Academic literature on the topic 'Non-monotonic Description Logic JClassicδє'
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Journal articles on the topic "Non-monotonic Description Logic JClassicδє"
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Giordano, L., V. Gliozzi, N. Olivetti, and G. L. Pozzato. "A non-monotonic Description Logic for reasoning about typicality." Artificial Intelligence 195 (February 2013): 165–202. http://dx.doi.org/10.1016/j.artint.2012.10.004.
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Di Noia, T., E. Di Sciascio, and F. M. Donini. "Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach." Journal of Artificial Intelligence Research 29 (July 12, 2007): 269–307. http://dx.doi.org/10.1613/jair.2153.
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Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request. We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics. We devise Concept Abduction and Concept Contraction as non-monotonic inferences in Description Logics suitable for modeling matchmaking in a logical framework, and prove some related complexity results. We also present reasonable algorithms for semantic matchmaking based on the devised inferences, and prove that they obey to some commonsense properties. Finally, we report on the implementation of the proposed matchmaking framework, which has been used both as a mediator in e-marketplaces and for semantic web services discovery.
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NICOLAS, PASCAL, FRÉDÉRIC SAUBION, and IGOR STÉPHAN. "HEURISTICS FOR A DEFAULT LOGIC REASONING SYSTEM." International Journal on Artificial Intelligence Tools 10, no. 04 (2001): 503–23. http://dx.doi.org/10.1142/s0218213001000635.
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In Artificial Intelligence, Default Logic is recognized as a powerful framework for knowledge representation when one has to deal with incomplete information. Its expressive power is suitable for non monotonic reasoning, but the counterpart is its very high level of theoretical complexity. Today, some operational systems are able to deal with real world applications. However, finding a default logic extension in a practical way is not yet possible in whole generality. This paper which is an extended version of18 shows how heuristics such as Genetic Algorithms and Local Search techniques can be used and combined to build an automated default reasoning system. We give a general description of the required basic components and we exhibit experimental results.
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Sokolov, I. A. "Theory and practice in artificial intelligence." Вестник Российской академии наук 89, no. 4 (2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.
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Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.
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Lukumbuzya, Sanja, Magdalena Ortiz, and Mantas Šimkus. "Resilient Logic Programs: Answer Set Programs Challenged by Ontologies." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2917–24. http://dx.doi.org/10.1609/aaai.v34i03.5683.
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We introduce resilient logic programs (RLPs) that couple a non-monotonic logic program and a first-order (FO) theory or description logic (DL) ontology. Unlike previous hybrid languages, where the interaction between the program and the theory is limited to consistency or query entailment tests, in RLPs answer sets must be ‘resilient’ to the models of the theory, allowing non-output predicates of the program to respond differently to different models. RLPs can elegantly express ∃∀∃-QBFs, disjunctive ASP, and configuration problems under incompleteness of information. RLPs are decidable when a couple of natural assumptions are made: (i) satisfiability of FO theories in the presence of closed predicates is decidable, and (ii) rules are safe in the style of the well-known DL-safeness. We further show that a large fragment of such RLPs can be translated into standard (disjunctive) ASP, for which efficient implementations exist. For RLPs with theories expressed in DLs, we use a novel relaxation of safeness that safeguards rules via predicates whose extensions can be inferred to have a finite bound. We present several complexity results for the case where ontologies are written in some standard DLs.
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Ma, Z. M., Fu Zhang, Hailong Wang, and Li Yan. "An overview of fuzzy Description Logics for the Semantic Web." Knowledge Engineering Review 28, no. 1 (2012): 1–34. http://dx.doi.org/10.1017/s0269888912000306.
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AbstractInformation imprecision and uncertainty exist in many real world applications, and such information would be retrieved, processed, shared, reused, and aligned in the maximum automatic way possible. As a popular family of formally well-founded and decidable knowledge representation languages, fuzzy Description Logics (fuzzy DLs), which extend DLs with fuzzy logic, are very well suited to cover for representing and reasoning with imprecision and uncertainty. Thus, a requirement naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web, because DLs are the logical foundation of the Semantic Web. Currently, there have been lots of fuzzy extensions of DLs with Zadeh's fuzzy logic theory papers published, to investigate fuzzy DLs and more importantly serve as identifying the direction of fuzzy DLs study. In this paper, we aim at providing a comprehensive literature overview of fuzzy DLs, and we focus our attention on fuzzy extensions of DLs based on fuzzy set theory. Other relevant formalisms that are based on approaches like probabilistic theory or non-monotonic logics are covered elsewhere. In detail, we first introduce the existing fuzzy DLs (including the syntax, semantics, knowledge base, and reasoning algorithm) from the origin, development (from weaker to stronger in expressive power), some special techniques, and so on. Then, the other important issues on fuzzy DLs, such as reasoning, querying, applications, and directions for future research, are also discussed in detail. Also, we make a comparison and analysis.
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Eiter, Thomas, Michael Fink, and Daria Stepanova. "Computing Repairs of Inconsistent DL-Programs over EL Ontologies." Journal of Artificial Intelligence Research 56 (July 27, 2016): 463–515. http://dx.doi.org/10.1613/jair.5047.
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Description Logic (DL) ontologies and non-monotonic rules are two prominent Knowledge Representation (KR) formalisms with complementary features that are essential for various applications. Nonmonotonic Description Logic (DL) programs combine these formalisms thus providing support for rule-based reasoning on top of DL ontologies using a well-defined query interface represented by so-called DL-atoms. Unfortunately, interaction of the rules and the ontology may incur inconsistencies such that a DL-program lacks answer sets (i.e., models), and thus yields no information. This issue is addressed by recently defined repair answer sets, for computing which an effective practical algorithm was proposed for DL-Lite A ontologies that reduces a repair computation to constraint matching based on so-called support sets. However, the algorithm exploits particular features of DL-Lite A and can not be readily applied to repairing DL-programs over other prominent DLs like EL. compared to DL-Lite A , in EL support sets may neither be small nor only few support sets might exist, and completeness of the algorithm may need to be given up when the support information is bounded. We thus provide an approach for computing repairs for DL-programs over EL ontologies based on partial (incomplete) support families. The latter are constructed using datalog query rewriting techniques as well as ontology approximation based on logical difference between EL-terminologies. We show how the maximal size and number of support sets for a given DL-atom can be estimated by analyzing the properties of a support hypergraph, which characterizes a relevant set of TBox axioms needed for query derivation. We present a declarative implementation of the repair approach and experimentally evaluate it on a set of benchmark problems; the promising results witness practical feasibility of our repair approach.
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Sridharan, Mohan, Michael Gelfond, Shiqi Zhang, and Jeremy Wyatt. "REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics." Journal of Artificial Intelligence Research 65 (June 17, 2019): 87–180. http://dx.doi.org/10.1613/jair.1.11524.
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This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granularity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the domain's transition diagrams, with the fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse-resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. The zoomed fine-resolution system description, and a probabilistic representation of the uncertainty in sensing and actuation, are used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions. The fine-resolution outcomes of executing these concrete actions are used to infer coarse-resolution outcomes that are added to the coarse-resolution history and used for subsequent coarse-resolution reasoning. The architecture thus combines the complementary strengths of declarative programming and probabilistic graphical models to represent and reason with non-monotonic logic-based and probabilistic descriptions of uncertainty and incomplete domain knowledge. In addition, we describe a general methodology for the design of software components of a robot based on these knowledge representation and reasoning tools, and provide a path for proving the correctness of these components. The architecture is evaluated in simulation and on a mobile robot finding and moving target objects to desired locations in indoor domains, to show that the architecture supports reliable and efficient reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.
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Nadjah, Chergui, and Boustia Narhimene. "Using Vulnerability to Reduce False Positive Rate in Intrusion Detection Systems." February 2, 2016. https://doi.org/10.5281/zenodo.1112091.
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Intrusion Detection Systems are an essential tool for network security infrastructure. However, IDSs have a serious problem which is the generating of massive number of alerts, most of them are false positive ones which can hide true alerts and make the analyst confused to analyze the right alerts for report the true attacks. The purpose behind this paper is to present a formalism model to perform correlation engine by the reduction of false positive alerts basing on vulnerability contextual information. For that, we propose a formalism model based on non-monotonic JClassicδє description logic augmented with a default (δ) and an exception (є) operator that allows a dynamic inference according to contextual information.
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"Knowledge Representation of Highly Dynamic Ontologies Using Defeasible Logic." International Journal of Advanced Trends in Computer Science and Engineering 10, no. 2 (2021): 948–54. http://dx.doi.org/10.30534/ijatcse/2021/661022021.
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Description logic gives us the ability of reasoning with acceptable computational complexity with retaining the power of expressiveness. The power of description logic can be accompanied by the defeasible logic to manage non-monotonic reasoning. In some domains, we need flexible reasoning and knowledge representation to deal the dynamicity of such domains. In this paper, we present a DL representation for a small domain that describes the connections between different entities in a university publication system to show how could we deal with changeability in domain rules. An automated support can be provided on the basis of defeasible logical rules to represent the typicality in the knowledge base and to solve the conflicts that might happen.
Dissertations / Theses on the topic "Non-monotonic Description Logic JClassicδє"
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Knorr, Matthias. "Combining open and closed world reasoning for the semantic web." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/6702.
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Dissertação para obtenção do Grau de Doutor
em Informática<br>One important problem in the ongoing standardization of knowledge representation
languages for the Semantic Web is combining open world ontology languages, such as the OWL-based ones, and closed world rule-based languages.
The main difficulty of such a combination is that both formalisms are quite orthogonal w.r.t. expressiveness and how decidability is achieved. Combining non-monotonic rules and ontologies is thus a challenging task
that requires careful balancing between expressiveness of the knowledge representation language and the computational complexity of reasoning.
In this thesis, we will argue in favor of a combination of ontologies and nonmonotonic
rules that tightly integrates the two formalisms involved, that has a computational complexity that is as low as possible, and that allows us to query for information instead of calculating the whole model. As our starting point we choose the mature approach of hybrid MKNF knowledge
bases, which is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. We extend the two-valued framework of MKNF logics to a three-valued logics, and we propose a well-founded semantics for non-disjunctive hybrid MKNF knowledge bases. This new semantics promises to provide better efficiency of reasoning,and it is faithful w.r.t. the original two-valued MKNF semantics and compatible with both the OWL-based semantics and the traditional Well-
Founded Semantics for logic programs. We provide an algorithm based on operators to compute the unique model, and we extend SLG resolution with tabling to a general framework that allows us to query a combination of non-monotonic rules and any given ontology language. Finally, we
investigate concrete instances of that procedure w.r.t. three tractable ontology
languages, namely the three description logics underlying the OWL 2 pro les.<br>Fundação para a Ciência e Tecnologia - grant contract SFRH/BD/28745/2006
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Sengupta, Kunal. "A Language for Inconsistency-Tolerant Ontology Mapping." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1441044183.
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Pensel, Maximilian. "A Lightweight Defeasible Description Logic in Depth: Quantification in Rational Reasoning and Beyond." 2019. https://tud.qucosa.de/id/qucosa%3A36341.
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Description Logics (DLs) are increasingly successful knowledge representation formalisms, useful for any application requiring implicit derivation of knowledge from explicitly known facts.
A prominent example domain benefiting from these formalisms since the 1990s is the biomedical field.
This area contributes an intangible amount of facts and relations between low- and high-level concepts such as the constitution of cells or interactions between studied illnesses, their symptoms and remedies.
DLs are well-suited for handling large formal knowledge repositories and computing inferable coherences throughout such data, relying on their well-founded first-order semantics.
In particular, DLs of reduced expressivity have proven a tremendous worth for handling large ontologies due to their computational tractability.
In spite of these assets and prevailing influence, classical DLs are not well-suited to adequately model some of the most intuitive forms of reasoning.
The capability for abductive reasoning is imperative for any field subjected to incomplete knowledge and the motivation to complete it with typical expectations.
When such default expectations receive contradicting evidence, an abductive formalism is able to retract previously drawn, conflicting conclusions.
Common examples often include human reasoning or a default characterisation of properties in biology, such as the normal arrangement of organs in the human body.
Treatment of such defeasible knowledge must be aware of exceptional cases - such as a human suffering from the congenital condition situs inversus - and therefore accommodate for the ability to retract defeasible conclusions in a non-monotonic fashion.
Specifically tailored non-monotonic semantics have been continuously investigated for DLs in the past 30 years.
A particularly promising approach, is rooted in the research by Kraus, Lehmann and Magidor for preferential (propositional) logics and Rational Closure (RC).
The biggest advantages of RC are its well-behaviour in terms of formal inference postulates and the efficient computation of defeasible entailments, by relying on a tractable reduction to classical reasoning in the underlying formalism.
A major contribution of this work is a reorganisation of the core of this reasoning method, into an abstract framework formalisation.
This framework is then easily instantiated to provide the reduction method for RC in DLs as well as more advanced closure operators, such as Relevant or Lexicographic Closure.
In spite of their practical aptitude, we discovered that all reduction approaches fail to provide any defeasible conclusions for elements that only occur in the relational neighbourhood of the inspected elements.
More explicitly, a distinguishing advantage of DLs over propositional logic is the capability to model binary relations and describe aspects of a related concept in terms of existential and universal quantification.
Previous approaches to RC (and more advanced closures) are not able to derive typical behaviour for the concepts that occur within such quantification.
The main contribution of this work is to introduce stronger semantics for the lightweight DL EL_bot with the capability to infer the expected entailments, while maintaining a close relation to the reduction method.
We achieve this by introducing a new kind of first-order interpretation that allocates defeasible information on its elements directly.
This allows to compare the level of typicality of such interpretations in terms of defeasible information satisfied at elements in the relational neighbourhood.
A typicality preference relation then provides the means to single out those sets of models with maximal typicality.
Based on this notion, we introduce two types of nested rational semantics, a sceptical and a selective variant, each capable of deriving the missing entailments under RC for arbitrarily nested quantified concepts.
As a proof of versatility for our new semantics, we also show that the stronger Relevant Closure, can be imbued with typical information in the successors of binary relations.
An extensive investigation into the computational complexity of our new semantics shows that the sceptical nested variant comes at considerable additional effort, while the selective semantics reside in the complexity of classical reasoning in the underlying DL, which remains tractable in our case.
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Malenko, Jaromír. "Usuzování v deskriptivní logice." Doctoral thesis, 2013. http://www.nusl.cz/ntk/nusl-326600.
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Title: Reasoning in Description Logics Author: Mgr. Jaromír Malenko Department: Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University in Prague Supervisor: Prof. RNDr. Petr Štěpánek, DrSc.; Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University in Prague Keywords: Description logic, Reasoner, Cartesian product, Non-monotonic reasoning Abstract: We deal with several aspects of reasoning in Description Logics. First, since description logic (DL) is a subset of First Order Logic (FOL), we use a FOL reasoner to reason in DL. We implemented dl2fol, a DL reasoner that takes an ontology (a DL theory with rules), translates it into a FOL theory, passes this set of formulae to an underling FOL reasoner, and interprets the result in terms of given ontology. This is an effective method for reasoning with newly introduced language constructors. However, we observed longer running times and that satisfiability of some DL concepts wasn't proved due to FOL undecidability. Second, we extend two DLs by introducing new language construct: cartesian product (CP) of concepts and roles. This allows for expressing relationships, that are not expressible by other means in weaker DLs. We...
Book chapters on the topic "Non-monotonic Description Logic JClassicδє"
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Wang, Geng, and Zuoquan Lin. "An Argumentation Framework for Non-monotonic Reasoning in Description Logic." In Rough Sets and Knowledge Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31900-6_26.
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Guebaili-Djider, Ratiba, Aicha Mokhtari, Farid Nouioua, Narhimene Boustia, and Karima Akli Astouati. "New Ontological Alignment System Based on a Non-monotonic Description Logic." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01863-8_2.
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Knorr Matthias, Hitzler Pascal, and Maier Frederick. "Reconciling OWL and Non-monotonic Rules for the Semantic Web." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-098-7-474.
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We propose a description logic extending SROIQ (the description logic underlying OWL 2 DL) and at the same time encompassing some of the most prominent monotonic and nonmonotonic rule languages, in particular Datalog extended with the answer set semantics. Our proposal could be considered a substantial contribution towards fulfilling the quest for a unifying logic for the Semantic Web. As a case in point, two non-monotonic extensions of description logics considered to be of distinct expressiveness until now are covered in our proposal. In contrast to earlier such proposals, our language has the &ldquo;look and feel&rdquo; of a description logic and avoids hybrid or first-order syntaxes.
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Guebaili_Djider Ratiba, Mokhtari Aicha, and Boustia Narhimene. "OWL." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-105-2-480.
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OWL&delta;&epsiv;is an ontological language for the semantic web extended by two operators &ldquo;Default(C)&rdquo; and &ldquo;Exception(C)&rdquo;. The aim of this extension is to bring out the typical aspect of concepts and their exceptions. This language is based on a non monotonic description logic named ExtDL&delta;&epsiv;.
Conference papers on the topic "Non-monotonic Description Logic JClassicδє"
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Lutz, Carsten, Quentin Manière, and Robin Nolte. "Querying Circumscribed Description Logic Knowledge Bases." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/47.
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Circumscription is one of the main approaches for defining non-monotonic description logics (DLs) and the decidability and complexity of traditional reasoning tasks, such as satisfiability of circumscribed DL knowledge bases (KBs) are well understood. For evaluating conjunctive queries (CQs) and unions thereof (UCQs), in contrast, not even decidability had been established. In this paper, we prove decidability of (U)CQ evaluation on circumscribed DL KBs and obtain a rather complete picture of both the combined complexity and the data complexity, for DLs ranging from ALCHIO via EL to various versions of DL-Lite. We also study the much simpler atomic queries (AQs).
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Gogacz, Tomasz, Sanja Lukumbuzya, Magdalena Ortiz, and Mantas Šimkus. "Datalog Rewritability and Data Complexity of ALCHOIF with Closed Predicates." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/44.
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We study the relative expressiveness of ontology-mediated queries (OMQs) formulated in the expressive Description Logic ALCHOIF extended with closed predicates. In particular, we present a polynomial-time translation from OMQs into Datalog with negation under the stable model semantics, the formalism that underlies Answer Set Programming. This is a novel and non-trivial result: the considered OMQs are not only non-monotonic but also feature a tricky combination of nominals, inverse roles, and role functionality. We start with atomic queries and then lift our approach to a large class of first-order queries where quantification is “guarded” by closed predicates. Our translation is based on a characterization of the query answering problem via integer programming, and a specially crafted program in Datalog with negation that finds solutions to dynamically generated systems of integer inequalities. As an important by-product of our translation, we get that the query answering problem is co-NP-complete in data complexity for the considered class of OMQs. Thus, answering these OMQs in the presence of closed predicates is not harder than answering them in the standard setting. This is not obvious as closed predicates are known to increase data complexity for some existing ontology languages.
Reports on the topic "Non-monotonic Description Logic JClassicδє"
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Bonatti, Piero, Carsten Lutz, and Frank Wolter. Expressive Non-Monotonic Description Logics Based on Circumscription. Technische Universität Dresden, 2005. http://dx.doi.org/10.25368/2022.149.
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Recent applications of description logics (DLs) strongly suggest the integration of non-monotonic features into DLs, with particular attention to defeasible inheritance. However, the existing non-monotonic extensions of DLs are usually based on default logic or autoepistemic logic, and have to be seriously restricted in expressive power to preserve the decidability of reasoning. In particular, such DLs allow the modelling of defeasible inheritance only in a very restricted form, where non-monotonic reasoning is limited to individuals that are explicitly identified by constants in the knowledge base. In this paper, we consider non-monotonic extensions of expressive DLs based on circumscription. We prove that reasoning in such DLs is decidable even without the usual, strong restrictions in expressive power. We pinpoint the exact computational complexity of reasoning as complete for NPNEXP and NEXPNP, depending on whether or not the number of minimized and fixed predicates is assumed to be bounded by a constant. These results assume that only concept names (and no role names) can be minimized and fixed during minimization. On the other hand, we show that fixing role names during minimization makes reasoning undecidable.