The benefits of using ontologies have been recognised in many areas such as knowledge and content management, electronic commerce and recently the emerging field of the Semantic Web. These new applications can be seen as a great success of research in ontologies. On the other hand, moving into real application comes with new challenges that need to be addressed on a principled level rather than for specific applications. This special issue will be devoted to less well-explored topics that have come into focus recently as a response to the new problems we face when trying to use ontologies in heterogeneous distributed environments. These environments include the use of ontologies in peer-to-peer and pervasive computing systems.

This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language OWL, is a collection of ontologies for describing places, agents and events and their associated properties in an intelligent meeting-room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning and privacy protection supports for pervasive context-aware systems. We also describe an inference engine for reasoning with information expressed using the COBRA-ONT ontology and the ongoing research in using the DAML-Time ontology for context reasoning.

Ontologies are entering widespread use in many areas such as knowledge and content management, electronic commerce and the Semantic Web. In this paper we show how the use of ontologies has helped us overcome some important problems in the development of pervasive computing environments. We have integrated ontologies and Semantic Web technology into our pervasive computing infrastructure. Our investigations have shown that Semantic Web technology can be integrated into our CORBA-based infrastructure to augment several important services. This work suggests a number of requirements for future research in the development of ontologies, reasoners, languages and interfaces.

We have produced an ontology specifying a model of computer attack. Our ontology is based upon an analysis of over 4000 classes of computer intrusions and their corresponding attack strategies and is categorised according to system component targeted, means of attack, consequence of attack and location of attacker. We argue that any taxonomic characteristics used to define a computer attack be limited in scope to those features that are observable and measurable at the target of the attack. We present our model as a target-centric ontology that is to be refined and expanded over time. We state the benefits of forgoing dependence upon taxonomies in favour of ontologies for the classification of computer attacks and intrusions. We have specified our ontology using the DARPA Agent Markup Language+Ontology Inference Layer and have prototyped it using DAMLJessKB. We present our model as a target-centric ontology and illustrate the benefits of utilising an ontology in lieu of a taxonomy, by presenting a use-case scenario of a distributed intrusion detection system.

This paper explores the hypothesis that ontologies can be used to improve the capabilities and performance of on-board route planning for autonomous vehicles. We name a variety of general benefits that ontologies may provide, and list numerous specific ways that ontologies may be used in different components of our chosen infrastructure: the 4D/RCS system architecture developed at NIST. Our initial focus is on simple roadway driving scenarios where the controlled vehicle encounters objects in its path. Our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with the different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to avoid the object. We describe our current experiments and plans for future work.

Ontologies provide potential support for knowledge and content management on a P2P platform. Although we can design ontologies beforehand for an application, it is argued that in P2P environments static or predefined ontologies cannot satisfy the ever-changing requirements of all users. So we propose every user should make proposals for what kind of ontology is the most apt to his need. Collecting all these proposals (or votes) helps the drift of ontologies. This paper introduces OntoVote, a scalable distributed vote-collecting mechanism based on application-level broadcast trees, and describes how OntoVote can be applied to ontology drift on a P2P platform by discussing several problems involved in the voting process.

We think of match as an operator that takes two graph-like structures (e.g. database schemas or ontologies) and produces a mapping between elements of the two graphs that correspond semantically to each other. The goal of this paper is to propose a new approach to matching, called semantic matching. As its name indicates, in semantic matching the key intuition is to exploit the model-theoretic information, which is codified in the nodes and the structure of graphs. The contributions of this paper are (i) a rational reconstruction of the major matching problems and their articulation in terms of the more generic problem of matching graphs, (ii) the identification of semantic matching as a new approach for performing generic matching and (iii) a proposal for implementing semantic matching by testing propositional satisfiability.