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The following topics are dealt with: knowledge engineering and data mining; multimedia computing; natural language processing; services and software engineering; middleware for the semantic Web; mobile semantic computing.
Geo-temporal criteria are important for filtering, grouping and prioritizing information resources. This presents techniques for extracting semantic geo-temporal information from text, using simple text mining methods that leverage on a gazetteer. A prototype system, implementing the proposed methods and capable of displaying information over maps and timelines, is described. This prototype can take...
Blogs, discussion forums and social networking sites are an excellent source for people's opinions on a wide range of topics. We examine the application of voting theory to "information mashups" - the combining and summarizing of data from the multitude of often-conflicting sources. This paper presents an information mashup in the music domain: a Top 10 artist chart based on user comments...
This paper presents a novel model for social network analysis in which, rather than analyzing the quantity of relationships (co-authorships, business relations, friendship, etc.), we analyze their communicative content. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular themes. The social analyst is then able to perform a study of...
The research presented in this paper focuses on the pre-processing stage of the clustering process, proposing a novel indexing technique which goes beyond the syntax of terms; trying to capture their unambiguous meaning from their context and to derive a set of concepts to be used to represent the documents. This approach overcomes some of the major drawbacks deriving from the use of bag of words...
The proposed work illustrates how "primitive concepts" can be automatically induced from a text corpus. The primitive concepts are identified by the orthonormal axis of a "conceptual" space induced by a methodology inspired tothe latent semantic analysis approach. The methodology represents a natural language sentence by means of a set of rotations of an orthonormal basis in the...
This paper presents a method to induce semantic taxonomies by applying the lattice theoretical technology of formal concept analysis to relations of predicates extracted from a natural language corpus. Our initial research results are in support of a future overall methodology for the semi-automatic construction of semantic hierarchies from term relations extracted from text. We describe our formal...
One challenge for relevance ranking in Web search is underspecified queries. For such queries, top-ranked documents may contain information irrelevant to the search goal of the user; some newly-created relevant documents are ranked lower due to their freshness and to the large number of existing documents that match the queries. To improve the relevance ranking for underspecified queries requires...
Interpreting the semantics of an image is a hard problem. However, for storing and indexing large multimedia collections, it is essential to build systems that can automatically extract semantics from images. In this research we show how we can fuse content and context to extract semantics from digital photographs. Our experiments show that if we can properly model context associated with media, we...
The indexing of spatio-temporal data is important for retrieval by spatio-temporal queries. The previous techniques on spatio-temporal indexing miss the semantics of the application since they are usually based on traditional indexing structures that has little to no semantic information incorporated. In those systems, the semantic queries were executed by using the low-level index structures. In...
Content-based image retrieval - CBIR uses visual content (low-level features) of images such as color, texture, shape, etc. to representand to index images. Extensive experiments on CBIR show that low-level features not represent exactly the high-level semantic concepts and can fail when used to retrieve similar images. In order to overpass this problem, different approaches aim to propose new methods...
In this paper, an effective multi-concept classifier is proposed for video semantic concept detection. The core of the proposed classifier is a supervised classification approach called C-RSPM (collateral representative subspace projection modeling) which is applied to a set of multimodal video features for knowledge discovery. It adaptively selects non-consecutive principal dimensions to form an...
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