Ontologies are a key component for building open and dynamic distributed pervasive computing systems in which agents and devices share contextual information. We describe our use of the Web Ontology Language OWL and other tools for building the foundation ontology for the Context Broker Architecture (CoBrA), a new context-aware pervasive computing framework. The current version of the CoBrA ontology models the basic concepts of people, agents, places, and presentation events in an intelligent meeting room environment. It provides a vocabulary of terms for classes and properties suitable for building practical systems that model context in pervasive computing environments. We also describe our ongoing research in developing an OWL inference engine using Flora-2 and in extending the present CoBrA ontology to use the DAML spatial and temporal ontologies.

Abstract. In this paper, we present an evaluation of four knowledge base systems (KBS) with respect to use in large OWL applications. To our knowledge, no experiment has been done with the scale of data used here. The smallest dataset used consists of 15 OWL files totaling 8MB, while the largest dataset consists of 999 files totaling 583MB. We evaluated two memory-based systems (OWLJessKB and memory-based Sesame) and two systems with persistent storage (database-based Sesame and DLDB-OWL). We describe how we have performed the evaluation and what factors we have considered in it. We show the results of the experiment and discuss the performance of each system. In particular, we have concluded that existing systems need to place a greater emphasis on scalability. 1

The growing infrastructure for Web Services assumes a "programmer in the loop" that hardcodes the connections between Web Services and directly programs Web Service composition. Emerging technology based on DAML-S and the Semantic Web allows Web Services to connect and transact automatically with minimal or no intervention from programmers. In this paper we discuss the problems related with autonomous Web Services, and how DAMLS provides the information to solve them. Furthermore, we describe the implementation of two demonstration systems that use such technology: the first system is a B2B application in which a business that assembles computers automatically finds partners providing parts and automatically transacts with them; the second describes is an e-commerce application that helps a user to organize a trip to a meeting automatically interacting with different Web Services and the calendar of the user stored in MS Outlook. The results of these experiments show how Web Services can be deployed on the Web to interact and provide information dynamically; second, how the transaction can be carried on automatically with no programmer intervention.

Context-aware computing is an emerging paradigm to free everyday users from manually configuring and instructing computer systems. As the general trend of computing is progressing towards an open and dynamic infrastructure, building context-aware systems can be difficult and costly. In order to build successful context-aware systems, we must develop an architecture to reduce the difficulty and cost of building these systems. This PhD. dissertation proposal describes a research plan to develop a broker-centric agent architecture that is aimed to relieve the burden of capability-limited agents of acquiring and reasoning about contexts, and to protect the privacy of users in a context-aware environment. The implementation of the Context Broker Archiecture will explore Web Ontology Language for modeling contexts and privacy policies, Jess for building a hybrid reasoning mechanism and JADE/FIPA for realizing broker behaviors and agent communications.

Abstract. IPR (Intellectual Property Rights) Management is a complex domain. The IPR field is structured by evolving regulations, practises, business models, … Therefore, DRMS (Digital Rights Management Systems) are very difficult to develop and maintain. The NewMARS DRMS is our contribution to this field. A knowledge oriented approach has been chosen in order to make this development capable of dealing with this complicated domain. This requirement and the objective of easy Web integration have made the Semantic Web technologies the best choice. NewMARS is a semantics enabled metadata managing system. Metadata is associated to IPs (Intellectual Properties) using URIs and it is structured using web ontologies. There are descriptive, rights and e-commerce ontologies for the different views on IPs. Semantic enabled metadata is then used to facilitate content providers to publish intellectual properties offers and customers to find and automatically negotiate purchase conditions. All NewMARS modules are interrelated using the ontologies shared semantics. This has allowed developing very flexible project infrastructures that facilitates easy adaptation to new IP e-commerce scenarios. 1

Abstract. The focus of this paper is the practical evaluation of the challenges and capabilities of combination of ontologies and rules in the context of realtime ubiquitous application. The ec(h)o project designed a platform to create a museum experience that consists of a physical installation and an interactive virtual layer of three-dimensional soundscapes that are physically mapped to the museum displays. The retrieval mechanism is built on the user model and conceptual descriptions of sound objects and museum artifacts. The rule-based user model was specifically designed to work in environments where the rich semantic descriptions are available. The retrieval criteria are represented as inference rules that combine knowledge from psychoacoustics and cognitive domains with compositional aspects of interaction. Evaluation results both from the laboratory and museum deployment testing are presented together with the end user usability evaluations. We also summarize our findings in the lessons learned that provide a transferable generic knowledge for similar type of applications. The ec(h)o proved that ontologies and rules provide an excellent platform for building a highly-responsive context-aware interactive application. 1

Abstract. We present a method for rapid development of benchmarks for Semantic Web knowledge base systems. At the core, we have a synthetic data generation approach for OWL that is scalable and models the real world data. The data-generation algorithm learns from real domain documents and generates benchmark data based on the extracted properties relevant for benchmarking. We believe that this is important because relative performance of systems will vary depending on the structure of the ontology and data used. However, due to the novelty of the Semantic Web, we rarely have sufficient data for benchmarking. Our approach helps overcome the problem of having insufficient real world data for benchmarking and allows us to develop benchmarks for a variety of domains and applications in a very time efficient manner. Based on our method, we have created a new Lehigh BibTeX Benchmark and conducted an experiment on four Semantic Web knowledge base systems. We have verified our hypothesis about the need for representative data by comparing the experimental result to that of our previous Lehigh University Benchmark. The difference in both experiments has demonstrated the influence of ontology and data on the capability and performance of the systems and thus the need of using a representative benchmark for the intended application of the systems. 1

All engineering firms maintain archives of previously designed artifacts, often in the form of databases of computer aided design (CAD) data. Design repositories are an evolution of such databases to include more heterogenous information and to provide enhanced capabilities through the application of knowledge representation techniques. This paper introduces on-going work on applying description logic and the Semantic Web to constructing such design repositories.

Abstract. Griwes is an initiative to develop a common model and an opensource freeware platform shared by different graph-based frameworks. We provide an overview of its objectives, architecture and specifications. We detail some of the basic mathematical structures that are used to characterize the primitives for graph-based knowledge representation. We then propose to factorize recurrent knowledge representation primitives that can be shared across specific graph-based languages and we provide a proof of concept by showing how two languages (Simple Conceptual Graphs and RDF) can be described in this framework.

Abstract—In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation. Index Terms—Inference engines, object-oriented programming, ontology languages, rule-based processing. 1