Access control is an important issue among the security problems of resources in distributed systems. In order to enable entities in distributed systems to understand and interpret policies correctly, common concern is drawn to the problem of expressing access control policies with semantic information. In this paper, we introduce how to express access control policies based on OWL and SWRL. It includes a definition of OWL ontology to describe the terms and a declaration of SWRL rules to explicit the relationship between properties. Finally some use cases are given to explain how to express policies in form of rule using the terms defined beforehand.

Ontology matching is an important task to achieve interoperation between semantic web applications using different ontologies. Structural similarity plays a central role in ontology matching. However, the existing approaches rely heavily on lexical similarity, and they mix up lexical similarity with structural similarity. In this paper, we present a graph matching approach for ontologies, called GMO. It uses bipartite graphs to represent ontologies, and measures the structural similarity between graphs by a new measurement. Furthermore, GMO can take a set of matched pairs, which are typically previously found by other approaches, as external input in matching process. Our implementation and experimental results are given to demonstrate the eec tiveness of the graph matching approach.

Falcon-AO is an automatic tool for aligning ontologies. There are two matchers integrated in Falcon-AO: one is a matcher based on linguistic matching for ontologies, called LMO; the other is a matcher based on graph matching for ontologies, called GMO. In Falcon-AO, GMO takes the alignments generated by LMO as external input and outputs additional alignments. Reliable alignments are gained through LMO as well as GMO according to the concept of reliability. The reliability is obtained by observing the linguistic comparability and structural comparability of the two ontologies being compared. We have performed Falcon-AO on tests provided by OAEI 2005 campaign and got some preliminary results. In this paper, we describe the architecture and techniques of Falcon-AO in brief and present our results in more details. Finally, comments abouttest cases and lessons learnt from the campaign will be presented.

It is tedious and error-prone to query search engines manually in order to accumulate a large body of factual information. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, or fuse information from multiple documents. This paper introduces DynamicView, a SemanticWeb application for researchers to query, browse and visualize distribution and evolution of research areas in computer science. Present and historical web pages of top 20 universities in USA and China are analyzed, and research areas of faculties in computer science are extracted automatically by segmentation based algorithm. Different ontologies of ACM and MST classification systems are combined by SKOS vocabularies, and the classification of research areas is learned from the ACM Digital Library. Query results including numbers of researchers and their locations are visualized in SVG map and animation. Interestingly, great differences of hot topics do exist between the two countries, and the number of researchers in certain areas changed greatly from the year 2000 to 2005.

In the Semantic Web, RDF (Resource Description Framework) and RDF Schema are commonly used to describe metadata. There are a great many RDF data in current web, therefore, efficient storage and retrieval of large RDF data sets is required. So far, several RDF storage and query system are developed. According to the inference strategy they used, they can be classified into two categories, one exclusively use forward chaining strategy; the other exclusively use backward chaining strategy. In most cases, the query performance of the former is superior to that of the latter. However, in some cases, the disadvantage of larger storage space may at some point outweigh the advantage of faster querying. Further, the existing systems that exclusively use forward chaining strategy have not yet presented a good solution to the deletion operation by now. In this paper, we design a RDF storage and query framework with flexible inference strategy, which can combine forward and backward chaining inference strategies. In addition, a new solution to the deletion operation is also given within our framework. The feasibility of our framework is illustrated by primary experiments.