Supporting different stakeholder viewpoints across the product lifecycle requires semantic richness to represent product-related information and enable multiview engineering simulations. This paper proposes a multilevel product modeling framework enabling stakeholders to define product models and relate them to physical or simulated instances. The framework is defined within the Model-Driven Architecture using the multilevel (data, model, metamodel) approach. The data level represents real-world products, the model level describes models (product models) of real-world products, and the metamodel level describes models of the product models. The metamodel defined in this paper is specialized from a web ontology language enabling product designers to express the semantics of product models in an engineering-friendly way. The interactions between these three levels are described to show how each level in the framework is used in a product engineering context. A product design scenario and user interface for the product metamodel is provided for further understanding of the framework.

In enterprises operating large-scale equipment, such as plants, maintenance workers must quickly and accurately find and understand the information in the equipment maintenance documents to perform maintenance tasks effectively. If the equipment maintenance documents include sentences with semantically ambiguous expressions, it will interfere with the maintenance knowledge search, and it may affect the maintenance performance of engineers. In order to solve these problems, text-based research of maintenance documents have been done to extract the key information or knowledge from these documents. Previous studies focused on finding the technical terminologies or calculating the similarity of documents using named entity recognition approaches. This paper proposes a method to extract knowledge of not only the technical terminologies but also their relations. The proposed method uses a rule-based approach that can be applied to the results of a named entity recognition approach and a dependency parsing approach. The named entity recognition approach found technical terms and the dependency parsing approach provided sentence structure information, so that the proposed method showed that a set of rules can extract maintenance knowledge, including entities and their relations. Trouble-shooting documents in the field were used as an experiment to demonstrate the effectiveness of the proposed method, and the experiment showed the possibility of practical use of the proposed method.

Several works have been done in the last decades for understanding tables in documents, but most of them were not specifically designed to understand tables in engineering specification documents. Tables in engineering specifications have characteristics such as various table structures with restricted terms. A framework is developed to address the issues in understanding tables in engineering specification documents. The framework consists of three steps: (1) Identifying minimal tables, (2) classifying cells, and (3) extending a domain knowledge map. A modified XY-tree algorithm was developed to find minimal tables, and a neural network algorithm was adopted to classify cells into labels and data. Then, specific domain rules were developed to discover concepts and relationships from terms in the classified cells. It is assumed a domain ontology is given, and it is extended with new concepts and relationships extracted from tables. We illustrated how each step performed with engineering table examples. The proposed framework could be used for searching product specification and for discovering hidden knowledge from tables in engineering specification documents.