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, we present a brief overview of Falcon-AO (version 0.7): a practical ontology matching system with acceptable to very good performance, a flexible architecture, and a number of unique features. We also show some preliminary results of Falcon-AO for this year's OAEI campaign: evaluation on seven different matching tasks.

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.

Ontology matching is a crucial task to enable interoperation between Web applications using different but related ontologies. Today, most of the ontology matching techniques are targeted to find 1:1 mappings. However, block mappings are in fact more pervasive. In this paper, we discuss the block matching problem and suggest that both the mapping quality and the partitioning quality should be considered in block matching. We propose a novel partitioning-based approach to address the block matching issue. It considers both linguistic and structural characteristics of domain entities based on virtual documents, and uses a hierarchical bisection algorithm for partitioning. We set up two kinds of metrics to evaluate of the quality of block matching. The experimental results demonstrate that our approach is feasible.