W3C | 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. 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.
In some cases such expenditures may effectively constitute a denial-of-service attack. A SPARQL protocol service may place restrictions on the resources that it retrieves or on the rate at which external resources are retrieved. SPARQL protocol services may remove, insert, and change underlying data via the update operation. Different IRIs may have the same appearance. 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].
This document can be referenced directly, by its own URI or indirectly, by content negotiation from the SKOS namespace URI as described in Appendix C of SKOS Reference [SKOS-REFERENCE]. See also the SKOS Namespace Document - RDF/XML Variant [SKOS-RDF]. References Acknowledgements. 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. Schema.org | Vocabulary. LIDER | Guidelines, Reference Cards. Linked Open Vocabularies. InPhO - Indiana Philosophy Ontology Project. Manchester OWL | Research. 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. Other ways to search for OWL ontologies include using Google: or the new Semantic Web search engine called Swoogle. 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. LOD2 | Interlinked Data. 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.
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. Cambridge Semantics. DCMI Home: Dublin Core® Metadata Initiative (DCMI) ANDS - Metadata Stores Solutions. Overview ANDS supports the development of institution-wide solutions for the discovery and reuse of research data collections. Having funded the development of a number of software solutions for creating rich metadata records about collections of data, the next step is to ensure that this metadata is properly managed so that it can be harvested and exposed to search engines as well as to researchers and research administrators.
Metadata Stores are a key component of this infrastructure. ANDS has supported metadata stores for data collections, with: connectors to institutional sources of truth,coverage over the entire institution, andfeeds provided to Research Data Australia. This guide is intended to provide an overview of the solutions that are being deployed at an institutional level. Types of metadata stores ANDS distinguishes between metadata stores by their coverage, the granularity of data that they describe, and the specialisation of their descriptions.
Solution Integration. WINGS | Workflow INstance Generation and Selection. Protégé. DIG Demos and Tools. Swoogle | Ontology Search. SameAs. Dandelion API.