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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 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: Tutorial 3: Semantic Modeling In the last lesson, we compared some of the more popular traditional forms of modeling data with the semantic model, and then introduced a situation where data sharing was enhanced and made significantly easier by using a semantic web approach. 4.1 A Starting Example 01. 08. 09. 10. 11.

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. This script requires three libraries to support it. MooWheel only requires 2 arguments to create a basic wheel graph. new MooWheel(dataArray, canvasElement); The canvas element can be passed as either an element reference or an id string. Note: In this context, the first argument (the data parameter), doesn't matter. Each item that has an "imageUrl" will have that image preloaded, and then added to the wheel when it is drawnIn terms of options, there are a number of options available that allow you to change the way the graph is generated and displayed: Using MooWheel is very simple and extremely easy. Next, you create an array of items for the connections: Then you add a container for the canvas tag to the body of your document:

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. Only classes can be arranged in a hierarchy—knowledge-representation systems do not have a notion of sub-instance. As a final note on defining a class hierarchy, the following set of rules is always helpful in deciding when an ontology definition is complete: The ontology should not contain all the possible information about the domain: you do not need to specialize (or generalize) more than you need for your application (at most one extra level each way). For our wine and food example, we do not need to know what paper is used for the labels or how to cook shrimp dishes. Similarly, the ontology should not contain all the possible properties of and distinctions among classes in the hierarchy. A value of a slot may depend on a value of another slot.

SKOS Simple Knowledge Organization System - home page SKOS is an area of work developing specifications and standards to support the use of knowledge organization systems (KOS) such as thesauri, classification schemes, subject heading lists and taxonomies within the framework of the Semantic Web ... [read more] Alignment between SKOS and new ISO 25964 thesaurus standard (2012-12-13) ISO 25964-1, published in 2011, replaced the previous thesaurus standards ISO 2788 and ISO 5964 (both now withdrawn). Members of the Working Group responsible for ISO 25964 have gone on to consider the implications for SKOS users. From Chaos, Order: SKOS Recommendation Helps Organize Knowledge (2009-08-18) Today W3C announces a new standard that builds a bridge between the world of knowledge organization systems - including thesauri, classifications, subject headings, taxonomies, and folksonomies - and the linked data community, bringing benefits to both. Call for Review: SKOS Reference Proposed Recommendation (2009-06-15)

How to organize your EndNote library By Sarah Tanksalvala EndNote includes a number of functions to help organize your sources and find exactly what you want when you want it. Properly organizing a large number of sources can save you hours of work and frustration. Here are five ways to organize your EndNote libraries, along with tips to use them more efficiently: 1) Smart Groups: Smart groups let you automatically sort your sources by select criteria. Expert tip: When auto-importing files, use smart groups to sort your sources before you ever read them. 2) Group Sets: Whether you’ve manually created your groups or created a smart group, you can combine groups either by creating a new group from smaller ones, or by creating a group set. Expert tip: Create very specific groups and then combine them under broader group sets so you can target precise references when you’re looking for something, or general groups for browsing. 4) Tagging and comments: Add comments and tags to any source and then use those for searches later.

Knowledge management Process of creating, sharing, using and managing the knowledge and information of an organization Knowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization.[1] It refers to a multidisciplinary approach to achieve organizational objectives by making the best use of knowledge.[2] An established discipline since 1991,[3] KM includes courses taught in the fields of business administration, information systems, management, library, and information science.[3][4] Other fields may contribute to KM research, including information and media, computer science, public health and public policy.[5] Several universities offer dedicated master's degrees in knowledge management. History[edit] In 1999, the term personal knowledge management was introduced; it refers to the management of knowledge at the individual level.[12] Research[edit] Dimensions[edit] Strategies[edit] Motivations[edit] KM technologies[edit]

Knowledge base A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems which were the first knowledge-based systems. The original use of the term knowledge-base was to describe one of the two sub-systems of a knowledge-based system. A knowledge-based system consists of a knowledge-base that represents facts about the world and an inference engine that can reason about those facts and use rules and other forms of logic to deduce new facts or highlight inconsistencies.[1] The term 'knowledge-base' was to distinguish from the more common widely used term database. At the time (the 1970s) virtually all large Management Information Systems stored their data in some type of hierarchical or relational database. Flat data. Early expert systems also had little need for multiple users or the complexity that comes with requiring transactional properties on data. See also[edit]

Tutorial 1: Introducing Graph Data Next: Introducing RDF The semantic web can seem unfamiliar and daunting territory at first. If you're eager to understand what the semantic web is and how it works, you must first understand how it stores data. We start from the ground up by outlining the graph database - the data storage model used by the semantic web. After this tutorial, you should be able to: Describe in basic terms what the semantic web is.Experience the paradigm-shift of storing information as a graph database, rather than a hierarchical or relational database.Understand that the semantic web of data is defined using Resource Description Framework (RDF).Understand the basic principles of RDF statements and how they can define data graphs. Estimated time: 5 minutes If you come from a traditional IT background and are used to the idea of storing data either in a hierarchy (for example XML) or in a relational database (for example MySQL, MS SQL), you may not yet have come across Resource Description Framework, or RDF. 03.

Spreadsheets Are Graphs Too! - Neo4j Graph Database By Felienne Hermans, Assistant Professor, Delft University of Technology | August 26, 2015 Editor’s Note: Last May at GraphConnect Europe, Felienne Hermans – Assistant Professor at Delft University of Technology – gave this engaging talk on why you shouldn’t overlook the power of the humble spreadsheet. Listen to or read her presentation below. Register for GraphConnect San Francisco to hear more speakers like Felienne present on the emerging world of graph database technologies. People often ask me, ‘How is it possible that you research spreadsheets? The answer is, Yes, I did. Ninety-five percent of all U.S. companies still use spreadsheets for financial reporting, so spreadsheets run the financial domain. Analysts decide the strategy of their company based on spreadsheets. Either way, analysts make decisions that steer the company based on the data in their spreadsheets. What You Don’t Know about Your Spreadsheets Is the Most Horrifying Part Spreadsheets often exist under the radar.

What is Pattern Analysis? PATN is a software package that performs Pattern Analysis. PATN aims to try and display patterns in complex data. Complex in PATN's terms, means that you have at least 6 objects that you want to know something about and a suite of more than 4 variables that describe those objects. Data must be in the form of a spreadsheet of rows (the objects in PATN) and the columns (variables), as in Microsoft Excel™. There are usually around 7 components to a 'realistic' (read as adequate, comprehensive, fair, reasonable or intelligent) pattern analysis in PATN- Import the data Check the data using PATN's Visible Statistics functions. PATN is setup to make it easy for you to follow this process.