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- New kinds of highly popular user-centered applications such as blogs, folksonomies, and wikis, have come to be known as "Web 2.0". The reason for their immediate success is the fact that no specific skills are needed for participating. These new kinds of tools do not only provide data but also generate a lot of weakly structured meta data. One perfect example is tagging. Here users add tags to a resource which can be seen as a kind of meta data. Tags are supposed to describe, from the users point of view, the resource. Such meta data is easy to produce but it lacks any kind of formal grounding used in the Semantic Web. On the other hand the Semantic Web complements the described bottom-up effort of the Web 2.0 community in a top down manner as, one of its central points is a fixed vocabulary, typed relations and a stronger knowledge representation based on some kind of ontology. Such structure is typically something users have in mind when they provide their information. But for researcher it is hidden in the data and needs to be extracted. Techniques to analyze network structures or weak knowledge representations like those found in the Web 2.0 have a long tradition in different other disciplines, like social network analysis, machine learning or data mining. These kinds of automatic mechanisms are necessary to extract the hidden information and to reveal the structure in a way that the Semantic Web community can benefit from, and thus provide added value to the end user. On the other hand the established way to represent knowledge gained from the unstructured data can be beneficial for the Web 2.0 in that it provides Web 2.0 users with enhanced SemanticWeb features to structure their data. Our aim is to bridge the gap between the Semantic Web and the upcoming Web 2.0 communities. Since both communities work on network like data structures, analysis methods from different fields of research could form a link between those communities. Techniques can be, but are not limited to, social network analysis, graph analysis, machine learning or data mining methods. By bringing together researchers from different fields, we aim to achieve this goal.
- OntoMat-Annotizer is a user-friendly interactive webpage annotation tool. It supports the user with the task of creating and maintaining ontology-based OWL-markups i.e. creating of OWL-instances, attributes and relationships. It include an ontology browser for the exploration of the ontology and instances and a HTML browser that will display the annotated parts of the text. It is Java-based and provide a plugin interface for extensions. The intended user is the individual annotator i.e., people that want to enrich their web pages with OWL-meta data. Instead of manually annotating the page with a text editor, say, emacs, OntoMat allows the annotator to highlight relevant parts of the web page and create new instances via drag?n?drop interactions. It supports the meta-data creation phase of the lifecycle. It is planned that a future version will contain an information extraction plugin, that offers a wizard which suggest which parts of the text are relevant for annotation. That aspect will help to ease the time-consuming annotation task.
- SWOOP is a tool for creating, editing, and debugging OWL ontologies. It was produced by the MIND lab at University of Maryland, College Park, but is now an open source project with contributers from all over.
- Navigational databases are characterized by the fact that objects in the database are found primarily by following references from other objects. Traditionally navigational interfaces are procedural, though one could characterize some modern systems like XPath as being simultaneously navigational and declarative.
- Tom Gruber is an innovator in technologies that extend human intelligence. Building on early work in computer-mediated learning and artificial intelligence, he focuses on creating environments for collective intelligence.
- Gruber said he saw four different ways of adding meta-tags to material, ranging from the loosest to the most strict: 1.Folksonomy (informal, user-defined) 2.Controlled vocabularies (the user must deploy a set of defined terms) 3.Taxonomy (Pre-defined terms in which specific terms are subsets of more general terms) 4.Ontology (A rich set of relationships is mapped out among all the terms)
- Medical Subject Headings (MeSH) is a huge controlled vocabulary (or metadata system) for the purpose of indexing journal articles and books in the life sciences. Created and updated by the United States National Library of Medicine (NLM), it is used by the MEDLINE/PubMed article database and by NLM's catalog of book holdings. MeSH can be browsed and downloaded free of charge on the Internet. The yearly printed version was discontinued in 2007.
- SNOMED (Systematized Nomenclature of Medicine), is a systematically organized computer processable collection of medical terminology covering most areas of clinical information such as diseases, findings, procedures, microorganisms, pharmaceuticals etc. It allows a consistent way to index, store, retrieve, and aggregate clinical data across specialties and sites of care. It also helps organizing the content of medical records, reducing the variability in the way data is captured, encoded and used for clinical care of patients and research.
- The User Interface Design group studies new user interface techniques, focusing on two areas: * Usable automation. Our goal is for every computer user to be able to harness the power of automation for handling repetitive, time-wasting, or error-prone tasks, without having to struggle with the complexity of programming. * Usable security. Effective security depends on good user interfaces. A door may have the strongest lock in the world, but if authorized users can't open and close it easily, and have to leave it open in order to get their jobs done, then it has no security at all. We are studying ways to rethink computer security so that it is not only secure against attack but also usable by ordinary people.
- Piggy Bank is a Firefox extension that turns your browser into a mashup platform, by allowing you to extract data from different web sites and mix them together. Piggy Bank also allows you to store this extracted information locally for you to search later and to exchange at need the collected information with others.
- The OBO Foundry is a collaborative experiment involving developers of science-based ontologies who have established a set of principles for ontology development with the goal of creating a suite of orthogonal interoperable reference ontologies in the biomedical domain. Currently the OBO Foundry ontologies form a part of the wider Open Biomedical Ontologies family, as listed below. In the longer term it is intended that the OBO Foundry will form one collection of ontologies alongside other such collections within the NCBO Bioportal.
- The NCI Thesaurus is an ontology-like vocabulary that includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities; anatomy; agents, drugs and chemicals; genes and gene products and so on.
- Welcome to the a project of the Structural Informatics Group at the University of Washington Department of Biological Structure and Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics.
- The Buffalo Ontology Site is maintained by Barry Smith. It contains information on ontology, on the history of ontology, and on contemporary ontology and its applications, with special reference to the area of biomedicine. It provides links to other ontology sites and announcements of relevant conferences and publications.
- Ontology still has very much the sense of a world view, a means of viewing and organizing and conceptualizing and defining a domain of interest
- Ontology Summit 2007 - Ontology, Taxonomy, Folksonomy: Understanding the Distinctions
- The purpose of the Institute is to foster the development and dissemination of the theory and applications of statistics and probability.
Publikationen (verstecken)
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