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Generation and Transformation of Virtualized Assets (GeToVA)

GeToVa Architecture

SE description

As today manufacturing ecosystems deal with increasing quantities of unstructured and semi-structured information in webpages, e-mails, text documents, spreadsheets, news articles, collaborative posts, patents to name but a few, there is a real need to extract this information, to represent it in a meaningful, structured way, to cluster and transform it in multiple formats in order to support interoperability. The FITMAN Specific Enabler for Generation and Transformation of Virtualized Assets is aiming at providing a state-of-the-art Information Extraction-driven semantic tool for (semi-)automatic Virtualized intangible Assets in order to heavily reduce manual data entry for the population of the FITMAN-CAM Specific Enabler. The GeToVA Specific Enabler is provided as a set of RESTFul services being implemented on top of the FITMAN baseline VF Platform. The GeToVA services APIs have been designed as fully compatible with FITMAN Platform components, namely the Data.SemanticsSupport for the GeToVA multi-formation ontology transformation and the Apps.Repository for registration of the assets generated by GeToVA.

Extraction of Virtualized Assets information from real-world semi-structured enterprise and network resource

The Knowledge Extractor component is using state-of-the-art IE-driven semantic tool for (semi-)automatic VAaaS in order to heavily reduce manual data entry for the population of the FITMAN-CAM SE. The component is using the GATE (http://gate.ac.uk/) for Information Extraction

Clustering of Virtualized intangible Assets enabling better search of such assets

The FITMAN-GeToVA Specific Enabler supports the clustering of Virtualized intangible Assets. GeToVA uses Text/Document clustering techniques. Apache Mahout is used for this purpose. Apache Mahout is a project of the Apache Software Foundation aiming to build a distributed, scalable machine-learning framework focused primarily in the areas of collaborative filtering, clustering and classification

Generation of semantic representation of Virtualized intangible Assets according to ontological models

The Knowledge Extractor is supported by domain ontologies i.e. curriculum vitae and company profile ontologies (see Europass Format Handler component) in order to identify the ontological concepts and relations that semantically describe the text content.

Multi-format ontology transformation between various formats, mapping and exchanging Future Internet (FI) data e.g. USDL

GeToVa allows transformation using a set SPARQL Constructs. SPARQL is a standard way, a set of specifications that provide languages and protocols to query and manipulate RDF graph content on the Web or in an RDF store. We use the SPARQL Constructs to transform between different RDF representations i.e. from Base RDF to other RDF formats (e.g. Resume Ontology, Linked-USDL).

Fitman webinar 2015 09-21 Generation and Transformation of Virtualized Assets (GeToVA)

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