Deep Web database clustering is a key operation in organizing Deep Web resources. Cosine similarity in Vector Space Model (VSM) is used as the similarity computation in traditional ways. However it cannot denote the semantic similarity between the contents of two databases. In this paper how to cluster Deep Web databases semantically is discussed. Firstly, a fuzzy semantic measure, which integr...

In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the representation of text documents. The basic hypothesis examined is that considering semantic relatedness between two text documents may improve the performance of the text document clust...

In this paper we introduce a new approach to multimedia data semantic characterization and in particular television programmes fingerprinting, based on multimodal content analysis and fuzzy clustering. The definition of the fingerprints can be seen as a space transformation process, which maps each programme description from the surrogate vector space to a new vector space, defined through a fu...

This paper proposes an open, extensible control plane for a global event service, based on semantically rich messages. This is based on the novel application of control plane separation and semantic-based matching to Content-Based Networks. Here we evaluate the performance issues involved in attempting to perform ontology-based reasoning for content-based routing. This provides us with the moti...

Although Semantic Web technology is increasingly becoming more and more important, tagging remains a popular method to describe Web resources. Therefore it is important to address the issues that are found in current tagging search engines, such as Flickr. We find that the free nature of tagging results in many issues for tag search engines, such as synonyms, homonyms, syntactic variations, etc...

The paper describes the application of kMeans, a standard clustering technique, to the task of inducing semantic classes for German verbs. Using probability distributions over verb subcategorisation frames, we obtained an intuitively plausible clustering of 57 verbs into 14 classes. The automatic clustering was evaluated against independently motivated, handconstructed semantic verb classes. A ...

This paper intend to present an approach to analyse the change of word meaning based on word embedding, which is a more general method to quantize words than before. Through analysing the similar words and clustering in different period, semantic change could be detected. We analysed the trend of semantic change through density clustering method called DBSCAN. Statics and data visualization is ...

In previous research in automatic verb classification, syntactic features have proved the most useful features, although manual classifications rely heavily on semantic features. We show, in contrast with previous work, that considerable additional improvement can be obtained by using semantic features in automatic classification: verb selectional preferences acquired from corpus data using a f...

We propose a system which is based on fuzzy c-means clustering and automatically organizes a collection of music files according to musical surface characteristics (e.g., zero crossings, short-time energy, etc.) and tempo. Our system is complemented by semantic metadata which offers the user the ability to check the clustering result, correct existing textual meta-information (such as genre and...