Ontologies will play a pivotal r61e in the "Semantic Web", where they will provide a source of precisely defined terms that can be communicated across people and applications. OilEd, is an ontology editor that has an easy to use fi'ame interface, yet at the same time allows users to exploit the full power of an expressive web ontology language (OIL). OilEd uses reasoning to support ontology design, facilitating the development of ontologies that are both more detailed and more accurate.

Ontologies are set to play a key role in the "Semantic Web" by providing a source of shared and precisely defined terms that can be used in descriptions of web resources. Reasoning over such descriptions will be essential if web resources are to be more accessible to automated processes. SHOQ(D) is an expressive description logic equipped with named individuals and concrete datatypes which has almost exactly the same expressive power as the latest web ontology languages (e.g., OIL and DAML). We present sound and complete reasoning services for this logic. 1

This paper presents a specifically database-inspired approach (called DOGMA) for engineering formal ontologies, implemented as shared resources used to express agreed formal semantics for a real world domain. We address several related key issues, such as knowledge reusability and shareability, scalability of the ontology engineering process and methodology, efficient and effective ontology storage and management, and coexistence of heterogeneous rule systems that surround an ontology mediating between it and application agents. Ontologies should represent a domain's semantics independently from "language", while any process that creates elements of such an ontology must be entirely rooted in some (natural) language, and any use of it will necessarily be through a (in general an agent's computer) language. To achieve the claims stated, we explicitly decompose ontological resources into ontology bases in the form of simple binary facts called lexons and into socalled ontological commitments in the form of description rules and constraints. Ontology bases in a logic sense, become "representationless " mathematical objects which constitute the range of a classical interpretation mapping from a first order language, assumed to lexically represent the commitment or binding of an application or task to such an ontology base. Implementations of ontologies become database-like on-line resources in the model-theoretic sense. The resulting architecture allows to materialize the (crucial) notion of commitment as a separate layer of (software agent) services, mediating between the ontology base and those application instances that commit to the ontology. We claim it also leads to methodological approaches that naturally extend key aspects of database modeling theory and practice. We discuss examples of the prototype DOGMA implementation of the ontology base server and commitment server.

This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualisation is encoded using the Ontology Inference Layer (OIL), a knowledge representation language that combines the modelling style of Frame-Based systems with the expressiveness and reasoning power of Description Logics. This paper is the second of a pair in this special issue. The first described the core of the OIL language and the need to use ontologies to deliver semantic bioinformatics resources. In this paper, the early stages of building an ontology component of a bioinformatics resource querying applicationn are described. This ontology holds the information about molecular biology represented in bioinformatics resources and the bioinformatics tasks performed over these resources. It, therefore, represents the metadata of the resources the application can query. It also manages the terminologies used in constructing the query plans used to retrieve instances from those external resources. The methodology used in this task capitalises upon features of OIL described in the first paper of this special issue -- The conceptualisation afforded by the Frame-Based view of OIL's syntax; the expressive power and reasoning of the logical formalism; and the ability to encode both hand-crafted, hierarchies of concepts, as well as defining concepts in terms of their properties, which can then be used to establish a classification and infer relationships not encoded by the ontologist. This ability forms the basis of the methodology described here: For each portion of the TaO, a basic frame-work of concepts is asserted by the ontologist. Then, the properties of these concepts are defined by the ontologist and the logic's reasoning power used to re-classify and ...

Ontologies will play an important role in bioinformatics, as they do in other disciplines, where they will provide a source of precisely defined terms that can be communicated across people and applications. The Ontology Inference Layer (OIL), is an ontology language that has an easy to use frame feel, yet at the same time allows users to exploit the full power of an expressive description logic. OilEd, an editor for OIL, uses reasoning to support ontology design, facilitating the development of ontologies that are both more detailed and more accurate. This paper presents a bioinformatics ontology building case study using OilEd to highlight the features of the combination of a frame representation and an expressive description logic.

The complex questions and analyses posed by biologists, as well as the diverse data resources they develop, require the fusion of evidence from different, independently developed and heterogeneous resources. The web as an enabler for interoperability has been an excellent mechanism for data publication and transportation. Successful exchange and integration of information, however, depends on a shared language for communication (a terminology) and a shared understanding of what the data means (an ontology). Without this kind of understanding, semantic heterogeneity remains a problem for both humans and machines. One means of dealing with heterogeneity in bioinformatics resources is through terminology founded upon an ontology. Bioinformatics resources tend to be rich in human readable and understandable annotation, with each resource using its own terminology. These resources are machine readable, but not machine understandable. Ontologies have a role in increasing this machine understanding, reducing the semantic heterogeneity between resources and thus promoting the flexible and reliable interoperation of bioinformatics resources. This paper describes a solution derived from the semantic web (a machine understandable WWW), the Ontology Inference Layer (OIL), as a solution for semantic bioinformatics resources. The nature of the heterogeneity problems are presented along with a description of how metadata from domain ontologies can be used to alleviate this problem. A companion paper in this issue gives an example of the development of a bioontology using OIL. Keywords: Ontology; OIL; semantics; interoperation; heterogeneity; understanding. 1

This paper gives an overview of MediBook, a joint project of the Medical Department of University Gieen (Germany) and the Department of Electrical Engineering and Information Technology at Darmstadt University of Technology (Germany) funded by the Hessian Ministry for Science and Arts (HMWK). The goal of the project is the enhancement of education in Medical Schools by means of a flexible system. The flexibility of the system aims at self-studying, independently from space and time. MediBook is a system which provides tools for storing, managing and especially locating and combining learning resources. It offers means for both describing single sources (thereby putting them into a context) and combining single resources to courses. Thus MediBook represents a knowledge base with efficient access and tools to generate arbitrary coherent courses from single information units

The Transparent Access to Multiple Bioinformatics Information Sources (TAMBIS) system uses an ontology to give the illusion of a common query interface to multiple, diverse, heterogeneous bioinformatics resources. The knowledge in the TAMBIS ontology (TaO) allows biologists to form complex, multi-source queries without having to know which source to use, the location of the source, the meaning of terms within the source and how to transfer information between resources. The TaO can be seen to add a semantic layer over these bioinformatics resources. Ontologies are finding a wider use in the bioinformatics arena, emphasising the usefulness of knowledge in bioinformatics. Just as the vision for the Semantic Web envisages the use of knowledge to give a machine processable web, so semantic bioinformatics resources will enable machine processable, rather than only machine readable, bioinformatics resources.

A unified representation for web data and web resources, is absolutely necessary in nowdays large scale Internet data management systems. This representation will allow for the machines to meaningfully process the available information and provide semantically correct answers to imposed queries. Ontologies are expected to play an important role towards this direction of web technology which defines the so called, Semantic Web. The goal of this paper is to provide an overview of the Knowledge Representation (KR) techniques and languages that can be used as standards in the Semantic Web .

Effective use of the vast quantity of information now available on the web will require the use of "Semantic Web" markup languages such as the DARPA Agent Markup Language (DAML). Such languages will enable the automated gathering and processing of much information that is currently available but insufficiently utilized. Effectively, such languages will facilitate the integration of multi-agent systems with the existing information infrastructure. As part of our exploration of Semantic Web technology, and DAML in particular, we have constructed ITTALKS, a web-based system for automatic and intelligent notification of information technology talks. In this paper, we describe the ITTALKS system, and discuss the numerous ways in which the use of Semantic Web concepts and DAML extend its ability to provide an intelligent online service to both the human community and the agents assisting them. 1