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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 Wikipedia:Researching with Wikipedia - Wikipedia For a quick and simple guide to using Wikipedia in Research, see WP:Research help. Wikipedia can be a great tool for learning and researching information. However, as with all reference works, not everything in Wikipedia is accurate, comprehensive, or unbiased. Many of the general rules of thumb for conducting research apply to Wikipedia, including:

List of datasets for machine learning research - Wikipedia These datasets are used for machine learning research and have been cited in peer-reviewed academic journals and other publications. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.[2][3][4][5] This list aggregates high-quality datasets that have been shown to be of value to the machine learning research community from multiple different data repositories to provide greater coverage of the topic than is otherwise available.

Upper ontology - Wikipedia A number of upper ontologies have been proposed, each with its own proponents. Library classification systems predate upper ontology systems. Though library classifications organize and categorize knowledge using general concepts that are the same across all knowledge domains, neither system is a replacement for the other. Tutorial 4: Introducing RDFS & OWL Next: Querying Semantic Data Having introduced the advantages of modeling vocabulary and semantics in data models, let's introduce the actual technology used to attribute RDF data models with semantics. RDF data can be encoded with semantic metadata using two syntaxes: RDFS and OWL. After this tutorial, you should be able to: Understand how RDF data models are semantically encoded using RDFS and OWLUnderstand that OWL ontologies are RDF documentsUnderstand OWL classes, subclasses and individualsUnderstand OWL propertiesBuild your own basic ontology, step by stepEstimated time: 5 minutes You should have already understood the following tutorial (and pre-requisites) before you begin:

datasets The DBpedia data set uses a large multi-domain ontology which has been derived from Wikipedia as well as localized versions of DBpedia in more than 100 languages. 1 Background Wikipedia has grown into one of the central knowledge sources of mankind and is maintained by thousands of contributors. The Sweet Compendium of Ontology Building Tools Download as PDF Well, for another client and another purpose, I was goaded into screening my Sweet Tools listing of semantic Web and -related tools and to assemble stuff from every other nook and cranny I could find. The net result is this enclosed listing of some 140 or so tools — most open source — related to semantic Web ontology building in one way or another. Ever since I wrote my Intrepid Guide to Ontologies nearly three years ago (and one of the more popular articles of this site, though it is now perhaps a bit long in the tooth), I have been intrigued with how these semantic structures are built and maintained.

MooWheel: a javascript connections visualization library View the project on Google Code 06.29.2008 version 0.2 now available! get it. What's new? Looking for version 0.1 instead? The purpose of this script is to provide a unique and elegant way to visualize data using Javascript and the <canvas> object. Attention Ecology: Trend Circulation and the Virality Threshold Abstract This article demonstrates the use of data mining methodologies for the study and research of social media in the digital humanities. Drawing from recent convergences in writing, rhetoric, and DH research, this article investigates how trends operate within complex networks. Through a study of trend data mined from Twitter, this article suggests the possibility of identifying a virality threshold for Twitter trends, and the possibility that such a threshold has broader implications for attention ecology research in the digital humanities.

What is an ontology and why we need it Figure 8. Hierarchy of wine regions. The "A" icons next to class names indicate that the classes are abstract and cannot have any direct instances. The same class hierarchy would be incorrect if we omitted the word “region” from the class names. Iris is an AI to help science R&D Most startups have a pitch. The team behind Iris AI has two: right now they’ve created an AI-powered science assistant that functions like a search tool, helping researchers track down relevant journal papers without having to know the right keywords for their search. But in future their big vision is their artificially intelligent baby grows up to become a scientist in her own right — capable of forming and even testing hypotheses, based on everything it’s going to learn in its science research assistant ‘first job’ role. Such is the multi-stage, big picture promise of artificial intelligence. Yet convincing customers to buy into AI’s potential now, at what is still a pretty nascent stage in the tech’s development, remains a challenge.