Agent-based ontology management towards interoperability

Ontologies are widely used as data representations for knowledge bases and marking up data on the emerging Semantic Web. Hence, techniques for managing ontol- ogy come to the centre of any practical and general solution of knowledge-based systems. Challenges arise when we look a step further in order to achieve flexibility and scalability of the ontology management. Previous works in ontology management, primarily for ontology mapping, ontology integration and ontology evolution, have exploited only one form or another of ontology management in restrictive settings. However, a distributed and heterogeneous environment makes it necessary for re- searchers in this field to consider ontology interoperability in order to achieve the vision of the Semantic Web. Several challenges arise when we set our goal to achieve ontology interoperability on the Web. The first one is to decide which soft- ware engineering paradigm to employ. The issue of such a paradigm is the core of ontology management when dynamic property is involved. It should make it easy to model complex systems and significantly improve current practice in software engineering. Moreover, it allows the extension of the range of applications that can feasibly be tackled. The second challenge is to exploit frameworks based on the pro- posed paradigm. Such a framework should make possible flexibility, interactivity, reusability and reliability for systems which are built on it. The third challenge is to investigate suitable mechanisms to cope with ontology mapping, integration and evolution based on the framework. It is known that predefined rules or hypotheses may not apply given that the environment hosting an ontology is changing over time. Fortunately, agents are being advocated as a next generation model for en- gineering complex and distributed systems. Also some researchers in this field have given a qualitative analysis to provide a justification for precisely why the agent-based approach is well suited to engineer complex software systems. From a multi-agent perspective, agent technology fits well in developing applications in uncontrolled and distributed environments which require substantial support for change. Agents in multi-agent systems (MAS) are autonomous and can engage in interactions which are essential for any ongoing agents' actions. A MAS approach is thus regarded as an intuitive and suitable way of modelling dynamic systems. Following the above discussion, an agent-based framework for managing ontology in a dynamic environment is developed. The framework has several key characteris- tics such as flexibility and extensibility that differentiate this research from others. Three important issues of the ontology management are also investigated. It is be- lieved that inter-ontology processes like ontology mapping with logical semantics are foundations of ontology-based applications. Hence, firstly, ontology mapping is discussed. Several types of semantic relations are proposed. Following these, the mapping mechanisms are developed. Secondly, based on the previous mapping results, ontology integration is developed to provide abstract views for participating organisations in the presence of a variety of ontologies. Thirdly, as an ontology is subject to evolution in its life cycle, there must be some kind of mechanisms to reflect their changes in corresponding interrelated ontologies. Ontology refinement is investigated to take ontology evolution into consideration. Process algebra is employed to catch and model information exchanges between ontologies. Agent negotiation strategy is applied to guide corresponding ontologies to react properly. A prototype is built to demonstrate the above design and functionalities. It is applied to ontologies dealing with the subject of beer (type). This prototype con- sists of four major types of agents, ranging from user agent, interface agent, ontology agent, and functionary agent. Evaluations such as query, consistency checking are conducted on the prototype. This shows that the framework is not only flexible but also completely workable. All agents derived from the framework exhibit their behaviours appropriately as expected.