Digital recording of performing arts: formats and conversion

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Finding and exploring commonalities between researchers using the resXplorer

Researcher community produces a vast of content on the Web. We assume that every researcher interest oneself in events, persons and findings of other related community members who share the same interest. Although research related archives give access to their content most of them lack on analytic services and adequate visualizations for this data. This work resides on our previous achievements[1,2,3,4] we made on semantically and Linked Data driven search and user interfaces for Research 2.0. We show how researchers can find and visually explore commonalities between each other within their interest domain, by introducing for this matter the user interface of "ResXplorer", and underlying search infrastructure operating over Linked Data Knowledge Base of research resources. We discuss and test most important components of "ResXplorer" relevant for detecting commonalities between researchers, closing up with conclusions and outlook for future work.Researcher community produces a vast of content on the Web. We assume that every researcher interest oneself in events, persons and findings of other related community members who share the same interest. Although research related archives give access to their content most of them lack on analytic services and adequate visualizations for this data. This work resides on our previous achievements[1,2,3,4] we made on semantically and Linked Data driven search and user interfaces for Research 2.0. We show how researchers can find and visually explore commonalities between each other within their interest domain, by introducing for this matter the user interface of "ResXplorer", and underlying search infrastructure operating over Linked Data Knowledge Base of research resources. We discuss and test most important components of "ResXplorer" relevant for detecting commonalities between researchers, closing up with conclusions and outlook for future work.P

A distance-based approach for semantic dissimilarity in knowledge graphs

In this paper, we introduce a distance-based approach for measuring the semantic dissimilarity between two concepts in a knowledge graph. The proposed Normalized Semantic Web Distance (NSWD) extends the idea of the Normalized Web Distance, which is utilized to determine the dissimilarity between two textural terms, and utilizes additional semantic properties of nodes in a knowledge graph. We evaluate our proposal on the knowledge graph Freebase, where the NSWD achieves a correlation of up to 0.58 with human similarity assessments on the established Miller-Charles benchmark of 30 term-pairs. These preliminary results indicate that the proposed NSWD is a promising approach for assessing semantic dissimilarity in very large knowledge graphs