Group items tagged

A Semantic Map is kind of like a dictionary, in that it's a representation of Cognition's ability to define things. Cognition claims that its Semantic Map has over 10 million semantic connections; over 4 million semantic contexts (word meanings that create contexts for specific meanings of other related words); over 536,000 word senses (word and phrase meanings); 75,000 concept classes (or synonym classes of word meanings); 7,500 nodes in the technology's ontology or classification scheme; and 506,000 word stems (roots of words) for the English language.

Welcome to the National Center for Biomedical Ontology's BioPortal. BioPortal is a Web-based application for accessing and sharing biomedical ontologies. New features in BioPortal 2.0 include: * Full ontology navigation using Flash visualization * Web-service access to BioPortal content and capabilities, which enables developers to use our BioPortal services in their tools. * Ability to add Marginal Notes to classes in BioPortal ontologies, a feature that enables the community to comment on ontologies and to discuss their contents * Ability to create Point to Point Mappings between concepts in different BioPortal ontologies * Bulk export of ontology-to-ontology mappings in RDF format * Navigation of multiple ontologies, which enables users to have several ontologies opened simultaneously in different tabs in the user interface * URIs for all ontology content, which enable developers to access and share BioPortal content from their applications * Improved support through Protégé for ontologies in OWL format

Andreas Hotho's talk more specifically addressed the search for methods to identify tags which describe the same concept (or a more specific / a more general concept respectively) within a folksonomy. He suggested two approaches: 1. Applying measures directly to folksonomy statistics, allowing to describe tags as a vector; e.g. co-occurrence frequency and FolkRank could serve as a similarity measure (with these two having a tendency towards high-frequency tags) or a cosine method (which is more likely to produce "siblings") 2. Looking up tags in an external thesaurus/vocabulary (for instance achieving semantic grounding by mapping a tag and its most similar tags with Wordnet Synsets)

Upper Mapping and Binding Exchange Layer A lightweight, subject concept reference structure for the Web

Belvedere 4.1 is designed to help support problem-based collaborative learning scenarios with concept and evidence moodels, and provides multiple representational views (tables and graphs) on those models. Belvedere was originally intended to help secondary school students learn critical inquiry skills that they can apply in everyday life as well as in science, but can be adopted to other applications as well.

Upper Mapping and Binding Exchange Layer A lightweight, subject concept reference structure for the Web

Natural languages are easy to learn by infants, they can express any thought that any adult might ever conceive, and they accommodate the limitations of human breathing rates and short-term memory. The first property implies a finite vocabulary, the second implies infinite extensibility, and the third implies a small upper bound on the length of phrases. Together, they imply that most words in a natural language will have an open-ended number of senses - ambiguity is inevitable.To achieve a comparable level of flexibility with formal ontologies, this paper proposes an organization with a dynamically evolving collection of formal theories, systematic mappings to formal concept types and informal lexicons of natural language terms, and a modularity that allows independent distributed development and extension of all resources, formal and informal.

Software for mindmapping and information organisation