Speaker
Description
Tables and graphs are knowingly used to organise data within a company with different levels of depth and complexity. Knowledge graphs (KGs) are particularly useful because they can cope with data diversity (high-quality complete data and sparse and incomplete data), they have a high degree of scalability and flexibility (the semantic data model can be inter-operational, large, wide and as deep as needed) and, last but not least, they provide reasoning and inference capabilities. On the other hand, the need for intelligent systems enabling the access to heritage information is becoming more and more pressing with the growing amount of accumulated data. This is especially relevant for Model Based System Engineering (MBSE) design where the creation of new complex concepts is facilitated by agreed data standards and data synchronisation tools. In this activity, ESA Engineering Models (EMs) based on the data model defined in the ECSS-E-TM-10-25A Annex A are migrated to a Knowledge Graph. The graph is further enriched with metadata information collected from the mission's feasibility reports. The resulting graph is first used to investigate similarities between past missions and to identify common subsystems architectures. Then, based on the knowledge contained in the KG, a novel recommender system to suggest engineering components for new missions is deployed.
