Minimally inconsistent reasoning in Semantic Web

Reasoning within expressive fuzzy rough description logics

Ontology description can use Description Logic(DL) language, so DL's reasoning service can be used in ontology reasoning. In each kind DL, there is a corresponding tableau algorithm to do the reasoning process. There are some sophisticated software to do DL reasoning, they using tree with conflict to decide whether a problem can be using satisfiability to solve. There are a lot of optimization methods to optimize the reasoning service, their attention on the tree structure with conflict. We propose a new algorithm, Heuristic Skipping Rules Algorithm(HSRA), to simply the rule set called by tableau algorithms, and experimentally proved the effectiveness of the HSRA.

In Semantic Web, the knowledge sources usually contain inconsistency because they are constantly changing and from different view points. As is well known, as based on the description logic, OWL is lack of the ability of tolerating inconsistent or incomplete data. Recently, the research in handling inconsistency in OWL becomes more and more important. In this paper, we present a paraconsistent OWL called quasi-classical OWL to handle inconsistency with holding important inference rules such as modus tollens, modus ponens, and disjunctive syllogism. We propose a terminable, sound and complete tableau algorithm to implement paraconsistent reasoning in quasi-classical OWL. In comparison with other approaches to handle inconsistency in OWL, our approach enhances the ability of reasoning by integrating paraconsistent reasoning with important classical inference rules.

The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition.We start by investigating a notion ofdefeasible subsumptionin the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for bothpreferentialandrationalsubsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level ofentailmentand present an algorithm for the computation ofrational closureof a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DLALC.

Bipolarity is an important feature of spatial information, involved in the expression of preferences and constraints about spatial positioning or in pairs of opposite spatial relations such as left and right. Another important feature is imprecision which has to be taken into account to model vagueness, inherent to many spatial relations (as for instance vague expressions such as close to, to the right of), and to gain in robustness in the representations. In previous works, we have shown that fuzzy sets and fuzzy mathematical morphology are appropriate frameworks, on the one hand, to represent bipolarity and imprecision of spatial relations and, on the other hand, to combine qualitative and quantitative reasoning in description logics extended with fuzzy concrete domains. The purpose of this paper is to integrate the bipolarity feature in the latter logical framework based on bipolar and fuzzy mathematical morphology and description logics with fuzzy concrete domains. Two important issues are addressed in this paper: the modeling of the bipolarity of spatial relations at the terminological level and the integration of bipolar notions in fuzzy description logics. At last, we illustrate the potential of the proposed formalism for spatial reasoning on a simple example in brain imaging.

Reasoning in Description Logics (DLs) can detect inconsistencies between trusted knowledge and not trusted sources. The proposed method is exemplified on fake news for Covid19. Machine learning is used to generate DL axioms from positive and negative examples using tools such as DL-Learner. The resulted knowledge graph formalised in DL is merged with the trusted ontologies on Covid-19. Reasoning in DL is then performed with the Racer engine, which is responsible to detect inconsistencies within the ontology. When detecting inconsistencies, a "red flag" is raised to signal possible fake news and the corresponding counterspeech is generated.

We propose a formal specification framework for functional aspects of services. We define services as operations which are specified by means of pre- and postconditions, for the specification of which we use extensions of description logic. The (extensions of) description logic and the specification framework itself are defined as institutions. This gives the framework a uniformity of definition and a solid algebraic and logical foundation. The framework can be used for the specification of service requests and service providers. Given a signature morphism from request to provider, we define when a service request is matched by a service provider, which can be used in service discovery. We provide a model-theoretic definition of matching and show that matching can be characterized by a semantic entailment relation which is formulated over a particular standard description logic. Thus proofs of matching can be reduced to standard reasoning in description logic for which one can use description logic reasoners.

Semantic web reasoners and languages enable the semantic web to function. Some of the latest reasoning models developed in the last few years are: DLP, FaCT, RACER, Pellet, MSPASS, CEL, Cerebra Engine, QuOnto, KAON2, HermiT and others. Some software tools such as Protege, Jena and others also have been developed, which provide inferencing as well as ontology development and management environments. These reasoners usually differ in their inference procedures, supporting logic, completeness of reasoning, expressiveness and implementation languages. Various semantic web languages with increasing expressive power continue to be developed for describing web services. We survey the some of the more recent languages like OWL-S (Ontology Web Language-Schema), WSML (Web Service Modeling Language), SWRL (Semantic Web Rule Language) and others that have been tested in early use. We also survey semantic web reasoners and their relationship to these languages.

Though many kinds of multi-agent systems based on argumentation have been proposed where only rule-based knowledge is taken into account, they have been unable to handle the ontological knowledge so far. In our daily life, however, there are a lot of human argumentation where both ontological and rule knowledges are used. For example, in e-commerce, a seller and a buyer usually use ontologies about products along with their respective strategic rules for buying and selling. Recent progress of the Semantic Web technology provides expressive ontology languages. In this paper, we demonstrate integration of the Semantic Web reasoning and argument-based reasoning. We have implemented the integrated system such that Logic of Multiple-valued Argumentation-based agent system (specialized to two values {f, t }) can be accessible to the Semantic Web reasoning established as the description logic reasoning system, given ontologies expressed by OWL DL or its notational variant the DL SHOIN(D). An interesting argumentation result using both ontologies and rules about the university curriculum is shown as an example executed by our system.

Algebraic tableau reasoning for the description logic [formula omitted

Semantic Web service matchmaking is the process of searching the space of possible matches between demands and supplies, and finds the best available ones. To achieve this, the semantic Web service matchmaking framework is one of the absolutely necessary components of service oriented architecture. This paper proposes a matchmaking framework based on Description Logic for semantic Web service described by RDF4S model, a novel semantic Web service description which consists of three layers: service oriented knowledge base, semantic description model and semantic description language. We describe the service description and service query conditions by using the ontologies that are included in service oriented knowledge base. The service resource specification is described according to Description Logics syntax and a Description Logic reasoner is used to compare service descriptions. By representing the semantics of service descriptions, the matchmaker enables the behavior of an intelligent agent to approach more closely that of a human user trying to locate suitable Web service resources.

The paper introduces a knowledge representation language that combines the event calculus with description logic in a logic programming framework. The purpose is to provide the user with an expressive language for modelling and analysing systems that evolve over time. The approach is exemplified with the logic programming language as implemented in the Fusemate system. The paper extends Fusemate's rule language with a weakly DL-safe interface to the description logic $\cal ALCIF$ and adapts the event calculus to this extended language. This way, time-stamped ABoxes can be manipulated as fluents in the event calculus. All that is done in the frame of Fusemate's concept of stratification by time. The paper provides conditions for soundness and completeness where appropriate. Using an elaborated example it demonstrates the interplay of the event calculus, description logic and logic programming rules for computing possible models as plausible explanations of the current state of the modelled system.

Classical Description Logics (DLs) are not suitable to represent vague pieces of information. The attempts to achieve a solution have lead to the birth of fuzzy DLs and rough DLs. In this work, we provide a simple solution to join these two formalisms and define a fuzzy rough DL. We also show how to extend two reasoning algorithms for fuzzy DLs, which are implemented in the fuzzy DL reasoners fuzzyDL and DeLorean.KeywordsDescription LogicVague ConceptReasoning AlgorithmIndiscernibility RelationLukasiewicz LogicThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the increasing number of applications of Description Logics (DLs), unsatisfiable concepts and inconsistent knowledge bases become quite common, especially when the knowledge bases are large and complex. This makes it challenging, even for experienced knowledge engineers, to identify and resolve these unsatisfiabilities and inconsistencies manually. It is thus crucial to provide services to explain how and why a result is derived. Motivated by the possibility of applying resolution technique in first‐order logic to construct explanations for DLs, we present an algorithm that uses patterns to generate explanations for unsatisfiability and inconsistency reasoning in , obtained by extending our previous work on . The use of resolution proofs to provide explanations for DL reasoners is due to their focus which, through literals involved in the process, contributes directly to the contradiction, hence acting as filters to discard irrelevant information. We also establish the soundness and completeness of the algorithm. The proposed solution approach is independent of the underlying DL reasoners, which suggests its potential application for any DL framework.

We describe a method for cascading Description Logic (DL) representation and reasoning on the one hand, and HTN action planning on the other. The planning domain description as well as the fundamental HTN planning concepts are represented in DL and can therefore be subject to DL reasoning; from these representations, concise planning problems are generated for HTN planning. We show that this method yields significantly smaller planning problem descriptions than regular representations do in HTN planning. The method is presented by example of a robot navigation domain.