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Online Access

Online Access
The DBpedia data set can be accessed online via a SPARQL query endpoint and as Linked Data. 1. Querying DBpedia The DBpedia data set enables quite astonishing query answering possibilities against Wikipedia data. 1.1. Public SPARQL Endpoint There is a public SPARQL endpoint over the DBpedia data set at OpenLink Virtuoso as the back-end database engine. There is a list of all DBpedia data sets that are currently loaded into the SPARQL endpoint. You can ask queries against DBpedia using: the Leipzig query builder at the OpenLink Interactive SPARQL Query Builder (iSPARQL) at the SNORQL query explorer at (does not work with Internet Explorer); or any other SPARQL-aware client(s). Fair Use Policy: Please read this post for information about restrictions on the public DBpedia endpoint. 1.2. There is a public Faceted Browser “search and find” user interface at 1.3. here. 1.4. 1.5. 1.6.

Solvent Solvent Why do I need screen scrapers? Piggy Bank needs web pages to embed information in a format that it can understand. This format is called RDF (Resource Description Framework) and its main advantage is that makes machine processing a lot easier. Unfortunately, at these very early stages, not many web pages embed or link to such "purer" RDF information. In short, screen scrapers allow you to turn a regular web page into a regular web page plus semantic data, and thus frees the data from the page/site that contains it. How do I use it? Watch a screencast of Solvent scraping the location of Starbucks coffee shops in Cambridge, MA and then use Piggy Bank to show the scraped data on a map. Also read the Piggy Bank screen scraping howto that uses Solvent to write a screen scraper for Piggy Bank. There is another tutorial about using Solvent to scrape web pages containing data about baseball players. What are the main features of Solvent? Where do I find other scrapers to learn from? Credits

Piggy Bank Piggy Bank Contributing Piggy Bank is an open source software and built around the spirit of open participation and collaboration. Blog about Piggy Bank Subscribe to our mailing lists to show your interest and give us feedback Report problems and ask for new features through our issue tracking system (but take a look at our todo list first) Send us patches or fixes to the code Publish Semantic Web data on your web site (how-to) for Piggy Bank’s consumption Write and submit new screen scrapers for others to use Research Publications on Piggy Bank: David Huynh, Stefano Mazzocchi, and David Karger. Related research: History Licensing & Legal Issues Piggy Bank is open source software and is licensed under the BSD license. Note, however, that this software ships with libraries that are not released under the same license; that we interpret their licensing terms to be compatible with ours and that we are redistributing them unmodified. Disclaimer Credits

SIMILE Project DBPedia and JQuery I was wondering if I can develop and app using DBPedia as well as JQuery ajax. Since most of the people out here were looking around for a simple demo application where they can utilize the power of semantic web to get some interesting data as well as some easy way to do so.After this tutorial I guess you will have a fare idea to start to build your first semantic web app. So I came up with a small project called “ Know India “. This project concentrates on showing various information from different stated of India. We will get along with this entire tutorial and build a small application that retrieves information according the state selected and gives us the external links to that state and information about the famous people out there. Configuring the auto-complete as well as fetching the abstract. Fetching the External links for the states. Fetching the famous people, their pics and an abstract about them. Prerequisites for the above tutorial are as follows :- Basic knowledge on SPARQL.

TopBraid Enterprise Vocabulary Net User and Reference Guide: User guide TopBraid Enterprise Vocabulary Net User and Reference Guide This chapter shows you how you can use EVN to perform the basic tasks associated with the editing and maintenance of multi-user vocabularies. Table of Contents 1. The EVN home screen The top of the EVN home screen lists up to three categories of vocabularies, depending on what role the logged-in user has for each vocabulary: Vocabularies that you can edit, Vocabularies that you can view, and Vocabularies that you have no access to. The menu below the listed vocabularies offers these options: Create New Vocabulary is described further in Creating a new vocabulary. The next section of the EVN home screen lists up to three categories of ontologies, depending on what role the logged-in user has for each: Ontologies that you manage, Ontologies that you can view, and Ontologies that you have no access to. For the purposes of EVN, vocabularies are defined as models that are based on SKOS. 2. 2.2 Browsing information about concepts

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