Standards are an important source of knowledge in product development. Due to the increasing digitization of the product development process, standard development organizations aim to develop machine-actionable standards that automatically enforce operations in output devices. However, the current representation format in PDF or XML does not meet the requirements of machine-actionable standards. This paper examines existing approaches towards the representation of XML data in knowledge graphs and their transferability towards the domain of digital standards. Based on these approaches, the paper aims to develop and validate a concept for transferring standard content from XML format to a graph-based representation, using the example of formulas. For this purpose, a concept for the automatic identification, extraction and modeling of formulas will be presented. Afterwards, the concept is validated using the example of DIN ISO 281 whereas a chatbot application serves as conversational user interface. It is proven, that knowledge graphs are suitable for the representation of machine-actionable standard content. Future work will investigate the abstraction towards a general approach as well as further information objects of standards.
Standards are a valuable source of knowledge in product development and support engineering activities throughout the entire product development process. However, against the industry-wide trend of digitization, the provision and usage of standards has not changed significantly over the last decade. To satisfy customer requirements, standards development organizations are increasingly interested in publishing their content outside of traditional formats such as print or PDF. One example is the content-based modularization rather than the provision of whole documents (Content-as-a-Service). This paper examines existing modularization approaches as well as their transferability towards the domain of standardization. Based on these approaches, a concept for the modularization of formulas and especially the development of a formula module is designed and elaborated. Therefore, descriptive elements of formulas are identified and structured. The module then serves as template for the future documentation of formulas in XML standards. Afterwards, the identified modules are integrated into an existing SMART standards expert system in order to demonstrate possible applications of content-based standard provision. Future work will investigate methods for the automatic extraction of the identified descriptive formula elements as well as their semantic modelling in knowledge graphs. Moreover, the described concept serves as starting point for future research in modularization of engineering standards.
The product development process faces several challenges, such as an increasing and differentiated number of customer requirements, increasing product complexity, and shortened time-to-market. To address these challenges, the implementation of automation approaches in form of machine learning (ML) algorithms appears promising. However, companies lack the implementation of these approaches in their processes, inter alia due to inadequate knowledge and experience in this field. Therefore, the aim of this paper is to develop a structured formulized way of characterising ML algorithms, which can support non-experts in identifying the optimal algorithm to solve a given problem. First, existing approaches covering the determination of appropriate ML algorithms for a given task are examined. Based on this, a pattern language approach is introduced to characterise ML algorithms and problems, allowing matching to be performed to identify the most suitable one for a given task. Due to their broad application, the concept is demonstrated by creating patterns for decision trees and artificial neural networks. A study is conducted to prove that the proposed concept is appropriate to support the ML algorithm selection.
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