Benoît Sagot - Accueil. INRIAGforge: SYNTAX: Information sur le projet. INRIAGforge: Arbre des projets. INRIAGforge: Telex2: Information sur le projet. INRIAGforge: Stateful SIP Firewall: Information sur le projet. INRIAGforge: Simple Screen Viewer: Information sur le projet. INRIAGforge: Librecours: Information sur le projet. Scribo. Un article de Wikipédia, l'encyclopédie libre. Objectif[modifier | modifier le code] L'objectif est de proposer des algorithmes et outils libres pour l'annotation semi-automatique et collaborative de documents numériques. L'approche est fondée sur l'extraction de connaissances à partir de textes et d'images. Les acteurs du projet sont l'AFP, le CEA LIST, l'INRIA, le LRDE (EPITA), Mandriva, Nuxeo, Proxem, Tagmatica et XWiki (coordinateur).
Financement[modifier | modifier le code] Le projet est financé par l'État et les collectivités territoriales franciliennes dans le cadre du 5e appel à projets lancé par le fonds de compétitivité des entreprises (FCE). Participants au projet[modifier | modifier le code] Retombées[modifier | modifier le code] Les composants réalisés seront intégrés dans les suites logicielles respectives des éditeurs Nuxeo, Proxem et XWiki. L'ensemble des développements effectués dans le cadre du projet sera disponible sous licence libre compatible GNU LGPL. Partenaires : SCRIBO - Welcome to SCRIBO.ws. Home - Common Tag. QuickStartGuide - Common Tag. From Common Tag Common Tags are defined using RDFa, a standard format for expressing structured data within HTML. This guide was designed to help you get started using the Common Tag format even if you don't know RDFa.
The examples can be used as simple cut-and-paste recipes to tag your content using the Common Tag format. Common Tags are not only useful for identifying the concepts covered in your content, but if you reference content elsewhere on the web, Common Tags can be used to indicate the concepts covered in that external content as well. The Common Tag format uses the URIs of concepts defined on the web as a way of anchoring the meaning of Tag objects (Tags). The two databases of structured content used in the examples below are: Freebase - A community managed database of concepts DBPedia - A machine readable version of Wikipedia For the full technical specification of Common Tag, visit the Specification section of the site.
Tagging whole documents Tagging an embedded video. Commontag : Common Tag. Vienne (Autriche) Blog Smarter. Download Zemanta for your browser. Zemanta. Linked Data | Linked Data - Connect Distributed Data across the.
Top-Down: A New Approach to the Semantic Web. Earlier this week we wrote about the classic approach to the semantic web and the difficulties with that approach. While the original vision of the layer on top of the current web, which annotates information in a way that is "understandable" by computers, is compelling; there are technical, scientific and business issues that have been difficult to address. One of the technical difficulties that we outlined was the bottom-up nature of the classic semantic web approach.
Specifically, each web site needs to annotate information in RDF, OWL, etc. in order for computers to be able to "understand" it. As things stand today, there is little reason for web site owners to do that. But there are alternative approaches. In this post, we will look at the solution that we call the top-down approach to the semantic web, because instead of requiring developers to change or augment the web, this approach leverages and builds on top of current web as-is. Why Do We Need The Semantic Web? Conclusion. Semantic Web: Difficulties with the Classic Approach. Summary: The original vision of the semantic web as a layer on top of the current web, annotated in a way that computers can "understand," is certainly grandiose and intriguing.
Yet, for the past decade it has been a kind of academic exercise rather than a practical technology. This article explores why; and what we can do about it. Update: Part 2 is available now Top-Down: A New Approach to the Semantic Web The semantic web is a vision pioneered by Sir Tim Berners-Lee, in which information is expressed in a language understood by computers. The purpose of the semantic web is to enable computers to "understand" semantics the way humans do. For example, in a New York Times article, written earlier this year, John Markoff discussed a scenario where you would be able to ask a computer to find you a low budget vacation, keeping in mind that you have a 3 year old child.
But while the vision of a semantic web is powerful, it has been a over a decade in making. Classic Semantic Web Review 1. Libérons les données ! Il me parait tous les jours plus clair que nos amis les données veulent partir en vacance. Ce besoin de voyage est d’autant plus fort que le coût d’un trajet numérique Boston / Bangalore tend vers zéro. Et puis après tout, n’est ce pas dans notre nature de partager, copier, diffuser l’information ? Nous sommes des primates, nous apprenons par mimétisme. Et dans un monde hautement interconnecté et numérique, où il n’a jamais été aussi simple d’apprendre et de partager, notre nature reprend le dessus. Je crois profondément que nous sommes à l’aube d’une ère nouvelle. De grands scientifiques, des philosophes, des politiques, parfois même des journalistes comparent l’avènement du web à l’imprimerie.
Malheureusement (heureusement?) Le monde change vite, trop vite. Et dans tout ça. Pour compléter cet article j’ai republié cet excellent reportage: La discussion est ouverte. share. Giant Global Graph | Decentralized Information Group (DIG) Bread. Well, it has been a long time since my last post here. So many topics, so little time. Some talks, a couple of Design Issues articles, but no blog posts. To dissipate the worry of expectation of quality, I resolve to lower the bar. More about what I had for breakfast.
So The Graph word has been creeping in. BradFitz talks of the Social Graph as does Alex Iskold, who discusses social graphs and network theory in general, points out that users want to own their own social graphs. He alo points out that examples of graphs are the Internet and the Web. Maybe it is because Net and Web have been used. The Net we normally use as short for Internet, which is the International Information Infrastructure. Simpler, more powerful. Programmers could write at a more abstract level. The word Web we normally use as short for World Wide Web. Also, it allowed unexpected re-use. So the Net and the Web may both be shaped as something mathematicians call a Graph, but they are at different levels.
Sysomos | Business Intelligence for Social Media. Sysomos offers two products, Heartbeat and Media Analysis Platform (MAP), to customers looking for leading-edge social media analytics services. Although Heartbeat and MAP are based on the same underlying technology, they provide different features to meet the needs of different users. Heartbeat is designed for day-to-day monitoring and measurement requirements, while MAP provides an in-depth research tool.
Heartbeat: Real-time monitoring of brands and products, with measurable metrics and the ability to engage with key influencers. Heartbeat Pro: Monitoring, measurement, in-depth metrics, key influencers, detailed sentiment for advanced users, and the ability to engage with key influencers. Sysomos MAP: A full-feature analytics service with unlimited access to billions of social media conversations, as well features such as automated sentiment and geo-demographics. MAP is ideal for in-depth research, historical analysis, and the preparation of value-added reports. Conversational Search — BackType. Radian6 - Social Media Monitoring, Measurement and Engagement. MoodViews — MoodViews. REST API - Evri. Table of Contents 1 Introduction 2 Registration and API Use Limits 2 URIs and Response Types 3 Versioning 4 Resources 5 Core Concepts 6 Example Scenarios 7 Code Samples 8 Feedback and Troubleshooting 9 Known Issues Introduction The Evri API gives you programmatic access to Evri’s rich mapping of the entity web, or the web of people, places and things connected to one another via language itself.
Entities located in an article or body of text Popular entities on the entity web broken down by various categories like politician , actor or film . Structured information about an entity including things like birth date , death date , universities attended , children , etc. How entities are related including the news, blog, or web language that links them on the entity web How entities or combinations of entities are related to documents, images and video found on the web Media recommendations for entities, or combinations of entities, based on a specific news, blog, or web article mention. Versioning Resources.