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Linked Data : Current Status

Linked Data : Current Status
What is Linked Data? The Semantic Web is a Web of Data — of dates and titles and part numbers and chemical properties and any other data one might conceive of. The collection of Semantic Web technologies (RDF, OWL, SKOS, SPARQL, etc.) provides an environment where application can query that data, draw inferences using vocabularies, etc. However, to make the Web of Data a reality, it is important to have the huge amount of data on the Web available in a standard format, reachable and manageable by Semantic Web tools. Furthermore, not only does the Semantic Web need access to data, but relationships among data should be made available, too, to create a Web of Data (as opposed to a sheer collection of datasets). This collection of interrelated datasets on the Web can also be referred to as Linked Data. What is Linked Data Used For? Linked Data lies at the heart of what Semantic Web is all about: large scale integration of, and reasoning on, data on the Web. Examples Learn More

http://www.w3.org/standards/semanticweb/data

Related:  Semantic Webdodhiambo404teaching: Linked Data

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: 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.

Term-based thesauri and SKOS (Part 1) I'm currently doing a piece of work on representing a thesaurus as linked data. I'm working on the basis that the output will make use of the SKOS model/RDF vocabulary. Some of the questions I'm pondering are probably SKOS Frequently Asked Questions, but I thought it was worth working through my proposed solution and some of the questions I'm pondering here, partly just to document my own thought processes and partly in the hope that SKOS implementers with more experience than me might provide some feedback or pointers. SKOS adopts a "concept-based" approach (i.e. the primary focus is on the description of "concepts" and the relationships between them); the source thesaurus uses a "term-based" approach based on the ISO 2788 standard. I found the following sources provided helpful summaries of the differences between these two approaches:

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 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.

Download SPARQL results into spreadsheet Download SPARQL results into spreadsheet SPARQL endpoint: SPARQL query: PREFIX owl: < PREFIX xsd: < PREFIX rdfs: < PREFIX rdf: < PREFIX foaf: < PREFIX dc: < PREFIX : < PREFIX dbpedia2: < PREFIX dbpedia: < PREFIX skos: < 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. Robot-writing increased AP’s earnings stories by tenfold 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:

Feeds Maybe you have just a wrong url. Go to first to see if the error persists. If you get the error again check that you: Don't use anonymizers, open proxies, VPNs, or TOR to access Project Gutenberg. This includes the Google proxies that are used by Chrome. 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

Hedge fund robots are crushing their human rivals Synopsis The hedge fund robots are winning again. In 2014, computer algorithm-led investing produced stellar returns, beating most human managers and recovering most of their losses from 2011, 2012 and 2013. Working with SPARQL in MarcEdit – Terry's Worklog Over the past couple of weeks, I’ve been working on expanding the linking services that MarcEdit can work with in order to create identifiers for controlled terms and headings. One of the services that I’ve been experimenting with is NLM’s beta SPARQL endpoint for MESH headings. MESH has always been something that is a bit foreign to me. While I had been a cataloger in my past, my primary area of expertise was with geographic materials (analog and digital), as well as traditional monographic data. While MESH looks like LCSH, it’s quite different as well.

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.

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