Bayesian Networks and Decision Graphs by Finn V. Jensen, Springer Verlag, £64.95, ISBN 0-387-95259-4

Analogues to much of today's work in ontologies have existed for centuries in text retrieval. The use of controlled vocabularies, or thesauri, has been fundamental to document indexing in library science. Thesauri serve several purposes, including:

[bull ] Knowledge organisation A thesaurus provides a hierarchy of concepts that organises domain-specific knowledge.

[bull ] Terminology normalisation By selecting a unique word or phrase to represent each domain concept, then linking synonymous terms to it, a thesaurus enforces terminological consistency.

[bull ] Query expansion A thesaurus facilitates the addition of terms to a query by providing explicit hierarchical and lateral relationships among terms.

These properties serve to mediate the information flow from indexer to user. Thesauri thus serve many of the same functions for people that ontologies are designed to serve for software agents. As automated retrieval has developed over the decades since the inception of computer processing of text, many techniques have been introduced to apply this typically manual work to the automated arena (see Soergel (1985) for an introduction to library information systems, also Anderson and Pélrez-Carballo (2001a, 2001b) for a summary of the intersection of human and machine indexing).

This paper describes the need for ontologies in the life sciences, some work that has been done already on the development of ontologies and languages for the exchange of life sciences objects, some important problems in the field where ontologies provide critical benefit and some of the organisations that are facilitating this work.

Academic work on agents and ontologies is often oblivious to the complexities and realities of enterprise computing. At the same time, the practitioners of enterprise computing, although they are adept at the building of robust, real-life enterprise applications, are unaware of the academic body of work and the opportunities of applying novel approaches of academic origin. Enterprise applications are very complex systems that are designed to support critical business operations. This article outlines the technical and business foundations of enterprise application software and briefly discusses viable opportunities for agents and ontology research.

In the coming years, the Web is expected to evolve from a structure containing information resources that have little or no explicit semantics to a structure having a rich semantic infrastructure. The key defining feature that is intended to distinguish the future Semantic Web from today's Web is that the content of the Web will be usable by machines (i.e. software agents). Meaning needs to be communicated between agents who advertise and/or require the ability to perform tasks on the Web. Agents also need to determine the meaning of passive (i.e. non-agent) information resources on the web to perform these tasks.

Currently the Web is the largest available environment for the deployment of agents, and much work in agent research is driven by Web-based applications (Luke et al. (1997), Joachims et al. (1997), Bollacker et al. (1998), Doorenbos et al. (1997) are just some examples; see also the May 2000 special issue of the Artificial Intelligence Journal on intelligent internet systems, 118 (1–2)). However, such applications of agent technology are hampered by the fact that the Web is not geared towards agent use, but is rather designed for human use. Current Web resources are lacking in explicit, machine-accessible descriptions of their contents; they are only fully accessible to agents with a competent grasp of English (i.e. limited to human agents only).