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.

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.

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.

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.

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.