W3C | Semantic Web Case Studies Case studies include descriptions of systems that have been deployed within an organization, and are now being used within a production environment. Use cases include examples where an organization has built a prototype system, but it is not currently being used by business functions. The list is updated regularly, as new entries are submitted to W3C. There is also an RSS1.0 feed that you can use to keep track of new submissions. Please, consult the separate submission page if you are interested in submitting a new use case or case study to be added to this list. (), by , , Activity area:Application area of SW technologies:SW technologies used:SW technology benefits: A short overview of the use cases and case studies is available as a slide presentation in Open Document Format and in PDF formats.
Linked Data Basics for Techies - OpenOrg Intended Audience This is intended to be a crash course for a techie/programmer who needs to learn the basics ASAP. It is not intended as an introduction for managers or policy makers (I suggest looking at Tim Berners-Lee's TED talks if you want the executive summary). It's primarily aimed at people who're tasked with creating RDF and don't have time to faff around. It will also be useful to people who want to work with RDF data. Please Feedback-- especially if something doesn't make sense!!!! If you are new to RDF/Linked Data then you can help me! I put a fair bit of effort into writing this, but I am too familar with the field! If you are learning for the first time and something in this guide isn't explained very well, please drop me a line so I can improve it. firstname.lastname@example.org Warning Some things in this guide are deliberately over-simplified. Alternatives If you don't like my way of explaining things, then there's other introductions out there; (suggest more!) Structure Merging URI vs URL a
SPARQL 1.1 Protocol 4.1 Security There are at least two possible sources of denial-of-service attacks against SPARQL protocol services. First, under-constrained queries can result in very large numbers of results, which may require large expenditures of computing resources to process, assemble, or return. Another possible source are queries containing very complex — either because of resource size, the number of resources to be retrieved, or a combination of size and number — RDF Dataset descriptions, which the service may be unable to assemble without significant expenditure of resources, including bandwidth, CPU, or secondary storage. Since a SPARQL protocol service may make HTTP requests of other origin servers on behalf of its clients, it may be used as a vector of attacks against other sites or services. SPARQL protocol services may remove, insert, and change underlying data via the update operation. Different IRIs may have the same appearance.
LOD2 | Interlinked Data Linked Data: Evolving the Web into a Global Data Space Read These Seven Books, and You’ll be a Better Writer Donald Miller I used to play golf but I wasn’t very good. I rented a DVD, though, that taught me a better way to swing, and after watching it a few times and spending an hour or so practicing, I knocked ten strokes off my game. I can’t believe how much time I wasted when a simple DVD saved me years of frustration. • The War of Art by Steven Pressfield: This book is aimed at writers, but it’s also applicable to anybody who does creative work. Pressfield leaves out all the mushy romantic talk about the writing life, talk I don’t find helpful. • On Writing Well by William Zinsser: Zinsser may be the best practical writing coach out there. • Bird by Bird by Anne Lamott: Before becoming a literary superstar, Anne Lamott taught writing, and Bird by Bird is the best of her advice, broken up into chapters. Save the Cat by Blake Snyder: Snyder’s book is specifically for screenwriters, and yet I recommend the book for writers of any kind, and teachers and preachers as well.
OntoWiki — Agile Knowledge Engineering and Semantic Web CubeViz -- Exploration and Visualization of Statistical Linked Data Facilitating the Exploration and Visualization of Linked Data Supporting the Linked Data Life Cycle Using an Integrated Tool Stack Increasing the Financial Transparency of European Commission Project Funding Managing Multimodal and Multilingual Semantic Content Improving the Performance of Semantic Web Applications with SPARQL Query Caching sameAs Protege Ontology Library OWL ontologies Information on how to open OWL files from the Protege-OWL editor is available on the main Protege Web site. See the Creating and Loading Projects section of the Getting Started with Protege-OWL Web page. AIM@SHAPE Ontologies: Ontologies pertaining to digital shapes. Frame-based ontologies In the context of this page, the phrase "frame-based ontologies" loosely refers to ontologies that were developed using the Protege-Frames editor. Biological Processes: A knowledge model of biological processes and functions that is graphical, for human comprehension, and machine-interpretable, to allow reasoning. Other ontology formats Dublin Core: Representation of Dublin Core metadata in Protege.
SKOS Simple Knowledge Organization System Namespace Status of this Document This document describes the schema available from the SKOS namespace. Introduction The Simple Knowledge Organization System (SKOS) is a common data model for sharing and linking knowledge organization systems via the Semantic Web.This document provides a brief description of the SKOS Vocabulary. For detailed information about the SKOS Recommendation, please consult the SKOS Reference [SKOS-REFERENCE] or the SKOS Primer [SKOS-PRIMER]. SKOS Schema Overview The following table gives a non-normative overview of the SKOS vocabulary; it replicates a table found in the (normative) SKOS Reference [SKOS-REFERENCE]. See also the SKOS Namespace Document - RDF/XML Variant [SKOS-RDF]. References SKOS Reference, Alistair Miles, Sean Bechhofer, Editors. SKOS Namespace - RDF/XML Variant. SKOS Primer, Antoine Isaac, Ed Summers, Editors. Acknowledgements This document is the result of extensive discussions within the W3C's Semantic Web Deployment Working Group.
Linked Data Platform 1.0 5.1 Introduction This section is non-normative. Many HTTP applications and sites have organizing concepts that partition the overall space of resources into smaller containers. To which URLs can I POST to create new resources? This document defines the representation and behavior of containers that address these issues. This document includes a set of guidelines for creating new resources and adding them to the list of resources linked to a container. The following illustrates a very simple container with only three members and some information about the container (the fact that it is a container and a brief title): Example 1 # The following is the representation of # # @base < @prefix dcterms: < This example is very straightforward - there is the containment triple with subject of the container, predicate of ldp:contains and objects indicating the URIs of the contained resources. Example 2 Example 3 Example 4 Example 5
Robot-writing increased AP’s earnings stories by tenfold | Poynter. Since The Associated Press adopted automation technology to write its earnings reports, the news cooperative has generated 3,000 stories per quarter, ten times its previous output, according to a press release from Automated Insights, the company behind the automation. Those stories also contained “far fewer errors” than stories written by actual journalists. The Associated Press began publishing earnings reports using automation technology in July for companies including Hasbro Inc., Honeywell International Inc. and GE. Appended to those stories is a note that reads “This story was generated automatically by Automated Insights ( using data from Zacks Investment Research. Full GE report: The stories include descriptions of each business and contain “forward-looking guidance provided by the companies,” according to the release. Automation has been used to generate content before. Here’s the release:
Disciplinary Metadata While data curators, and increasingly researchers, know that good metadata is key for research data access and re-use, figuring out precisely what metadata to capture and how to capture it is a complex task. Fortunately, many academic disciplines have supported initiatives to formalise the metadata specifications the community deems to be required for data re-use. This page provides links to information about these disciplinary metadata standards, including profiles, tools to implement the standards, and use cases of data repositories currently implementing them. For those disciplines that have not yet settled on a metadata standard, and for those repositories that work with data across disciplines, the General Research Data section links to information about broader metadata standards that have been adapted to suit the needs of research data. Please note that a community-maintained version of this directory has been set up under the auspices of the Research Data Alliance.