Ontology driven dynamic linking of biology resources.

Biologists were early adopters of the Web and continue to use it as the primary means of delivering data, tools and knowledge to their community. The Web is made by the links between pages, yet these links have many limitations: they are static and maintained by hand; they can only link one lexical item to another single resource; ownership is necessary for the placement of link anchors and the link mechanism is essentially inflexible. Dynamic linking services, supported by ontologies, offer a mechanism to overcome such restrictions. The Conceptual Open Hypermedia Service (COHSE) system enhances web resources through the dynamic addition of hypertext links. These links are derived through the use of an ontology and associated lexicon along with a mapping from concepts to possible link targets. We describe an application of COHSE to Bioinformatics, using the Gene Ontology (GO) as an ontology and associated keyword mappings and GO associations as link targets. The resulting demonstrator (referred to here as GOHSE) provides both glossary functionality and the possibility of building knowledge based hypertext structures linking bioinformatics resources.

TAMBIS: transparent access to multiple bioinformatics information sources.

UNLABELLED: TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) is an application that allows biologists to ask rich and complex questions over a range of bioinformatics resources. It is based on a model of the knowledge of the concepts and their relationships in molecular biology and bioinformatics. AVAILABILITY: TAMBIS is available as an applet from http://img.cs.man.ac.uk/tambis SUPPLEMENTARY: A full manual, tutorial and videos can be found at http://img.cs.man.ac.uk/tambis. CONTACT: tambis@cs.man.ac.uk

Ontology-based knowledge representation for bioinformatics.

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 require 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, the paper will describe the process of building an ontology, introducing the reader to the techniques and methods currently in use and the open research questions in ontology development.

An ontology for bioinformatics applications.

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, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. RESULTS: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. AVAILABILITY: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.

TAMBIS--Transparent Access to Multiple Bioinformatics Information Sources.

The TAMBIS project aims to provide transparent access to disparate biological databases and analysis tools, enabling users to utilize a wide range of resources with the minimum of effort. A prototype system has been developed that includes a knowledge base of biological terminology (the biological Concept Model), a model of the underlying data sources (the Source Model) and a 'knowledge-driven' user interface. Biological concepts are captured in the knowledge base using a description logic called GRAIL. The Concept Model provides the user with the concepts necessary to construct a wide range of multiple-source queries, and the user interface provides a flexible means of constructing and manipulating those queries. The Source Model provides a description of the underlying sources and mappings between terms used in the sources and terms in the biological Concept Model. The Concept Model and Source Model provide a level of indirection that shields the user from source details, providing a high level of source transparency. Source independent, declarative queries formed from terms in the Concept Model are transformed into a set of source dependent, executable procedures. Query formulation, translation and execution is demonstrated using a working example.

The GRAIL concept modelling language for medical terminology.

The GALEN representation and integration language (GRAIL) has been developed to support effective clinical user interfaces and extensible re-usable models of medical terminology. It has been used successfully to develop the prototype GALEN common reference (CORE) model for medical terminology and for a series of projects in clinical user interfaces within the GALEN and PEN&PAD projects. GRAIL is a description logic or frame language with novel features to support part-whole and other transitive relations and to support the GALEN modelling style aimed at re-use and application independence. GRAIL began as an experimental language. However, it has clarified many requirements for an effective knowledge representation language for clinical concepts. It still has numerous limitations despite its practical successes. The GRAIL experience is expected to form the basis for future languages which meet the same requirements but have greater expressiveness and more soundly based semantics. This paper provides a description and motivation for the GRAIL language and gives examples of the modelling paradigm which it supports.