Space mission design ontology: extraction of domain-specific entities and concepts similarity analysis

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Abstract

Expert Systems, computer programs able to capture human expertise and mimic experts' reasoning, can support the design of future space missions by assimilating and facilitating access to accumulated knowledge. To organise these data, the virtual assistant needs to understand the concepts characterising space systems engineering. In other words, it needs an ontology of space systems. Unfortunately, there is currently no official European space systems
ontology. Developing an ontology is a lengthy and tedious process, involving several human domain experts, and therefore prone to human error and subjectivity. Could the foundations of an ontology be instead semi-automatically extracted from unstructured data related to space systems engineering? This paper presents an implementation of the first layers of the Ontology Learning Layer Cake, an approach to semi-automatically generate an ontology. Candidate
entities and synonyms are extracted from three corpora: a set of 56 feasibility reports provided by the European Space Agency, 40 books on space mission design publicly available and a collection of 273 Wikipedia pages. Lexica of relevant space systems entities are semi-automatically generated based on three different methods: a frequency analysis, a term frequency-inverse document frequency analysis, and Index filtering. The frequency-based lexicon of the combined corpora is then fed to a word embedding method, word2vec, to learn the context of each entity. With a cosine similarity analysis, concepts with similar contexts are matched.
Original languageEnglish
Pages1-13
Number of pages13
Publication statusPublished - 6 Jan 2020
EventAIAA SciTech Forum 2020 - Orlando, United States
Duration: 6 Jan 202010 Jan 2020
https://www.aiaa.org/SciTech?SSO=Y

Conference

ConferenceAIAA SciTech Forum 2020
CountryUnited States
CityOrlando
Period6/01/2010/01/20
Internet address

Keywords

  • ontology data
  • space mission design
  • machine learning
  • word embedding

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