Semantic hyper-graph-based knowledge representation architecture for complex product development

Wu, Zhihua; Liao, Jihua; Song, Wenyan; Mao, Hanling; Huang, Zhenfeng; Li, Xinxin; Mao, Hanying

doi:10.1016/j.compind.2018.04.008

Cited by 39 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ringsquandl et al described a graph-based analytics framework for advanced manufacturing analytic [31]. Wu et al presented a semantic hyper-graphbased knowledge representation framework to support knowledge sharing for product development [32]. Bruno et al proposed a data model called the manufacturing knowledge organization (MAKO) to help managers structure and reuse the technological knowledge available [33].…”

Section: B Manufacturing Knowledge Managementmentioning

confidence: 99%

Social Production System: A Three-Layer Smart Framework for Implementing Autonomous Human-Machine Collaborations in a Shop Floor

2021

View full text Add to dashboard Cite

The continuous fast-changing of production requirements and the development of advanced information technology have promoted the development of the production system in a more knowledgeintensive, resources-decentralized, and autonomous collaborative manner. In order to provide guidelines for the smart transformation of the production shop floor, this paper proposes a smart production system with a three-layer framework defined as the social production system (Social-PS) by integrating cyber-physical system (CPS), knowledge graph technology, and production-event driven model. The concept of smart resources (include smart operator, smart machine, smart workpiece, and smart gateway), social sensor, the unified information model is clarified to construct the physical carrier of Social-PS. The unified knowledge repository's overall construction method based on the knowledge graph is elaborated to provide taskresource-associated heterogeneous production data sharing and management for human-machine collaboration. Subsequently, to support the production dynamic monitoring and interaction, a productionevent and active knowledge indexing model is proposed. Furthermore, to validate the proposed Social-PS framework, a software and hardware integrated prototype system is implemented to demonstrate the knowledge-driven human-machine collaboration of the production process, which provides a basis for realizing smart and autonomous production collaboration.

show abstract

Section: B Manufacturing Knowledge Managementmentioning

confidence: 99%

Social Production System: A Three-Layer Smart Framework for Implementing Autonomous Human-Machine Collaborations in a Shop Floor

2021

View full text Add to dashboard Cite

show abstract

“…Design knowledge is tacit and embedded in most cases and it is difficult for designers to express their knowledge fully and explicitly. For this reason, a set of Knowledge Acquisition processes (Wu et al 2018) are adopted by researchers with several methodological perspectives (from established engineering disciplines to psychology, from ethnographic to simulation and operations research) and use both qualitative and ICED21 quantitative techniques (Kan et al 2009;Shealy et al 2019). Thanks to the increasing availability of data, text mining (TM) has proven to be an important approach used by designer due to the possibility to: (i) extract information that are relevant for the design process but that are hidden in massive quantity of unstructured documents (Chiarello et al 2018;Chiarello et al 2019b); (ii) exploit publicly available sources, thus helping in resolving the problem of the non-availability of data (Parraguez et al 2017).…”

Section: Extracting Technical Design Knowledge From Textmentioning

confidence: 99%

Detecting Bad Design and Bias From Patents

Melluso

Pardelli²,

Fantoni

et al. 2021

Proc. Des. Soc.

View full text Add to dashboard Cite

The representation of the product use context is a well established design practice in Engineering Design. Recently, design theory is studying the product interaction involving several cognitive aspects such as the possible conditions in which a wrong interaction occurs. The aim of this paper is to find a quantitative evidence of the causes of these misuses. In particular, this study focuses on the detection of bad design and biases.In this paper, we propose a method that helps to the automatic detection of bad design and biases from patents. The method is based on an approach that defines syntactic rules to detect sentences containing these artifacts. These rules are defined based on an exploratory analysis of the explicit mention of “bad design” and “bias” and then, tested with multiple experiments on a sample of patents. The results give a first quantitative evidence of the presence of bad design and biases in patents and consequently of their importance in the design theory. In particular, it is provided a fine grain analysis of the linguistic structure of sentences containing these artifacts helping designers in detecting automatically them from patents.

show abstract

“…Kim [23] proposed a knowledge representation framework based on semantic hypergraphs to support knowledge sharing in product development. The KG construction process generally includes the following steps: entity extraction, unit relationship extraction, and structured display [24]. Z. Liu et al [25] presented the Entity-Duet Neural Ranking Model, which uses to the two components are learned end-to-end, making the Entity-Duet Neural Ranking Model (EDRM) a natural combination of entity-oriented search and neural information retrieval.…”

Section: Related Workmentioning

confidence: 99%

KnowIME: A System to Construct a Knowledge Graph for Intelligent Manufacturing Equipment

Yan

Yang

2020

IEEE Access

View full text Add to dashboard Cite

With the development of a new generation of information technology, such as big data and cognitive intelligence, we are in the postmodern era of artificial intelligence. Currently, the manufacturing industry is in the critical period of transitioning to smart manufacturing, but the cognitive capabilities of devices in smart factories are still scarce. Knowledge Graph (KG) is one of the key technologies of cognitive intelligence, which opens a new path for the horizontal integration of intelligent manufacturing. Therefore, this paper proposes and builds a manufacturing equipment information query system based on KG. Firstly, a large amount of heterogeneous data that contains vast devices information is obtained from the network. Secondly, the conditional random fields (CRF) algorithm is used to extract the entity name, product place, and company name of the device, and then the relationship between the device entities is identified by calculating the similarity and Chinese syntax analysis. In the validation section, we use to the map of Neo4j graph database, when we input a name of a device in the search box, the system can return a relational graph node. In addition, the shortest path optimization algorithm is used to calculate the similarity between nodes in the search process to achieve the recommendation of similar node information.

show abstract

Semantic hyper-graph-based knowledge representation architecture for complex product development

Cited by 39 publications

References 31 publications

Social Production System: A Three-Layer Smart Framework for Implementing Autonomous Human-Machine Collaborations in a Shop Floor

Social Production System: A Three-Layer Smart Framework for Implementing Autonomous Human-Machine Collaborations in a Shop Floor

Detecting Bad Design and Bias From Patents

KnowIME: A System to Construct a Knowledge Graph for Intelligent Manufacturing Equipment

Contact Info

Product

Resources

About