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.

In this paper, two kinds of vocabulary consent relations are proposed as a mechanism for the partial reuse and integration of ontologies, and then a formal semantics of consent relationships is given to support such a mechanism. Particularly, some semantic conditions are proposed to guarantee the expected consequences of vocabulary consent relationships. The issue about reasoning with the given semantic framework is also briefly discussed.

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.

While the Semantic Web technology has reached a considerable achievement, the main part of the Web is still far from semantic. To achieve semantic search on the current Web, we developed Falcon-S, a Semantic Web application which indexes soccer images semantically and provides ontology-driven searching and browsing mechanisms. Especially a graph-based user interface is provided to customize semantic queries for domain objects. In addition, the meanings of the image contents are captured and utilized to improve performance. From our experiments and the evaluations, it can be figured out that before the Semantic Web eventually creates order out of chaos on the Web, an ontology-driven approach applied to traditional search technology is a promising way to bring semantics to current Web in the searching area.