From engineering models to knowledge graph: delivering new insights into models

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Abstract

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 doc2ve
Original languageEnglish
Number of pages7
Publication statusPublished - 30 Sep 2020
Event9th International Systems & Concurrent Engineering for Space Applications Conference (SECESA 2020) - Digital
Duration: 30 Sep 20202 Oct 2020
https://atpi.eventsair.com/QuickEventWebsitePortal/20c06-secesa/secesa

Conference

Conference9th International Systems & Concurrent Engineering for Space Applications Conference (SECESA 2020)
Period30/09/202/10/20
Internet address

Keywords

  • knowledge graph
  • Machine Learning
  • doc2vec
  • space systems design
  • concurrent engineering
  • engineering models

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  • Cite this

    Berquand, A., & Riccardi, A. (2020). From engineering models to knowledge graph: delivering new insights into models. Paper presented at 9th International Systems & Concurrent Engineering for Space Applications Conference (SECESA 2020), .