It is now more than ten years since researchers in the US Knowledge Sharing Effort envisaged a future where complex systems could be built by combining knowledge and services from multiple knowledge bases and the first agent communication language, KQML, was proposed (Neches et al., 1991). This model of communication, based on speech acts, a declarative message content representation language and the use of explicit ontologies defining the domains of discourse (Genesereth & Ketchpel, 1994), has become widely recognised as having great benefits for the integration of disparate and distributed information sources to form an open, extensible and loosely coupled system. In particular, this idea has become a key tenet in the multi-agent systems research community.

This paper describes an approach to ontology negotiation between agents supporting intelligent information management. Ontologies are declarative (data-driven) expressions of an agent's “world”: the objects, operations, facts and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies.

Our objective is to increase the opportunities for “strange agents” – that is, agents not necessarily developed within the same framework or with the same contextual operating assumptions – to communicate in solving tasks when they encounter each other on the web. In particular, we have focused on information search tasks.

We have developed a protocol that allows agents to discover ontology conflicts and then, through incremental interpretation, clarification and explanation, establish a common basis for communicating with each other. We have implemented this protocol in a set of Java classes that can be added to a variety of agents, irrespective of their underlying ontological assumptions. We have demonstrated the use of the protocol, through this implementation, in a test-bed that includes two large scientific archives: NASA's Global Change Master Directory and NOAA's Wind and Sea Index.

This paper presents an overview of different methods for resolving ontology mismatches and motivates the Ontology Negotiation Protocol (ONP) as a method that addresses some problems with other approaches. Much remains to be done. The protocol must be tested in larger and less familiar contexts (for example, numerous archives that have not been preselected) and it must be extended to accommodate additional forms of clarification and ontology evolution.

This paper examines a perceived desire amongst software agent application and platform developers to have the ability to send domain-specific objects within inter-agent messages. If this feature is to be supported without departing from the notion that agents communicate in terms of knowledge, it is important that the meaning of such objects be well defined. Using an object-oriented metamodelling approach, the relationships between ontologies and agent communication and content languages in FIPA-style agent systems are examined. It is argued that for use with distributed multi-agent systems, ontologies should describe the nature of object identity and reference for each defined concept, and a UML profile supporting these modelling capabilities is presented. Finally it is shown how, given an ontology in UML, an ontology-specific object-oriented content language can be generated, allowing object structures (viewed in the abstract as UML object diagrams) to be used within message content to represent propositions, definite descriptions or (for classes without identity) value expressions.

This article presents and motivates an extended ontology knowledge model which represents explicitly semantic information about concepts. This knowledge model results from enriching the standard conceptual model with semantic information which precisely characterises the concept's properties and expected ambiguities, including which properties are prototypical of a concept and which are exceptional, the behaviour of properties over time and the degree of applicability of properties to subconcepts. This enriched conceptual model permits a precise characterisation of what is represented by class membership mechanisms and helps knowledge engineers to determine, in a straightforward manner, the meta-properties holding for a concept. Meta-properties are recognised to be the main tool for a formal ontological analysis that allows us to build ontologies with a clean and untangled taxonomic structure.

This enriched semantics can prove useful to describe what is known by agents in a multi-agent system, and might facilitate the use of reasoning mechanisms on the knowledge that instantiates an ontology. These mechanisms can be used to solve ambiguities that can arise when agents with heterogeneous ontologies have to interoperate in order to perform a task.

Ontologies are becoming increasingly important because they provide the critical semantic foundation for many rapidly expanding technologies such as software agents, e-commerce and knowledge management (McGuinness, 2002). The Unified Modelling Language (UML)1 has been widely adopted by the software engineering community and its scope is broadening to include more diverse modelling tasks. This paper discusses the recent convergence of UML and ontologies and suggests some possible future directions.

The IEEE Standard Upper Ontology (IEEE, 2001) is an effort to create a large, general-purpose, formal ontology. The ontology will be an open standard that can be reused for both academic and commercial purposes without fee, and it will be designed to support additional domain-specific ontologies. The effort is targeted for use in automated inference, semantic interoperability between heterogeneous information systems and natural language processing applications. The effort was begun in May 2000 with an e-mail discussion list, and since then there have been over 6000 e-mail messages among 170 subscribers. These subscribers include representatives from government, academia and industry in various countries. The effort was officially approved as an IEEE standards project in December 2000. Recently a successful workshop was held at IJCAI 2001 to discuss progress and proposals for this project (IJCAI, 2001).

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).