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.

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.

In current Semantic Web community, some researches have been done on ranking ontologies, while very little is paid to ranking vocabularies within ontology. However, finding important vocabularies within a given ontology will bring benefits to ontology indexing, ontology understanding and even ranking vocabularies from a global view. In this paper, Vocabulary Dependency Graph (VDG) is proposed to model the dependencies among vocabularies within an ontology, and Textual Score of Vocabulary (TSV) is established based on the idea of virtual documents. And then a Double Focused PageRank algorithm is applied on VDG and TSV to rank vocabulary within ontology. Primary experiments demonstrate that our approach turns out to be useful in finding important vocabularies within ontology.

On the investigation of linguistic techniques used in ontology matching, we propose a new idea of virtual documents to pursue a cost-effective approach to linguistic matching in this paper. Basically, as a collection of weighted words, the virtual document of a URIref declared in an ontology contains not only the local descriptions but also the neighboring information to reect the intended meaning of the URIref. Document similarity can be computed by traditional vector space techniques, and then be used in the similaritybased approaches to ontology matching. In particular, the RDF graph structure is exploited to dene the description formulations and the neighboring operations. Experimental results show that linguistic matching based on the virtual documents is dominant in average F-Measure as compared to other three approaches. It is also demonstrated by our experiments that the virtual documents approach is cost-effective as compared to other linguistic matching approaches.

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.