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Semantic Scholar. XML Metadata Interchange (XMI) Beispiele. Download der XMI-Datei <? Xml version="1.0" encoding="UTF-8"? Download der Datei <? Service provided by Mario Jeckle Generated: 2004-06-07T12:31:36+01:00 Feedback SiteMap This page's original location: RDF description for this page. XML Metadata Interchange. It can be used for any metadata whose metamodel can be expressed in Meta-Object Facility (MOF).

The most common use of XMI is as an interchange format for UML models, although it can also be used for serialization of models of other languages (metamodels). Overview[edit] In the OMG vision of modeling, data is split into abstract models and concrete models. The abstract models represent the semantic information, whereas the concrete models represent visual diagrams. Abstract models are instances of arbitrary MOF-based modeling languages such as UML or SysML. One purpose of XML Metadata Interchange (XMI) is to enable easy interchange of metadata between UML-based modeling tools and MOF-based metadata repositories in distributed heterogeneous environments.

Integration of industry standards[edit] XMI integrates four industry standards: The integration of these four standards into XMI allows tool developers of distributed systems to share object models and other metadata. See also[edit] Talks/09_04_standardization_of_ontologies_paper.pdf. :: Ontology Matching :: Semantic Web. Gensim: Topic modelling for humans.

Emotions ontology for collaborative modelling and learning of emotional responses. Open Access Highlights Affective applications require a common way to represent emotion knowledge. Ontologies provide rich semantic models for emotion knowledge modelling. EmotionsOnto is a generic ontology for describing emotions. EmotionsOnto is used in EmoCS to collaboratively collect emotion common sense. Currently, emotion from user input but Brain–Computer Interfaces being tested. Abstract Emotions-aware applications are getting a lot of attention as a way to improve the user experience, and also thanks to increasingly affordable Brain–Computer Interfaces (BCI). Keywords Emotion; Ontology; Collaborative learning; Social networks; Knowledge representation; Affective computing 1. The emotional dimension of the interaction of humans with computers was considered for a long time a marginal factor (Brave & Nass, 2002).

Interest in this area is driven by a wide spectrum of promising applications, such as virtual reality, smart surveillance or perceptual interfaces (Tao & Tan, 2005). Teaching: RDF. Ontology of the emotions. J.Hastings Researchers across the life sciences today face the need to integrate vast quantities of data and information deriving from heterogeneous sources, each of which is proliferating at exponential growth rates.

Modern computational facilities are designed to assist in this task with ever-larger Internet-based databases supported by integrated searching and filtering utilities. However, the separation of raw data across different research and application domains makes inter-domain searching and integration intractable without the assistance of a computational representation for the semantics of the domains. Ontologies are standardized representations of the types of entities found in different domains, constructed in such a way as to allow computerised logical reasoning within and across the associated domains of data [1]. References 1. 2. 3. 4. 5. 6. 7. Basic Formal Ontology (BFO) | Home. The Basic Formal Ontology (BFO) is a small, upper level ontology that is designed for use in supporting information retrieval, analysis and integration in scientific and other domains. BFO is a genuine upper ontology. Thus it does not contain physical, chemical, biological or other terms which would properly fall within the coverage domains of the special sciences.

BFO is used by more than 130 ontology-driven endeavors throughout the world. The BFO project was initiated in 2002 under the auspices of the project Forms of Life sponsored by the Volkswagen Foundation. The theory behind BFO was developed first by Barry Smith and Pierre Grenon and presented in a series of publications listed here. Since then important contributions to BFO have been made by many people, including: ...and by more than hundred other members of the BFO Discussion Group. News BFO 2.0 Now Released For more information see here and for a video introduction to the BFO 2.0 release here. Contextual/Semantic Search Engines. Conceptual and Practical Distinctions in the Attributes Ontology. Download as PDF Logicians going back at least as far as Charles S. Peirce [1] — and computer scientists as early as the entity-relationship (ER) model from the mid-1970s [2] — have made the relations-attributes distinction for predicates relating to data objects.

There are both conceptual and practical bases for these distinctions. This article elaborates upon the relations-attributes distinction in the UMBEL Attributes Ontology. I also try to precisely define my terms because terminology is overlapping and confusing amongst competing data models and standards. In one of its first communiques in 1999 regarding the Resource Description Framework, its sponsor, the W3C (World Wide Web Consortium), noted the RDF data model was a member of the entity-relationship modeling family [3]. Recap of the UMBEL Attributes Ontology My prior article introduced the Attributes Ontology (AO), a new module and extension about to be released for UMBEL (Upper Mapping and Binding Exchange Layer)

. [3] Ralph R. Semantic Web. WEB SEMÁNTICA. The Semantic Web. Ontologies. Skip to end of metadataGo to start of metadata Ontologies are a list of related terms, both nodes (concepts) and links (relationships), which can be imported and added to a VUE map to provide semantic meaning. VUE can import ontologies defined in RDF-S or OWL formats allowing for the creation of concept maps from pre-defined object and relationship types. A defined mapping vocabulary scaffolds map creation and supports computer-assisted map comparison and assessment. The visual characteristics of objects and relationships defined in an ontology may also be styled via a CSS file (a default CSS file ships with VUE). Ontological terms are searchable. Both ontological terms and ontological membership keywords can be added to nodes. To import an ontology: From the menu bar, select Windows > Ontologies.

Nodes that contain an ontological term will display the star icon. Ontological Membership (terms associated with the node) can be inspected by right-clicking a node, and then selecting Node Info. Semantic technologies. Lexical Semantics Resources for English. Ground-breaking semantic technology delivering clear business benefits. In a recent post, we talked about the successful conclusion of our Knowledge Transfer Partnership programme: Jie Gao, a postgraduate student from the University of Southampton, worked on-site at ActiveStandards for two years to help us develop cutting-edge semantic-analysis technology. But what exactly is semantic technology? How did we go about designing, testing and implementing it? How does it work within ActiveStandards? And more importantly, what does it mean to you and to your business?

Here are the answers to these questions and others. Q: What is semantic technology? A: Put simply, semantic technology provides ways to make information more universally understandable between different systems – enabling meaning-based interpretation of data and applications. Semantic analysis extracts meaning from unstructured content, for example, by automatically detecting topics, concepts, entities and relationships.

Q: What can Semantic Technology do for you? Content Insight reports. Article - An article, such as a news article or piece of investigative report. Newspapers and magazines have articles of many different types and this is intended to cover them all. Library Success: A Best Practices Wiki - Library Success: A Best Practices Wiki. Welcome to Library Success: A Best Practices Wiki. This wiki was created to be a one-stop shop for great ideas and information for all types of librarians. All over the world, librarians are developing successful programs and doing innovative things with technology that no one outside of their library knows about.

There are lots of great blogs out there sharing information about the profession, but there is no one place where all of this information is collected and organized. That's what we're trying to do. If you've done something at your library that you consider a success, please write about it in the wiki or provide a link to outside coverage. If you have materials that would be helpful to other librarians, add them to the wiki.

And if you know of a librarian or a library that is doing something great, feel free to include information or links to it. This wiki is not run by any commercial entity and does not represent any commercial interests. Community.