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CODE

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Commercially empowered Linked Open Data Ecosystems in Research

Linked Open Data (LOD) shows enormous potential in becoming the next big evolutionary step of the WWW. However, this potential remains largely untapped due to missing usage and monetisation strategies.CODE's vision is to establish... ver más

30/04/2014

KNOW

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

KNOW CENTER RESEARCH GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 3 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2014-04-30 No tenemos la información de la convocatoria

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3 Participantes

KNOW

0.00€ | Lider

UNI PASSAU

N.D. | Participante

MeisterLabs

420.56K€ | Participante

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Líder del proyecto

KNOW CENTER RESEARCH GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 3M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Linked Open Data (LOD) shows enormous potential in becoming the next big evolutionary step of the WWW. However, this potential remains largely untapped due to missing usage and monetisation strategies.CODE's vision is to establish the foundation for a web-based, commercially oriented ecosystem for Linked Open Data. This ecosystem establishes a sustainable and commercial value-creation-chain among traditional (e.g. data provider and consumer) and non-traditional (e.g. data analyst) roles in data marketplaces. Monetary incentives will motivate people to analyse, organise and integrate LOD with unstructured information sources thereby increasing data quality and quantity.Our use-case focuses on research papers as a source for mining facts and their integration into LOD repositories and light-weight ontologies. Hence, we will leverage the wealth of knowledge contained in research publications on a semantic, machine-readable level by creating the Linked Science Data cloud. This cloud will have an impact on innovation driven businesses by making scientific knowledge more accessible and transparent.Therefore, we will research and develop:1.crowd-sourcing enabled semantic enrichment & integration techniques for integrating facts contained in unstructured information into the LOD cloud2.federated, provenance-enabled querying methods for fact discovery in LOD repositories3.web-based visual analysis interfaces to support human based analysis, integration and organisation of facts4.socio-economic factors - roles, revenue-models and value chains - realisable in the envisioned ecosystem.The consortium brings together high-potential partners, providing the necessary resources for realizing our vision: tera-bytes of research publications, millions of light-weight ontologies, a steadily growing user community in the millions and expertise in unstructured information analysis, distributed databases, semantic technologies, visual analytics and economic success factors in web businesses.

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