Extraction of Formal Manufacturing Rules from Unstructured English Text. (May 2021) - Kang, SungKu; Patil, Lalit; Rangarajan, Arvind; Moitra, Abha; Jia, Tao; Robinson, Dean; Ameri, Farhad; Dutta, Debasish
Abstract: Semantics-based approaches – founded on the idea of explicitly encoding meaning separately from the data or the application code – are being applied to manufacturing, for example, to enable early manufacturability feedback. These approaches rely on formal, i.e., computer-interpretable, knowledge and rules along with the context or semantics, which facilitates the reuse and sharing of the knowledge via semantic web technologies. On the other hand, manufacturing knowledge has been maintained primarily in the form of unstructured English text. It is considered impractical for engineers to author accurate, formal, and structured manufacturing rules. However, previous efforts on extracting semantics from unstructured text in manufacturing have mainly focused on basic concept names and hierarchies for ontology creation, rather than extracting complex manufacturing rules. In this context, this paper focuses on the development of a semantics-based framework for acquiring formal manufacturing rules from English text, such as those written in manufacturing handbooks, by guiding standard Natural Language Processing (NLP) techniques with formal manufacturing knowledge (i.e., controlled vocabulary and domain ontology). Specifically, this paper studies the problem of rule extraction in the manufacturing domain, proposes the formal rule extraction framework, and demonstrates its feasibility. From the dataset of 133 sentences with a manufacturing rule, the proposed framework was able to extract correct rules from approximately 57% of the sentences. This paper also demonstrates the extensibility of the framework. Specifically, the framework was initially developed using the three sections of a manufacturing handbook, including milling, metal stamping, and die-casting sections, and could be successfully applied to the rest of the book after just updating the formal manufacturing knowledge to cover the other sections. This paper provides meaningful results in terms of formalization, thus will contribute to the development, sharing, and reuse of formal manufacturing knowledge that includes complex manufacturing rules. Highlights: Domain ontology can be used to transform NLP result to a formal manufacturing rule. Controlled vocabulary can improve the extensibility of rule extraction framework. Manufacturing rule model can help rule extraction framework infer implicit context.