Requirements and Use Cases ; Report I on the sub-project Smart Content Enrichment

In this technical report, we present the results of the first milestone phase of the Corporate Smart Content sub-project "Smart Content Enrichment". We present analyses of the state of the art in the fields concerning the three working packages defined in the sub-project, which are aspect-oriented ontology development, complex entity recognition, and semantic event pattern mining. We compare the research approaches related to our three research subjects and outline briefly our future work plan

Ontology modularization: principles and practice

Technological advances have provided us with the capability to build large intelligent systems capable of using knowledge, which relies on being able to represent the knowledge in a way that machines can process and interpret. This is achieved by using ontologies; that is logical theories that capture the knowledge of a domain. It is widely accepted that ontology development is a non-trivial task and can be expedited through the reuse of existing ontologies. However, it is likely that the developer would only require a part of the original ontology; obtaining this part is the purpose of ontology modularization. In this thesis a graph traversal based technique for performing ontology module extraction is presented. We present an extensive evaluation of the various ontology modularization techniques in the literature; including a proposal for an entropy inspired measure. A task-based evaluation is included, which demonstrates that traversal based ontology module extraction techniques have comparable performance to the logical based techniques. Agents, autonomous software components, use ontologies in complex systems; with each agent having its own, possibly different, ontology. In such systems agents need to communicate and successful communication relies on the agents ability to reach an agreement on the terms they will use to communicate. Ontology modularization allows the agents to agree on only those terms relevant to the purpose of the communication. Thus, this thesis presents a novel application of ontology modularization as a space reduction mechanism for the dynamic selection of ontology alignments in multi-agent systems. The evaluation of this novel application shows that ontology modularization can reduce the search space without adversely affecting the quality of the agreed ontology alignment