Schema Evolution publication categorizer

We develop a new algorithm for clustering search results. Differently from many other clustering systems that have been recently proposed as a post-processing step forWeb search engines, our systemis not based on phrase analysis inside snippets, but instead uses latent semantic indexing on thewhole document content.Amain contribution of the paper is a novel strategy – called dynamic SVDclustering – to discover the optimal number of singular values to be used for clustering purposes.

Research has investigated mappings among data sources under two perspectives. On one side, there are studies of practical tools for schema mapping generation; these focus on algorithms to generate mappings based on visual specifications provided by users. On the other side, we have theoretical researches about data exchange. These study how to generate a solution -- i.e., a target instance -- given a set of mappings usually specified as tuple generating dependencies.

Ontologies proliferate with the progress of the Semantic Web. Ontology matching is an important way of establishing interoperability between (Semantic) Web applications that use different but related ontologies. Due to their sizes and monolithic nature, large ontologies regarding real world domains bring a new challenge to the state of the art ontology matching technology. In this paper, we propose a divide-and-conquer approach to matching large ontologies.

Whenever transformation of data is used to bridge the gap of different data formats, and a query is given in the destination format, query reformulation can speed up the transformation of data. We achieve this speed-up in transformation when only the required data segment, described by the computed reformulated query, is transformed. Whenever the required section of data is not too large, query reformulation allows transformation on demand, even when the input data is large.