Anni-Yasmin Turhan

Computing the least common subsumer w.r.t. a background terminology

Abstract Methods for computing the least common subsumer (lcs) are usually restricted to rather inexpressive Description Logics (DLs) whereas existing knowledge bases are written in very expressive DLs. In order to allow the user to re-use concepts defined in such terminologies and still support the definition of new concepts by computing the lcs, we extend the notion of the lcs of concept descriptions to the notion of the lcs w.r.t. a background terminology. We will show both theoretical results on the existence of the least common subsumer in this setting, and describe a practical approach—based on a method from formal concept analysis—for computing good common subsumers, which may, however, not be the least ones. We will also describe results obtained in a first evaluation of this practical approach.

Exploiting Pseudo Models for TBox and ABox Reasoning in Expressive Description Logics

This paper investigates optimization techniques and data structures exploiting the use of so-called pseudo models. These techniques are applied to speed up TBox and ABox reasoning for the description logics ALCNHR+ and ALC(D). The advances are demonstrated by an empirical analysis using the description logic system RACE that implements TBox and ABox reasoning for ALCNHR+.

Employing description logics in Ambient Intelligence for modeling and reasoning about complex situations

Ambient Intelligence systems need to represent information about their environment and recognize relevant situations to perform appropriate actions proactively and autonomously. The context information gathered by these systems comes with imperfections such as incompleteness or incorrectness. These characteristics need to be handled gracefully by the Ambient Intelligence system. Moreover, the represented information must allow for a fast and reliable recognition of the current situation. To solve these problems we propose a method for situation modeling using the Description Logics based ontology language OWL DL and a framework for employing Description Logics reasoning services to recognize the current situation based on context. The benefits from the approach are manifold: the semantics of Description Logics allow for graceful handling of incomplete knowledge. The well-investigated reasoning services do not only allow recognizing the current situation, but also can add to the reliability of the overall system. Moreover optimized reasoning systems are freely available and ready to use. We underpin the feasibility of our approach by providing a case study based on a smart home application conducting an evaluation of different Description Logics reasoners with respect to our application ontology as well as a discussion of Description Logics systems in Ambient Intelligence.

Computing the Least Common Subsumer w.r.t. a Background Terminology

Methods for computing the least common subsumer (lcs) are usually restricted to rather inexpressive Description Logics (DLs) whereas existing knowledge bases are written in very expressive DLs. In order to allow the user to re-use concepts defined in such terminologies and still support the definition of new concepts by computing the lcs, we extend the notion of the lcs of concept descriptions to the notion of the lcs w.r.t. a background terminology. We will both show a theoretical result on the existence of the least common subsumer in this setting, and describe a practical approach (based on a method from formal concept analysis) for computing good common subsumers, which may, however, not be the least ones.

A framework for semantic-based similarity measures for ELH -concepts

Similarity measures for concepts written in Description Logics (DLs) are often devised based on the syntax of concepts or simply by adjusting them to a set of instance data. These measures do not take the semantics of the concepts into account and can thus lead to unintuitive results. It even remains unclear how these measures behave if applied to new domains or new sets of instance data.

On the Problem of Computing Small Representations of Least Common Subsumers

For Description Logics with existential restrictions, the size ofthe least common subsumer (lcs) of concept descriptions may grow exponentially in the size of the input descriptions. The first (negative) result presented in this paper is that it is in general not possible to express the exponentially large concept description representing the lcs in a more compact way by using an appropriate (acyclic) terminology. In practice, a second and often more severe cause of complexity was the fact that concept descriptions containing concepts defined in a terminology must first be unfolded (by replacing defined names by their definition) before the known lcs algorithms could be applied. To overcome this problem, we present a modified lcs algorithm that performs lazy unfolding, and show that this algorithm works well in practice.

Pushing doors for modeling contexts with OWL DL - a case study

In this paper we present an integrated view for modeling and reasoning for context applications using OWL DL. In our case study, we describe a task driven approach to model typical situations as context concepts in an OWL DL ontology. At run-time OWL individuals form situation descriptions and by use of realization we recognise a certain context. We demonstrate the feasibility of our approach by performance measurements of available highly optimised description logics (DL) reasoners for OWL DL

Description logic reasoning for semantic web ontologies

The ontology language for the semantic web OWL provides means to describe entities of an application domain in an ontology in a well-structured way. The underlying formalism for OWL are Description Logics (DLs) [6], which are a family of knowledge representation formalisms that have formal semantics. This family of logics is tailored towards representing terminological knowledge of an application domain in a structured and formally well-understood way.

Sonic — Non-standard Inferences Go OilEd

Sonic is the first prototype implementation of non-standard inferences for Description Logics usable via a graphical user interface. The contribution of our implementation is twofold: it extends an earlier implementation of the least common subsumer and of the approximation inference to number restrictions, and it offers these reasoning services via an extension of the graphical ontology editor OilEd [3].

Similarity-based relaxed instance queries

In Description Logics (DL) knowledge bases (KBs), information is typically captured by clear-cut concepts. For many practical applications querying the KB by crisp concepts is too restrictive; a user might be willing to lose some precision in the query, in exchange of a larger selection of answers. Similarity measures can offer a controlled way of gradually relaxing a query concept within a user-specified limit.In this paper we formalize the task of instance query answering for DL KBs using concepts relaxed by concept similarity measures (CSMs). We investigate computation algorithms for this task in the DL EL , their complexity and properties for the CSMs employed regarding whether unfoldable or general TBoxes are used. For the case of general TBoxes we define a family of CSMs that take the full TBox information into account, when assessing the similarity of concepts.