for the weighted and partially preordered ABox. To achieve this, we consider all the extensions of the partial preorder defined over the assertions, which yields as many compatible ABoxes for the initial ABox. Then, we compute the possibilistic repair associated with each one of the compatible ABoxes. Finally, we obtain a single repair for the initial ABox from the intersection of all the possibilistic repairs. We establish the compelling computational properties of our method thanks to an equivalent characterization based on the notion of -accepted assertions. In essence, these are assertions with a priority level that is strictly higher than the priority level of at least one assertion of each conflict in the initial ABox. We show that the computation of the possibilistic repair of a partially preordered DL-Lite ontology can be performed in polynomial time in the size of the ABox.
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Keywords: DL-Lite Ontology, Inconsistent Knowledge Base, Possibility Theory.
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@article{ROIA_2022__3_3-4_373_0,
author = {Sihem Belabbes and Salem Benferhat},
title = {Une extension possibiliste pour les ontologies {DL-Lite} inconsistantes partiellement pr\'e-ordonn\'ees},
journal = {Revue Ouverte d'Intelligence Artificielle},
pages = {373--391},
publisher = {Association pour la diffusion de la recherche francophone en intelligence artificielle},
volume = {3},
number = {3-4},
year = {2022},
doi = {10.5802/roia.35},
language = {fr},
url = {https://roia.centre-mersenne.org/articles/10.5802/roia.35/}
}
TY - JOUR AU - Sihem Belabbes AU - Salem Benferhat TI - Une extension possibiliste pour les ontologies DL-Lite inconsistantes partiellement pré-ordonnées JO - Revue Ouverte d'Intelligence Artificielle PY - 2022 SP - 373 EP - 391 VL - 3 IS - 3-4 PB - Association pour la diffusion de la recherche francophone en intelligence artificielle UR - https://roia.centre-mersenne.org/articles/10.5802/roia.35/ DO - 10.5802/roia.35 LA - fr ID - ROIA_2022__3_3-4_373_0 ER -
%0 Journal Article %A Sihem Belabbes %A Salem Benferhat %T Une extension possibiliste pour les ontologies DL-Lite inconsistantes partiellement pré-ordonnées %J Revue Ouverte d'Intelligence Artificielle %D 2022 %P 373-391 %V 3 %N 3-4 %I Association pour la diffusion de la recherche francophone en intelligence artificielle %U https://roia.centre-mersenne.org/articles/10.5802/roia.35/ %R 10.5802/roia.35 %G fr %F ROIA_2022__3_3-4_373_0
Sihem Belabbes; Salem Benferhat. Une extension possibiliste pour les ontologies DL-Lite inconsistantes partiellement pré-ordonnées. Revue Ouverte d'Intelligence Artificielle, Volume 3 (2022) no. 3-4, pp. 373-391. doi : 10.5802/roia.35. https://roia.centre-mersenne.org/articles/10.5802/roia.35/
[1] The Complexity of the Consistency Problem in the Probabilistic Description Logic , 12th International Symposium on Frontiers of Combining Systems (FroCoS), London, UK (2019), pp. 167-184 | DOI | Zbl
[2] A General Modifier-Based Framework for Inconsistency-Tolerant Query Answering, Principles of Knowledge Representation and Reasoning (KR), Cape Town, South Africa (2016), pp. 513-516
[3] On Dealing with Conflicting, Uncertain and Partially Ordered Ontologies, 33rd International Florida Artificial Intelligence Research Society Conference, (FLAIRS), North Miami Beach, USA (2020), pp. 9-14
[4] Ontologies légères inconsistantes partiellement pré-ordonnées en théorie des possibilités, Conférence Nationale en Intelligence Artificielle, CNIA 2020, Annual French AI Conference, Angers, France, June 29 - July 1, 2020 (2020), pp. 6-13
[5] Computing a Possibility Theory Repair for Partially Preordered Inconsistent Ontologies, IEEE Transactions on Fuzzy Systems (2021), pp. 1-10 | DOI
[6] Elect : An Inconsistency Handling Approach for Partially Preordered Lightweight Ontologies, Logic Programming and Nonmonotonic Reasoning (LPNMR), Philadelphia, USA (2019), pp. 210-223 | DOI | Zbl
[7] Handling inconsistency in partially preordered ontologies : the Elect method, Journal of Logic and Computing, Volume 31 (2021) no. 5, pp. 1356-1388 | DOI | MR | Zbl
[8] Min-based possibilistic DL-Lite, Journal of Logic and Computation, Volume 27 (2017) no. 1, pp. 261-297 | DOI | MR | Zbl
[9] How to Select One Preferred Assertional-Based Repair from Inconsistent and Prioritized DL-Lite Knowledge Bases ?, International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina (2015), pp. 1450-1456
[10] How to Infer from Inconsistent Beliefs without Revising ?, International Joint Conference on Artificial Intelligence (IJCAI) (1995), pp. 1449-1457
[11] Reasoning with partially ordered information in a possibilistic logic framework, Fuzzy Sets and Systems, Volume 144 (2004) no. 1, pp. 25-41 | DOI | MR | Zbl
[12] Inconsistency-Tolerant Querying of Description Logic Knowledge Bases, Reasoning Web : Logical Foundation of Knowledge Graph Construction and Query Answering, Volume 9885 (2016), pp. 156-202 | DOI | Zbl
[13] Querying Inconsistent Description Logic Knowledge Bases under Preferred Repair Semantics, AAAI (2014), pp. 996-1002
[14] Query-Driven Repairing of Inconsistent DL-Lite Knowledge Bases, IJCAI, New York, USA (2016), pp. 957-964
[15] Computing and Explaining Query Answers over Inconsistent DL-Lite Knowledge Bases, Journal of Artificial Intelligence Research, Volume 64 (2019), pp. 563-644 | DOI | MR | Zbl
[16] Reasoning within Fuzzy OWL 2 EL revisited, Fuzzy Sets and Systems, Volume 351 (2018), pp. 1-40 | DOI | MR | Zbl
[17] Recent Advances in Querying Probabilistic Knowledge Bases, 27th International Joint Conference on Artificial Intelligence, (IJCAI), Stockholm, Sweden (2018), pp. 5420-5426 | DOI
[18] Fuzzy Description Logics – A Survey, Scalable Uncertainty Management (SUM) (2017), pp. 31-45 | DOI
[19] Tractable Reasoning and Efficient Query Answering in Description Logics : The DL-Lite Family, Journal of Automated Reasoning, Volume 39 (2007) no. 3, pp. 385-429 | DOI | MR | Zbl
[20] Evolution of DL-Lite Knowledge Bases, International Semantic Web Conference (1) (2010), pp. 112-128 | DOI
[21] Possibilistic uncertainty and fuzzy features in description logic. A preliminary discussion, Fuzzy Logic and the Semantic Web. Volume 1 of Capturing Intelligence (2006), pp. 101-113 | DOI
[22] Possibility Theory and Its Applications : Where Do We Stand ?, Springer Handbook of Computational Intelligence, Springer, Berlin, Heidelberg, 2015, pp. 31-60 | DOI
[23] Generalized possibilistic logic : Foundations and applications to qualitative reasoning about uncertainty, Artificial Intelligence, Volume 252 (2017), pp. 139-174 | DOI | MR | Zbl
[24] Advances in Weighted Logics for Artificial Intelligence, International Journal of Approximate Reasoning, Volume 88 (2017), pp. 385-386 | DOI | MR | Zbl
[25] An Alternative Proof Method for Possibilistic Logic and its Application to Terminological Logics, International Journal of Approximate Reasoning, Volume 12 (1995) no. 2, pp. 85-109 | DOI | MR | Zbl
[26] The Combined Approach to Query Answering in DL-Lite, 12th International Conference on Principles of Knowledge Representation and Reasoning (KR), Toronto, Canada (2010), pp. 247-257
[27] Inconsistency-Tolerant Semantics for Description Logics, Web Reasoning and Rule Systems - Fourth International Conference, RR 2010, Bressanone/Brixen, Italy (2010), pp. 103-117 | DOI
[28] Inconsistency Handling in Datalog+/- Ontologies, ECAI, Montpellier, France (2012), pp. 558-563 | Zbl
[29] Probabilistic Description Logics for Subjective Uncertainty, 12th International Conference on Principles of Knowledge Representation and Reasoning (KR), Toronto, Canada (2010)
[30] OWL 2 Web Ontology Language Profiles. W3C Recommendation, 11 December 2012. Available at https://www.w3.org/TR/owl2-profiles/
[31] Expressive Querying over Fuzzy DL-Lite Ontologies, 20th DL workshop, Bressanone, Italy (2007)
[32] Extending description logics with uncertainty reasoning in possibilistic logic, International Journal of Intelligent Systems, Volume 26 (2011) no. 4, pp. 353-381 | DOI | Zbl
[33] Towards Top-k Query Answering in Description Logics : The Case of DL-Lite, 10th European Conference on Logics in Artificial Intelligence (JELIA), Liverpool, UK (2006), pp. 439-451 | DOI | Zbl
[34] Foundations of Fuzzy Logic and Semantic Web Languages, Chapman & Hall/CRC, 2013
[35] Polynomial Algorithms for Computing a Single Preferred Assertional-Based Repair, KI, Volume 31 (2017) no. 1, pp. 15-30 | DOI
[36] Possibilistic reasoning with partially ordered beliefs, Journal of Applied Logic, Volume 13 (2015) no. 4, pp. 770-798 | DOI | MR | Zbl
[37] Querying Expressive DL Ontologies under the ICAR Semantics, 31st DL workshop, Tempe, USA (2018)
[38] Query Rewriting for DL Ontologies Under the ICAR Semantics, Rules and Reasoning - Third International Joint Conference, RuleML+RR, Bolzano, Italy (2019), pp. 144-158 | DOI
[39] Efficient Query Answering over Expressive Inconsistent Description Logics, IJCAI, New York, USA (2016), pp. 1279-1285
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