390,324 research outputs found
Enriching Ontologies with Multilingual Information
Multilinguality in ontologies has become an impending need for institutions worldwide that have to deal with data and linguistic resources in different natural languages. Since most ontologies are developed in one language, obtaining multilingual ontologies implies to localize or adapt them to a concrete language and culture community. As the adaptation of the ontology conceptualization demands considerable efforts, we propose to modify the ontology terminological layer by associating an external repository of linguistic data to the ontology. With this aim we provide a model called Linguistic Information Repository (LIR) that associated to the ontology meta-model allows terminological layer localization
Modelling Multilinguality in Ontologies
an impending need for institutions
worldwide with valuable linguistic resources
in different natural languages.
Since most ontologies are developed in
one language, obtaining multilingual ontologies
implies to localize or adapt them
to a concrete language and culture community.
As the adaptation of the ontology
conceptualization demands considerable
efforts, we propose to modify the ontology
terminological layer, and provide a
model called Linguistic Information Repository
(LIR) that associated to the ontology
meta-model allows terminological
layer localization
From engineering models to knowledge graph : delivering new insights into models
Essential information on the early stages of a mission design is contained in Engineering Models. Yet, these models are often uneasy to visualise, query, let alone compare. This study demonstrates how Knowledge Graphs can overcome these data silos, interconnect information, provide a big-picture perspective, and infer new knowledge that would have remained hidden otherwise. Following the migration of CubeSats Engineering Models to a Knowledge Graph, two case studies are explored. The first case study illustrates how graph inference can derive implicit knowledge from existing explicit concepts. In the second case study, a Natural Language Processing layer is adjoined to the Knowledge Graph to enhances the analysis of textual content. The Natural Language Processing layer relies on the document embedding method doc2v
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