Ralf Möller

RACER System Description

RACER implements a TBox and ABox reasoner for the logic SHIQ. RACER was the first full-fledged ABox description logic system for a very expressive logic and is based on optimized sound and complete algorithms. RACER also implements a decision procedure for modal logic satisfiability problems (possibly with global axioms).

The RacerPro knowledge representation and reasoning system

RacerPro is a software system for building applications based on ontologies. The backbone of RacerPro is a description logic reasoner. It provides inference services for terminological knowledge as well as for representations of knowledge about individuals. Based on new optimization techniques and techniques that have been developed in the research field of description logics throughout the years, a mature architecture for typical-case reasoning tasks is provided. The system has been used in hundreds of research projects and industrial contexts throughout the last twelve years. W3C standards as well as detailed feedback reports from numerous users have influenced the design of the system architecture in general, and have also shaped the RacerPro knowledge representation and interface languages. With its query and rule languages, RacerPro goes well beyond standard inference services provided by other OWL reasoners.

On the Scalability of Description Logic Instance Retrieval

Practical description logic systems play an ever-growing role for knowledge representation and reasoning research even in distributed environments. In particular, the ontology layer of the often-discussed semantic web is based on description logics (DLs) and defines important challenges for current system implementations. The article introduces and evaluates optimization techniques for the instance retrieval problem w.r.t. the description logic

A Description Logic with Concrete Domains and a Role-forming Predicate Operator ∗

\mathcal{SHIQ}(\mathcal{D}_{n})^{-}

Ontology-Based Reasoning Techniques for Multimedia Interpretation and Retrieval

, which covers large parts of the Web Ontology Language (OWL). We demonstrate that sound and complete query engines for OWL-DL can be built for practically significant query classes. Experience with ontologies derived from database content has shown that it is often necessary to effectively solve instance retrieval problems with respect to huge amounts of data descriptions that make up major parts of ontologies. We present and analyze the main results about how to address this kind of scalability problem.

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

This article presents the description logic ALCRP(D) with concrete domains and a roleforming 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 signicance 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.

The Description Logic ALCNHR+ Extended with Concrete Domains: A Practically Motivated Approach

In this chapter, we show how formal knowledge representation and reasoning techniques can be used for the retrieval and interpretation of multimedia data. This section explains what we mean by an “interpretation” using examples of audio and video interpretation. Intuitively, interpretations are descriptions of media data at a high abstraction level, exposing interrelations and coherencies. In Section 3.2.3, we introduce description logics (DLs) as the formal basis for ontology languages of the OWL (web ontology language) family and for the interpretation framework described in subsequent sections. As a concrete example, we consider the interpretation of images describing a sports event in Section 3.3. It is shown that interpretations can be obtained by abductive reasoning, and a general interpretation framework is presented. Stepwise construction of an interpretation can be viewed as navigation in the compositional and taxonomical hierarchies spanned by a conceptual knowledge base. What do we mean by “interpretation” of media objects? Consider the image shown in Fig. 3.1. One can think of the image as a set of primitive objects such as persons, garbage containers, a garbage truck, a bicycle, traffic signs, trees, etc. An interpretation of the image is a description which “makes sense” of these primitive objects. In our example, the interpretation could include the assertions “two workers empty garbage containers into a garbage truck” and “a mailman distributes mail” expressed in some knowledge representation language. When including the figure caption into the interpretation process, we have a multimodal interpretation task which in this case involves visual and textual media objects. The result could be a refinement of the assertions above in terms of the location “in Hamburg”. Note that the interpretation describes activities extending in time although it is only based on a snapshot. Interpretations may generally include

Consistency Testing: The RACE Experience

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+.

Multimedia Interpretation for Dynamic Ontology Evolution

In this paper the description logic ALCNHR+(D)- is introduced. Prominent language features beyond conjunction, full negation, and quantifiers are number restrictions, role hierarchies, transitively closed roles, generalized concept inclusions, and concrete domains. As in other languages based on concrete domains (e.g. ALC(D)) a so-called existential predicate restriction is provided. However, compared to ALC(D) only features and no feature chains are allowed in this operator. This results in a limited expressivity w.r.t. concrete domains but is required to ensure the decidability of the language. We show that the results can be exploited for building practical description logic systems for solving e.g. configuration problems.

A Stream-Temporal Query Language for Ontology Based Data Access

This paper presents the results of applying RACE, a description logic system for \(\mathcal{ALCNH}_{R^{+}}\), to modal logic SAT problems. Some aspects of the RACE architecture are discussed in detail: (i) techniques involving caching and (ii) techniques for dealing with individuals.