Most description logic (DL) systems present the application programmer with a functional interface, often defined using a Lisp-like syntax. Such interfaces may be more or less complex, depending on the sophistication of the implemented system, and may be more or less compliant with the KRSS description logic specification [7]. The Lisp style of the KRSS syntax reflects the fact that Lisp is still the most common implementation language for DLs. This can create considerable barriers to the use of DL systems by application developers, who often prefer other languages (in particular the currently ubiquitous Java), and who are becoming more accustomed to component based software development environments. In such an environment, a DL might naturally be viewed as a self contained component, the details of whose implementation, and even the precise location in which its code is being executed, is hidden from the application [2]. This approach has several advantages: the issue of implementation language is finessed; the API can be defined in some standard formalism intended for the purpose; a mechanism

Much of biology works by applying prior knowledge (`what is known') to an unknown entity, rather than the application of a set of axioms that will elicit knowledge. In addition, the complex biological data stored in bioinformatics databases often requires the addition of knowledge to specify and constrain the values held in that database. One way of capturing knowledge within bioinformatics applications and databases is the use of ontologies. An ontology is the concrete form of a conceptualisation of a community's knowledge of a domain. This paper aims to introduce the reader to the use of ontologies within bioinformatics. A description of the type of knowledge held in an ontology will be given. The paper will be illustrated throughout with examples taken from bioinformatics and molecular biology, and a survey of current biological ontologies will be presented. From this it will be seen that the use to which the ontology is put largely determines the content of the ontology. Finally, t...

Motivation: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, suchas the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. Results:

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of . an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; . a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents). Keywords Open hypermedia, link service, ontology, navigation, metadata.

The paper presents a review of the ONIONS project. ONIONS is committed to developing a large­scale ontology library for medical terminology. The developed methodology exploits a description logic­based design for the modules in the library and makes extended use of generic theories, thus creating a stratification of the modules. Terminological knowledge is acquired by conceptual analysis and ontology integration over a set of authoritative sources. After addressing general issues about conceptual analysis and integration, the methodology is briefly described. The central part of the article presents the investigation we have made on the 476,000 medical concepts singled out by the National Library of Medicine as the Metathesaurus^TM in the UMLS project. This is followed by several case studies concerning lexical polysemy, the interface between ontologies and lexicon, and other special problems encountered in the specification of the ontologies. A section describing the current structure of the library and the generic theories reused is provided. Current results of our research include the integration of some top­level ontologies in the ON9.2 ontology library, and the formalization of the terminological knowledge in the UMLS Metathesaurus.

Motivation: This paper reports on a survey of bioinformatics tasks currently undertaken by working biologists. The aim was to find the range of tasks that need to be supported and the components needed to do this in a general query system. This enabled a set of evaluation criteria to be used to assess both the biology and mechanical nature of general query systems. Results: A classification of the biological content of the tasks gathered offers a check-list for those tasks (and their specialisations) that should be offered in a general bioinformatics query system. This semantic analysis was contrasted with a syntactic analysis that revealed the small number of components required to describe all bioinformatics questions. Both the range of biological tasks and syntactic task components can be seen to provide a set of bioinformatics requirements for general query systems. These requirements were used to evaluate two bioinformatics query systems. Contact: robert.stevens@cs.man.ac.uk. Sup...

This paper surveys the area of biological and genomic sources integration, which has recently become a major focus of the data integration research field. The challenges that an integration system for biological sources must face are due to several factors such as the variety and amount of data available, the representational heterogeneity of the data in the different sources, and the autonomy and differing capabilities of the sources.

Approximation is a new inference service in Description Logics first mentioned by Baader, Küsters, and Molitor. Approximating a concept, defined in one Description Logic, means to translate this concept to another concept, defined in a second typically less expressive Description Logic, such that both concepts are as closely related as possible with respect to subsumption. The present paper provides the first in-depth investigation of this inference task. We prove that approximations from the Description Logic ALC to ALE always exist and propose an algorithm computing them. As a measure for the accuracy of the approximation, we introduce a syntax-oriented difference operator, which yields a concept that contains all aspects of the approximated concept that are not present in the approximation. It is also argued that a purely semantical difference operator, as introduced by Teege, is less suited for this purpose. Finally, for the logics under consideration, we propose an algorithm computing the difference.

Knowledge management research focuses on concepts, methods, and tools supporting the management of human knowledge. The main objective of this paper is to survey basic concepts that have been used in Computer Science for the representation of knowledge and summarize some of their advantages and drawbacks. A secondary objective is to relate these techniques to Information Science theory and practice. The survey classifies the concepts used for knowledge representation into four broad ontological categories. Static ontologies describe static aspects of the world, i.e., what things exist, their attributes and relationships. A dynamic ontology, on the other hand, describes the changing aspects of the world in terms of states, state transitions and processes. Intentional ontologies encompass the world of things agents believe in, want, prove or disprove, and argue about. Finally, social ontologies cover social settings – agents, positions, roles, authority, permanent organizational structures or shifting networks of alliances and interdependencies. 1.

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 ...