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The largest multilingual encyclopedic dictionary and semantic network

The largest multilingual encyclopedic dictionary and semantic network
Related:  Languages

Terminology databases - Terminology Coordination Unit [DGTRAD] - European Parliament Termsciences: is a terminology portal developed by INIST in association with LORIA and ATILF. Its aim is to promote, pool and share the terminological resources (specialist vocabularies, dictionaries, thesaurus) of public sector research and further education establishment to thus create a common terminological reference resource. Glossaries from EU institutions and bodies : a compilation of nearly 300 glossaries on various topics of EU legislation such as agriculture, taxation, migration or technology, containing relevant EU jargon, many of them in all 24 official EU languages. Tilde Terminology: terminology extraction and lookup in the cloud – about 5 million standardised and reliable terms; cloud facilities for terminology management and sharing; integrated terminology recognition and lookup in Translation Environment Tools: SDL Trados Studio, Wordfast Anywhere, OmegaT, memoQ. IATE (InterActive Terminology for Europe): the European Union’s terminology database. ESCWA Glossary Humanterm

Extending Google Refine for VIVO - VIVO Dr. Curtis L. Cole, Dan Dickinson, Kenneth Lee, Eliza Chan Weill Cornell Medical College Google Refine (previously Freebase Gridworks) is a freely available open source software package for manipulating datasets. One of Google Refine’s unique features is tight integration with the Freebase database. Google Refine’s integration with Freebase allows users to “reconcile” data in a grid format against entities found within the Freebase graph. Google Refine also allows a dataset to be aligned to the Freebase schema, to convert grid data into graph data, and then export it into a triple format importable by Freebase. Weill Cornell Medical College proposes to enhance both Google Refine and VIVO to allow integration between the two systems, similar to the existing integration with Freebase. Section I: VIVO servlet - Reconciliation service The VIVO reconciliation service is a Java HttpServlet that parses requests from Google Refine and returns query results back to Google Refine. Summary 1. 1.

YAGO - D5: Databases and Information Systems (Max-Planck-Institut für Informatik) Overview YAGO is a huge semantic knowledge base, derived from Wikipedia WordNet and GeoNames. Currently, YAGO has knowledge of more than 10 million entities (like persons, organizations, cities, etc.) and contains more than 120 million facts about these entities. YAGO is special in several ways: The accuracy of YAGO has been manually evaluated, proving a confirmed accuracy of 95%. YAGO is developed jointly with the DBWeb group at Télécom ParisTech University. Localingual: listen to the voices of the world Symplectic/vivo Agile Knowledge Engineering and Semantic Web (AKSW) — Agile Knowledge Management and Semantic Web (AKSW) AKSW member will participate in ECAI 2014, Prague, Czech Republic Hello! The 21st European Conference on Artificial Intelligence (ECAI) will be held in the city of Prague, Czech Republic from 18th to 22nd August 2014. Various excellent papers on artificial intellegence, logic, rule mining and many more topics will be presented. Read more about "AKSW member will participate in ECAI 2014, Prague, Czech Republic" Additional contributions to SEMANTiCS 2014 Hello again! Five AKSW Papers at SEMANTiCS 2014 Hello Community! AKSW Colloquium “Knowledge Extraction and Presentation” on Monday, July 28, 3.00 p.m. in Room P702 Knowledge Extraction and Presentation On Monday, July 28, in room P702 at 3.00 p.m., Edgard Marx proposes a question answering system. [CfP] Semantic Web Journal: Special Issue on Question Answering over Linked Data

Список книг которые есть в группе (в разработке) | English Books Daily Search sign up Phone or email Password Don't remember me Forgot your password? English Books Daily Discussion board Discussion107 Список книг которые есть в группе (в разработке) English Books Daily May 28, 2013 at 5:09 am ГРАММАТИКА:---------------------------------------------------------------------1. Like 26Show likes English Books Daily May 28, 2013 at 5:21 am СЛОВАРНЫЙ ЗАПАС:--------------------------------------------------------------------1. Like 19Show likes English Books Daily May 28, 2013 at 5:27 am IELTS------------------------------------------------------------------1. Like 34Show likes English Books Daily May 28, 2013 at 5:38 am ENGLISH FOR SPECIFIC PURPOSES---------------------------------------------------------------1. Like 9Show likes English Books Daily May 28, 2013 at 5:53 am SPEAKING-----------------------------------------------------------------------------1. Like 12Show likes English Books Daily May 28, 2013 at 5:55 am Like 7Show likes Like 17Show likes Thank you! Hi! Hi! Hi!

A Direct Mapping of Relational Data to RDF 1 Introduction Relational databases proliferate both because of their efficiency and their precise definitions, allowing for tools like SQL [SQLFN] to manipulate and examine the contents predictably and efficiently. Resource Description Framework (RDF) [RDF-concepts] is a data format based on a web-scalable architecture for identification and interpretation of terms. This document defines a mapping from relational representation to an RDF representation. Strategies for mapping relational data to RDF abound. The direct mapping defines a simple transformation, providing a basis for defining and comparing more intricate transformations. The Direct Mapping is intended to provide a default behavior for R2RML: RDB to RDF Mapping Language [R2RML]. 2 Direct Mapping Description (Informative) The direct mapping defines an RDF Graph [RDF-concepts] representation of the data in a relational database. 2.1 Direct Mapping Example HTML tables will be used in this document to convey SQL tables.

Love in Translation I moved to Geneva to be with my husband, Olivier, who had moved there because his job required him to. My restaurant French was just passable. Drugstore French was a stretch. IKEA French was pretty much out of the question, meaning that, since Olivier, a native speaker, worked twice as many hours a week as Swiss stores were open, we went for months without things like lamps. We had established our life together in London, where we met on more or less neutral ground: his continent, my language. He had learned the language over the course of many years. “What is the English for ‘female athlete’?” “ ‘Bitch,’ ” the driver said. They drove on toward Ulster County, Olivier straining for a glimpse of the Manhattan skyline. Five years later, Olivier found himself in England, a graduate student in mathematics. After England, he moved to California to pursue a Ph.D., still barely able to cobble together a sentence. We moved in together quickly. “Huh?” “Their capillarity isn’t very good.” “Alors!”

VIVO Data - what and from where - VIVO Introduction You've looked at VIVO, you've seen VIVO in action at other universities or organizations, you've downloaded and installed the code. What next? The answer may be different everywhere – it depends on a number of factors. How big is your organization? Next – what is different about data in VIVO? As we've described, it's well worth learning the VIVO editing environment and creating sample data even if you know you will require an automated approach to data ingest and update. VIVO makes certain assumptions about data based largely on the types of data, relationships, and attributes described in the VIVO ontology. In VIVO, data about people, organizations, events, courses, places, dates, grants, and everything else are stored in one very simple, three-part structure – the RDF statement. This is not the place to explain everything about RDF – there are many good tutorials available and other sections of this wiki explain the VIVO ontology and the more technical aspects of RDF.

About World Languages Reconciling DERI researchers using Sindice | GRefine RDF Extension In this example, I will reconcile a list of people working at the Digital Enterprise Research Institute DERI with the help of Sindice. The CSV file can be downloaded. Create a Google Refine project from the CSV file. Alternatively, Sindice domain-specific services can be directly added through the RDF menu as shown below. For more details and technical documentation see Reconciliation using Sindice