Abstract Description logics (DLs) are formalisms for representing knowledge bases of application domains. The Web Ontology Language (OWL) is a syntactic variant of a very expressive DL. OWL reasoners can infer implied information from OWL ontologies. The performance of OWL reasoners can be severely affected by situations that require decision‐making over many alternatives. Such a nondeterministic behavior is often controlled by heuristics that are based on insufficient information. This article proposes a novel OWL reasoning approach that applies machine learning (ML) to implement pragmatic and optimal decision‐making strategies in such situations. Disjunctions occurring in ontologies are one source of nondeterministic actions in reasoners. We propose two ML‐based approaches to reduce the nondeterminism caused by dealing with disjunctions. The first approach is restricted to propositional DL while the second one can deal with standard DL. Both approaches speed up our ML‐based reasoner by up to two orders of magnitude in comparison to the non‐ML reasoner. Another source of nondeterministic actions is the order in which tableau rules should be applied. On average, our ML‐based approach achieves a speedup of two orders of magnitude when compared to the most expensive rule ordering of the non‐ML reasoner.

Intelligent agents are able to assist human in managing highly dynamic and complex systems in various knowledge intensive domains. The communication between different agents interacting in an integrated multi-agents system can be managed through a set of steering rules, which together form interaction protocols. To support the negotiation, communication and interaction between different intelligent agents, using an appropriate knowledge representation formalism is crucial. This paper introduces the potential of category theory as a formal representation vehicle to facilitate evolutionary analysis of agent interaction and negotiation for managing evolving ontologies in the domain of biomedicine. Utilizing categories supports agents' communication, negotiation, state transitions, compositions and transformations in different levels of abstractions.

One of the most frequently used inference services of description logic reasoners classifies all named classes of OWL ontologies into a subsumption hierarchy. Due to emerging OWL ontologies from the web community consisting of up to hundreds of thousand of named classes and the increasing availability of multi-processor and multi- or many-core computers, we extend our work on parallel TBox classification and propose a new algorithm that is sound and complete and demonstrates in a first experimental evaluation a low overhead w.r.t. subsumption tests (less than 3%) if compared with sequential classification.

We propose the use of formal ontological inferencing, rather than cladistics, to reconstruct phylogeny trees and to analyze the evolutionary relationships between species. For this experiment, we focused on the phylogeny of fungi. Lexical chaining technique has been used for incremental population of evolving ontological elements. Also category theory has been employed to provide an underlying formalism for capturing and analyzing the evolutionary behavior of the system.

We propose an approach for extending a tableau-based satisfiability algorithm by an arithmetic component. The result is a hybrid concept satisfiability algorithm for the Description Logic (DL) 𝒜ℒ𝒞𝒬 which extends 𝒜ℒ𝒞 with qualified number restrictions. The hybrid approach ensures a more informed calculus which, on the one hand, adequately handles the interaction between numerical and logical restrictions of descriptions, and on the other hand, when applied is a very promising framework for average case optimizations.

In this paper we report on our experience using a linguistic technique, called lexical chaining, in assisting the dynamic hierarchical learning of sequentially accessible information for both human and software agents.

Representing and reasoning about time and change is one of the primary issues in the area of artificial intelligence (AI) and knowledge representation (KR). Despite the importance of time and change in ontology change management process, current ontology researches are based on time- independent models. One of the reasons is that considering time in ontologies can increase the complexity and a comprehensive and very expressive ontology language is needed to represent it. In this paper we propose using state space model along with category theory as a mathematical notation, which is independent of a specific choice of ontology language and any particular implementation to analyze and represent temporal ontological models in the domain of biomedical applications.

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.

There exists a large variety of techniques to detect and correct software security vulnerabilities at the source code level, including human code reviews, testing, and static analysis. In this article, we present a static analysis approach that supports both the identification of security flaws and the reasoning about security concerns. We introduce an ontology-based program representation that lets security experts and programmers specify their security concerns as part of the ontology. Within our tool implementation, we support complex queries on the underlying program model using either predefined or user-defined concepts and relations. Queries regarding security concerns, such as exception handling, object accessibility etc. are demonstrated in order to show the applicability and flexibility of our approach

The OWL ontology explorer ONTOXPL is based on the web server tomcat. Standard HTML browsers can be used to interact with ONTOXPL. It is intended to complement existing ontology editors and does not offer any editing support. ONTOXPL uses the OWL DL reasoner RACER via its extensive query interface in order to support the intelligent exploration of OWL ontologies.

This article presents the description logic ALCRP(D) with concrete domains and a role-forming predicate operator as its prominent aspects. We demonstrate the feasibility of ALCRP(D) for reasoning about spatial objects and their qualitative spatial relationships and provide an appropriate concrete domain for spatial objects. The general significance of ALCRP(D) is demonstrated by adding temporal reasoning to spatial and terminological reasoning using a combined concrete domain. The theory is motivated as a basis for knowledge representation and query processing in the domain of geographic information systems. In contrast to existing work in this domain, which mainly focuses either on conceptual reasoning or on reasoning about qualitative spatial relations, we integrate reasoning about spatial information with terminological reasoning. Key words: Description logic, spatial reasoning, spatio temporal reasoning, theoretical foundations for GIS.