We examine the possible use of Description Logics as a knowledge representation and reasoning system for high-level scene interpretation. It is shown that aggregates composed of multiple parts and constrained primarily by temporal and spatial relations can be used to represent high-level concepts such as object configurations, occurrences, events and episodes. Scene interpretation is modelled as a stepwise process which exploits the taxonomical and compositional relations between aggregate concepts while incorporating visual evidence and contextual information. It is shown that aggregates can be represented by a Description Logic ALCF(D) which provides feature chains and a concrete domain extension for quantitative temporal and spatial constraints. Reasoning services of the DL system can be used as building blocks for the interpretation process, but additional information is required to generate preferred interpretations. A probabilistic model is sketched which can be integrated with the knowledge-based framework.

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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 SHIQ(D n ) − , 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.

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