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RDF

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https://docs.google.com/presentation/d/1Q7KOu3uD5LOMIhmAd2KU-b76gcIAWyXGwbTt_UzwBu4/edit?usp=sharing

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HTML�

  • Hyper Text Markup Language = Lenguaje de marcación de Hipertexto
  • Lenguaje de marcas de texto utilizado normalmente en la WWW (World Wide Web). El término HTML se suele referir tanto al tipo de documento estructurado como al lenguaje de marcas que representa esos documentos.

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http://www.monografias.com/trabajos7/html/html.shtm

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RDF Literatura biológica

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  1. Web semántica y linked data
  2. Entender RDF
  3. Aplicaciones de RDF para literatura científica
  4. Buscadores rdf literatura biología
  5. Ejemplos RDF literatura biología

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W3C

https://www.w3.org/wiki/Main_Page

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RDF

  1. Sujeto (IRI, nodo blanco)
  2. Predicado (IRI)
  3. Objeto (IRI, Literal, nodo blanco)

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Ejemplo tripletes informal

<https://zookeys.pensoft.net/article/6232/list/18/> <is a> <paper>.�<https://zookeys.pensoft.net/article/6232/list/18/> <was created by> <R. Page>.�<https://zookeys.pensoft.net/article/6232/list/18/> <has a doi> <10.3897/zookeys.550.9293>.

<https://zookeys.pensoft.net/article/6232/list/18/> <has a alternative url> <https://doi.org/10.3897/zookeys.550.9293>.

<https://zookeys.pensoft.net/article/6232/list/18/> <has a publication date> <the 07 january 2016>.�<https://zookeys.pensoft.net/article/6232/list/18/> <has a title> <Surfacing the deep data of taxonomy>.

<R. Page> <is a> <author>.

"Roderic D. M. Page", "homepage": "http://iphylo.blogspot.com"

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Ejemplo grafo

https://zookeys.pensoft.net/article/6232/list/18/

Paper

https://doi.org/10.3897/zookeys.550.9293

R. Page

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RDF

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Resource Description Framework = Marco de Descripción de Recursos, lenguaje para especificar metadatos

RDF puede utilizarse en diferentes áreas como en la recuperación de recursos para los buscadores, robots y agentes inteligentes, catalogación para describir el contenido y otros.

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http://www.hipertexto.info/documentos/rdf.htm

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RDF

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SPARQL�

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SPARQL (pronunciado "sparkle") es un lenguaje de recuperación basado en RDF; su nombre es un acrónimo recursivo del inglés SPARQL Protocol and RDF Query Language (Protocolo y Lenguaje de búaqueda RDF). Se trata de una recomendación para crear un lenguaje de consulta dentro de la Web semántica que está ya implementada en muchos lenguajes y bases de datos.

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http://serqlsparql.50webs.com/sparql.html

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http://lod-cloud.net/

http://lov.okfn.org/dataset/lov/

http://lov.okfn.org/dataset/lov/

https://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData

https://www.w3.org/TR/hcls-senselab/

https://data.globalchange.gov/instrument/solar-stellar-irradiance-comparison-experiment.thtml

https://www.w3.org/2001/sw/rdb2rdf/use-cases/

https://www.researchgate.net/post/RDF_Triples_extraction_from_text

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Aplicaciones

https://www.w3.org/RDF/Validator/rdfval?

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RDF Triples extraction from text ?

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There are several services that can analyze text to and get RDF from content for concepts, entities, keywords, categories, sentiment, emotion, relations, and semantic roles, based on natural language understanding. Some of those services are free, and others are commercial, but you can use it for free for a small amount of calls.

  1. DBpedia Spotlight, this is an annotation service based on DBpedia.http://demo.dbpedia-spotlight.org/
  2. Open Calais from Thomson Reuters. http://www.opencalais.com/. There is a demo site so you can test it. http://www.opencalais.com/opencalais-demo/
  3. Natural Language Understanding service from IBM Bluemix Watson services. This service is the continuation of Alchemy API and is one of the best services. https://www.ibm.com/watson/developercloud/natural-language-understanding.html
  4. FRED: Machine Reading for the Semantic Web, it is a service that can to parse natural language text in 48 different languages and transform it into linked data. http://wit.istc.cnr.it/stlab-tools/fred. And you can read the paper about FRED here: http://semantic-web-journal.org/system/files/swj1379.pdf
  5. You can also look at this project presented in the paper "Extracting knowledge from text using SHELDON, a Semantic Holistic framEwork for LinkeD ONtology data". http://speroni.web.cs.unibo.it/publications/reforgiato-recupero-2015-extracting-knowledge-from.pdf

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RDF Triples extraction from text ?. Available from: https://www.researchgate.net/post/RDF_Triples_extraction_from_text [accessed Jul 17, 2017].

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Elements

Subject

Property

Object

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Cosas (entidad, objeto, sujeto)

Propiedades (predicados)

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RDF literature

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http://agris.fao.org/aos/records/US7929347?output=xml

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JSON

https://api.crossref.org/v1/works/http://dx.doi.org/10.3897/zookeys.550.9293

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RDF

  1. JSON
  2. JSONLD
  3. Turtle
  4. NT
  5. N3/Turtle
  6. RDF/XML
  7. XML
  8. N-Triples

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The First Step: from Terms to Concept

The Second Step: from Concepts to Statemen

subject > predicate > object

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The Third Step: Annotation of Statements with Context and Provenan

conditional

symetrical

true

relevant

The Fourth Step: Treating Richly Annotated Statements as Nano Publications

category of triplets:

Curated Statements (Essentially Annotations).

Observational Statements (Coexpression, Cooccurrence, Statistic

Hypothetical Statements (Inferred by Established and Published Algori

The Fifth Step: Removing Redundancy, Metaanalyzing WebStatements (Raw Triples to Refined Triples

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