Microformat A microformat (sometimes abbreviated μF) is a web-based approach to semantic markup which seeks to re-use existing HTML/XHTML tags to convey metadata[1] and other attributes in web pages and other contexts that support (X)HTML such as RSS. This approach allows software to process information intended for end-users (such as contact information, geographic coordinates, calendar events, and similar information) automatically. Although the content of web pages is technically already capable of "automated processing", and has been since the inception of the web, such processing is difficult because the traditional markup tags used to display information on the web do not describe what the information means.[2] Microformats can bridge this gap by attaching semantics, and thereby obviate other, more complicated, methods of automated processing, such as natural language processing or screen scraping. Background[edit] Neither CommerceNet nor Microformats.org operates as a standards body. class rel
schema.org Most webmasters are familiar with HTML tags on their pages. Usually, HTML tags tell the browser how to display the information included in the tag. For example, <h1>Avatar</h1> tells the browser to display the text string "Avatar" in a heading 1 format. However, the HTML tag doesn't give any information about what that text string means—"Avatar" could refer to the hugely successful 3D movie, or it could refer to a type of profile picture—and this can make it more difficult for search engines to intelligently display relevant content to a user. Schema.org provides a collection of shared vocabularies webmasters can use to mark up their pages in ways that can be understood by the major search engines: Google, Microsoft, Yandex and Yahoo! 1. 1a. Your web pages have an underlying meaning that people understand when they read the web pages. 1b. itemscope and itemtype Let's start with a concrete example. To begin, identify the section of the page that is "about" the movie Avatar. Back to top 1d.
How Schema.org Will Change Your Search Results & What it Means for Marketers Jeff Ente is the director of Who's Blogging What, a weekly e-newsletter that tracks over 1,100 social media, web marketing and user experience blogs to keep readers informed about key developments in their field and highlight useful but hard to find posts. Mashable readers can subscribe for free here. Algorithms aren’t going away anytime soon now that websites have a better way to directly describe their content to major search engines. Schema.org attempts to close a loophole in the information transfer from website data to presentation as search results. Simply put, Schema.org hopes to create a uniform method of putting the structure back into the HTML where the spiders can read it. How Schema.org Works Schema.org was born out of conflict between competing standards. Until this month. Microdata, true to its name, embeds itself deeply into the HTML. Abraham Lincoln was born on Feb. 12, 1809. He became known as Honest Abe and later served as President of the United States.
Gellish Gellish is a formal language that is natural language independent, although its concepts have 'names' and definitions in various natural languages. Any natural language variant, such as Gellish Formal English is a controlled natural language. Information and knowledge can be expressed in such a way that it is computer-interpretable, as well as system-independent and natural language independent. Etymologically speaking, "Gellish" is originally derived from "Generic Engineering Language." Overview[edit] Gellish is intended for the expression of facts (statements), queries, answers, etc. In principle, for every natural language there is a Gellish variant that is specific for that language. A full Gellish Message Table requires additional columns for unique identifiers, the intention of the expression, the language of the expression, cardinalities, unit of measure, the validity context, status, creation date, author, references, and various other columns. Models about individual things.
Exhibit 3.0 Project Getting Involved Join us on IRC on freenode or browse the SIMILE Widgets mailing list archives to ask questions about Exhibit. Chances are others may have similar questions, and the list is a great place to share answers. Background The Exhibit 3 project was supported by the Library of Congress. Exhibit 3.0 development work proceeds from the proposed architecture (PDF) released in early 2011. Demos Exhibit 3.0: What's New? Exhibit 3.0 is available for community input. Exhibit 3.0 Scripted (client-side) Exhibit 3.0 Staged Beta 2(client/server): Scalability: Browse hundreds of thousands of records Persistence: Pick up where you left off browsing an Exhibit Export data in HTML + RDFa format Community Involvement Developers in the community are encouraged to contribute code. We also encourage all members of the Exhibit community to share their ideas, suggestions, documentation, examples, and contributions in any other way you can think of!
New York Times - Linked Open Data RDF-Gravity Sunil Goyal, Rupert Westenthaler {sgoyal, rwestenthaler}@salzburgresearch.at Salzburg Research, Austria RDF Gravity is a tool for visualising RDF/OWL Graphs/ ontologies. Its main features are: Graph VisualizationGlobal and Local Filters (enabling specific views on a graph) Full text SearchGenerating views from RDQL QueriesVisualising multiple RDF files RDF Gravity is implemented by using the JUNG Graph API and Jena semantic web toolkit. Figure 1: Screenshot of RDF-Gravity, showing a part of Wine Ontology 1 Graph Visualisation RDF Gravity defines a visualization package on top of the JUNG Graph API. Configurable renderers for edges and nodes of a graph, including different node shapes and edge decorations etc.A Renderer Factory allowing the configuration of the above node and edge renderers based on the type of an edge or node. For graph layout, it uses the layout algorithms directly supported by the Jung API. 2 Global & Local Filters 3 Full Text Search 4 Visualising Multiple RDF Files
NetSeer Pushes Concepts, Not Keywords, for Contextual Targeting Written on Aug 11, 2011 Author Gavin Dunaway | Share ♻ Retweet ADOTAS – At an abstract level, graph theory is about representing the relationship between nodes (interconnected objects) and edges (the lines that connect them). Considering its Herculean task, it doesn’t seem surprising that Content Mapper, the technology base of NetSeer’s concept-based contextual advertising, grew out of technology developed by UCLA scientists for studying the complexity of the human genome. “Content Mapper is complicated but intuitive,” says the NetSeer CEO. A computer science major from MIT, Mracek been around just about every tech block, including a stint with Apple during the tumultuous 90s (i.e., before the return of Steve Jobs). He thinks about Content Mapper this way: When a person views a web page, the brain uses all his/her collected knowledge and experience to analyze what’s on that page. The system recognize “off-page” concepts and weighs the value of conceptual connections — direct, indirect, etc.
RDF Vocabulary Description Language 1.0: RDF Schema Abstract RDF Schema provides a data-modelling vocabulary for RDF data. RDF Schema is an extension of the basic RDF vocabulary. Status of This Document This section describes the status of this document at the time of its publication. This document is an edited version of the 2004 RDF Schema Recommendation. This document was published by the RDF Working Group as a Recommendation. This document has been reviewed by W3C Members, by software developers, and by other W3C groups and interested parties, and is endorsed by the Director as a W3C Recommendation. This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. Table of Contents 1. RDF Schema provides a data-modelling vocabulary for RDF data. This document is intended to provide a clear specification of RDF Schema to those who find the formal semantics specification [RDF11-MT] daunting. RDF Schema is a semantic extension of RDF. 2. 2.1 rdfs:Resource 2.2 rdfs:Class 3. Note
Why the Semantic Web Will Fail Don't get too excited by the title. But I do want to share a few thoughts... It was running through my head just now, the work that we were doing here in Moncton to work on an e-learning cluster. Because I saw that 'cluster building' is still one of the major pillars of NRC's strategy, and I was wondering whether our work would ever be a part of that again. And I was thinking about some of the things that didn't go so well in our first few years. Some companies went under - a couple, before we even talked to them, another, after we were in a project with them. And they weren't interested. And I thought about where we're right today and where we might be wrong, and why. And I'm saying the semantic web won't work. But how do you explain that intuition? And I was thinking about the edgy things of Web 2.0, and where they're working, and more importantly, where they're beginning to show some cracks. A few of key things today: - the rumour that Google is turning off the search API Not a chance.
Microformats vs. RDF: How Microformats Relate to the Semantic Web Update: Joe from the Squio blog has posted a response to this entry. Microformats are a wildly popular set of formats for embedding metadata within normal XHTML. The primary advantage Microformats offer over RDF (including its embedded serializations) is that you can embed metadata directly in the XHTML, reducing the amount of markup you need to write (e.g. you don't have to write XHTML and additional RDF). Many people have contended that Microformats are a possible replacement for RDF, however Microformats were not designed to cover the same scope as RDF was. While both Microformats and RDF make it possible to store data about data, they simply do not work to solve the same set of problems. A quick comparison I don't blame the Microformats people for this confusion over what Microformats are or are not. What Microformats were not intended to be: There you have it, clearly stated and all. What RDF allows (and Microformats lacks): Persisting the data within Microformats Conclusions
XML Introduction - What is XML? ConceptNet | Common Sense Computing Initiative ConceptNet aims to give computers access to common-sense knowledge , the kind of information that ordinary people know but usually leave unstated. The data in ConceptNet is being collected from ordinary people who contributed it on sites like Open Mind Common Sense . ConceptNet represents this data in the form of a semantic network, and makes it available to be used in natural language processing and intelligent user interfaces. ConceptNet is an open source project, with a Python implementation and a REST API that anyone can use to add computational common sense to their own project. Some of the nodes and links in ConceptNet. Places to go next ConceptNet Development Team Current developers Project alumni Papers Papers about ConceptNet itself Havasi, C., Speer, R. & Alonso, J. (2007) ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. Liu, H. & Singh, P. (2004).