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Large-scale RDF Graph Visualization Tools

Large-scale RDF Graph Visualization Tools
AI3 Assembles 26 Candidate Tools The pending UMBEL subject concept “backbone” ontology will involve literally thousands of concepts. In order to manage and view such a large structure, a concerted effort to find suitable graph visualization software was mounted. A subsequent post will present the surprise winner of our evaluation. Starting Resources See Various Example Visualizations For grins, you may also like to see various example visualizations, most with a large-graph bent: Software Options Here is the listing of 26 candidate graph visualization programs assembled to date: Cytoscape – this tool, based on GINY and Piccolo (see below), is under active use by the bioinformatics community and highly recommended by Bio2RDF.org GINY implements a very innovative system for sub-graphing and allows for stunning visuals. headline: Large-scale RDF Graph Visualization Tools alternativeHeadline: author: Mike Bergman image: description: articleBody: see above

Metadata? Thesauri? Taxonomies? Topic Maps! Making sense of it all Abstract To be faced with a document collection and not to be able to find the information you know exists somewhere within it is a problem as old as the existence of document collections. Information Architecture is the discipline dealing with the modern version of this problem: how to organize web sites so that users actually can find what they are looking for. Information architects have so far applied known and well-tried tools from library science to solve this problem, and now topic maps are sailing up as another potential tool for information architects. The paper argues that topic maps go beyond the traditional solutions in the sense that it provides a framework within which they can be represented as they are, but also extended in ways which significantly improve information retrieval. Table of contents 1. The task of an information architect is to create web sites where users can actually find the information they are looking for. 2. 2.1. Metadata 2.2. title

Ontology (information science) In computer science and information science, an ontology formally represents knowledge as a hierarchy of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts.[1][2] Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework. The term ontology has its origin in philosophy and has been applied in many different ways. The word element onto- comes from the Greek ὤν, ὄντος, ("being", "that which is"), present participle of the verb εἰμί ("be"). According to Gruber (1993): Common components of ontologies include:

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