Abstract Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, which supports navigation using... Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, which supports navigation using domain-specific concepts as well as keywords that are characterizing the text corpus. The KbC graph is a weighted graph, created by tightly integrating keywords extracted from documents and concepts obtained from domain taxonomies. Documents in the corpus are associated to the nodes of the graph based on evidence supporting contextual relevance; thus, the KbC graph supports contextually informed access to these documents. The construction of the KbC graph relies on a spreading-activation like technique which mimics the way the brain links and constructs knowledge. In this paper, we also present CoSeNa (Context-based Search and Navigation) system that leverages the KbC model as the basis for document exploration as well as contextuallyinformed media integration. Read More Read Less

Abstract Distributed multimedia applications have emerged at an increasing rate during the last decade in several domains (video conferencing, e-health, virtual meeting rooms, etc). This has created several new challenging problems related to data integration and fragmentation, user-oriented and adaptive interfaces, real time and network performances, etc. In this article, we focus on the problem of data(base) fragmentation, initially consisting of... Distributed multimedia applications have emerged at an increasing rate during the last decade in several domains (video conferencing, e-health, virtual meeting rooms, etc). This has created several new challenging problems related to data integration and fragmentation, user-oriented and adaptive interfaces, real time and network performances, etc. In this article, we focus on the problem of data(base) fragmentation, initially consisting of reducing irrelevant data accesses by grouping data frequently accessed together in dedicated segments, in a multimedia context. We mainly address the issue of query and predicate implication required in current fragmentation algorithms, and provide a formal approach to identify such implications, in order to partition multimedia data efficiently. It is worthy to note that our approach is capable of considering multimedia-based as well as semantic comparisons, based on a generalized notion of functional dependencies, which are called multimedia functional dependencies. Read More Read Less

Abstract The volume of documents in the digital repositories numbers in thousands and is increasing constantly, in such a scenario it becomes a very important issue to organize and retrieve these documents in a way that relates to the human mind. In this paper, we present a novel approach to classify the documents in a digital repository and find the semantically... The volume of documents in the digital repositories numbers in thousands and is increasing constantly, in such a scenario it becomes a very important issue to organize and retrieve these documents in a way that relates to the human mind. In this paper, we present a novel approach to classify the documents in a digital repository and find the semantically significant keywords related to those documents to make the organization and the retrieval of the documents faster and more efficient. We approach this problem using Probabilistic Latent Semantic Analysis with incomplete training data to organize them and mark the relevant keywords. This approach makes the classification faster and instead of the unlabeled clustering gives classification with well defined topics relating to human logic. Read More Read Less