We present our approach in building WonderDesk-a semantic desktop for resource sharing and management, which is a component of our WonderSpace application and designed to be an e-Science tool. We propose a desktop architecture for resource sharing and management using semanticenriched RSS model and P-P technology. To enable fine-grained metadata for e-Science, an ontology about resource is presented as basic vocabulary to be used in constructing resource metadata along with RSS. Further, group-specific discipline ontology is proposed to enhance the semantic description of information resource within user group.

Interactive multi-agent system improves reusability of agents by separating application design from agent design. However, it remains difficult for application designers (usually non-computer professionals) to script massive multi-agents. This is especially true for scripting hundreds of NPCs (Non Player Characters, agents) hosted by hostile, dynamic and complex 3D (threedimensional) environments in military simulation. Out of perspective of interaction between agents and environments, namely virtual spaces, we introduce virtual space ontologies to facilitate interaction between humans and agents. Three advantages are obtained by using virtual space ontologies: 1) A hybrid approach of integrating qualitative and quantitative spatial reasoning is achieved so that application designers can specify arguments of actions (scenario primitives) qualitatively. 2) Primitive actions of agents are abstracted hierarchically so that application designers can sketch scenarios for units of agents. 3) Better intent communication between humans and agents is realized so that application users can control agents easily in real time.

This paper is concerned with the problem of robust stability for stochastic interval delayed additive neural networks (SIDANN) with Markovian switching. The time delay is assumed to be time-varying. In such neural networks, the features of stochastic systems, interval systems, timevarying delay systems and Markovian switching are taken into account. The mathematical model of this kind of neural networks is first proposed. Secondly, the global exponential stability in the mean square is studied for the SIDANN with Markovian switching. Based on the Lyapunov method, several stability conditions are presented, which can be expressed in terms of linear matrix inequalities. As a subsequent result, the stochastic interval additive neural networks with time-varying delay are also discussed. A sufficient condition is given to determine its stability. Finally, two simulation examples are provided to illustrate the effectiveness of the results developed.

Ontology matching is a crucial task of enabling interoperation betweenWeb applications using different but related ontologies. Due to the size and the monolithic nature, large-scale ontologies regarding real world domains cause a new challenge to current ontology matching techniques. In this paper, we propose a method for partition-based block matching that is practically applicable to large class hierarchies, which are one of the most common kinds of large-scale ontologies. Based on both structural affinities and linguistic similarities, two large class hierarchies are partitioned into small blocks respectively, and then blocks from different hierarchies are matched by combining the two kinds of relatedness found via predefined anchors as well as virtual documents between them. Preliminary experiments demonstrate that the partitionbased block matching method performs well on our test cases derived from Web directory structures.

Ontology summarization is very important to quick understanding and selection of ontologies. In this paper, we study extractive summarization of ontology. We propose a notion of RDF sentence as the basic unit of summarization. An RDF Sentence Graph is proposed to characterize the links between RDF sentences derived from a given ontology. The salience of each RDF sentence is assessed in terms of its "centrality" in the graph. We propose to summarize an ontology by extracting a set of salient RDF sentences according to a re-ranking strategy. We compare several measurements in assessing the salience of RDF sentences and give an overall evaluation of experiment results, which shows that our approach to ontology summarization is feasible.