describe their services in terms of the ontology, while educators can search for them using domain concepts. The integration of this enhanced service search mechanism in Gridcole, a real grid-based CSCL system, is described to illustrate its performance. 1.

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Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared

), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. As in other regionbased retrieval systems, an image is represented by a set of regions, roughly corresponding to objects

and leverages the advantages of both so that they complement each other. We have developed collaborative prototypes in PowerPoint, Impress, and ReviewPlus applications to demonstrate this, using Message Oriented Middleware (NaradaBrokering Message Service) to provide a Grid messaging

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs

predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering

Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation, and knowledgebase development. We have developed a set of tools and services to support the process of achieving consensus on common shared ontologies by geographically

A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much

This article presents a measure of semantic similarityinanis-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach