Extraction of Manufacturing Rules From Unstructured Text Using a Semantic Framework

Abstract

Formal ontology and rule-based approaches founded on semantic technologies have been proposed as powerful mechanisms to enable early manufacturability feedback. A fundamental unresolved problem in this context is that all manufacturing knowledge is encoded in unstructured text and there are no reliable methods to automatically convert it to formal ontologies and rules. It is impractical for engineers to write accurate domain rules in a structured semantic languages such as Web Ontology Language (OWL) or Semantic Application Design Language (SADL). Previous efforts in manufacturing research that have targeted extraction of OWL ontologies from text have focused on basic concept names and hierarchies. This paper presents a semantics-based framework for acquiring more complex manufacturing knowledge, primarily rules, in a semantically-usable form from unstructured English text such as those written in manufacturing handbooks. The approach starts with existing domain knowledge in the form of OWL ontologies and applies natural language processing techniques to extract dependencies between different words in the text that contains the rule. Domain-specific triples capturing each rule are then extracted from each dependency graph. Finally, new computer-interpretable rules are composed from the triples. The feasibility of the framework has been evaluated by automatically and accurately generating rules for manufacturability from a manufacturing handbook. The paper also documents the cases that result in ambiguous results. Analysis of the results shows that the proposed framework can be extended to extract domain ontologies which forms part of the ongoing work that also focuses on addressing challenges to automate different steps and improve the reliability of the system.

A survey of ontology learning techniques and applications

“…Dependency analysis helps in finding relations between terms by using dependency information present in parsing trees ( 37 ). Ciaramita et al ( 38 ) used dependency paths information present in parse trees to find relationship patterns.…”

A cloud service platform integrating additive and subtractive manufacturing with high resource efficiency

“…To be specific, an IoT-based framework for real-time information capturing and integration was proposed by , so that the real-time status of manufacturing resources can be reached by the cloud platform and customers. The Web Ontology Language (OWL) was applied to extract the manufacturing characteristics (Kang et al, 2015), and the manufacturing capabilities were encapsulated by using the Manufacturing Service Description Language (MSDL) (Ameri and McArthur, 2014). Then, the model of manufacturing services could be established with UDDI (Jang et al, 2008).…”

Automated Extraction of System Structure Knowledge From Text

“…In biomimetic design, Cheong and Shu [28] used syntactic rules to retrieve causally related functions from biology texts, to support analogical reasoning. Recently, Kang et al [29] attempted to extract manufacturing rules in the form of Semantic Application Design Language from text and Renu and Mocko [30] investigated potentially extracting assembly planning knowledge from assembly work instructions. While all of the above mentioned work used off-the-shelf parsers, Zeng [31] used the author's own recursive object model to translate requirements text into UML diagrams.…”