In this paper, we introduce a system to collect product information from manufacturers and make it available in tools that are used for concurrent design of spacecraft. The planning of a spacecraft needs experts from different disciplines, like propulsion, power, and thermal. Since these different disciplines rely on each other there is a high need for communication between them, which is often realized by a Model-Based Systems Engineering (MBSE) process and corresponding tools. We show by comparison that the product information provided by manufacturers often does not match the information needed by MBSE tools on a syntactic or semantic level. The information from manufacturers is also currently not available in machine-readable formats. Afterwards, we present a prototype of a system that makes product information from manufacturers directly available in MBSE tools, in a machine-readable way.
PDF data sheets are meant to describe a product technically but there is no standard regarding the syntax or semantic of this description. There are several approaches to extract (semantic) information from data sheets, e.g. by @cite_21 , @cite_29 , @cite_12 , and @cite_0 , but we do not know of an accessible tool that performs that task reliably.
@cite_0 @cite_29 @cite_21 @cite_12
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Selection of the right space parts is an essential step during the design of complex engineering systems and requires information that is typically embedded in unstructured documents like datasheets, Interface Control Documents (ICDs) and technical manuals. Satsearch (https: satsearch.co) aims to consolidate global space supply chain information within a single platform, by converting unstructured datasheets into machine-readable, human-readable, electronic datasheets (EDS). After satsearch’s initial, manual efforts at generating EDS from source datasheets for space parts, they realized that the process is not scalable. A possible solution is to employ knowledge base information extraction systems. The Design Engineering Assistant (DEA) team from the University of Strathclyde is currently working on the automation of the extraction of information from unstructured documents (e.g. textbooks, reports, datasheets, research papers, etc.) through the development of an expert system. This paper summarizes the approach and outcomes of a feasibility study for the DEA, assessing benefits and obstacles for the implementation of a fully-automated information extraction process, focusing at this stage only on datasheets for space parts for preliminary mission design. In the context of the Pangea project at IBM, we needed to design an information extraction module in order to extract some information from datasheets. Contrary to several information extraction systems based on some machine learning techniques that need some linguistic parsing of the documents, we propose an hybrid approach based on association rules mining and decision tree learning that does not require any linguistic processing. The system may be parameterized in various ways that influence the efficiency of the information extraction rules we discovered. The experiments show the system does not need a large training set to perform well. The development of information retrieval and extraction systems is still a challenging task. The occurrence of natural language limits the application of existing approaches. Therefore the approach of a new framework which combines natural language processing and semantic web technology is discussed.