Discovering a Hierarchical Design Process Model Using Text Mining

Lan, Lijun; Liu, Ying; Lu, Wen Feng

doi:10.1115/detc2016-59829

Cited by 3 publications

(4 citation statements)

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“…Similarly, Cheong et al also utilized SAO structures from text to acquire functional [50] and system structure knowledge [51] using Functional Basis terms [52], WordNet [53], and word2vec [54]. Lan et al [55] demonstrated the extraction of design information using the combination of content-based document clustering, named entity recognition (NER) and frequency-based entity relationship detection. Wang et al [56] extracted process contradiction matrices from patent documents using NLP and a domain ontology to support Computer-Aided Process Innovation (CAPI).…”

Section: Extraction Of Manufacturing Knowledge From Textmentioning

confidence: 99%

Extraction of Formal Manufacturing Rules from Unstructured English Text

Kang

Patil

Rangarajan

et al. 2021

Computer-Aided Design

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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. 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. Previous efforts on extracting semantics from unstructured text in manufacturing have focused exclusively on basic concept names and hierarchies. In this context, this dissertation focuses on the development of a semantics-based framework for acquiring more complex manufacturing knowledge, primarily rules, in a formal form, from unstructured English text such as those written in manufacturing handbooks. This dissertation includes the following specific research tasks. First, it studies the problem in manufacturing domain, proposes the formal rule extraction framework, and demonstrates its feasibility. Second, it extends the framework to complement standard Natural Language Processing (NLP) techniques with manufacturing domain knowledge to resolve ambiguities, called as domainspecific ambiguities, that are due to manufacturing-specific meanings implicit in the English text. Finally, this dissertation extends the framework to identify the cases that need input text validation, and provide the relevant feedback to the user to modify the input text for the extraction of correct rules.

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Section: Extraction Of Manufacturing Knowledge From Textmentioning

confidence: 99%

Extraction of Formal Manufacturing Rules from Unstructured English Text

Kang

Patil

Rangarajan

et al. 2021

Computer-Aided Design

View full text Add to dashboard Cite

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“…Unlike traditional document retrieval where processing information occurs at document or fragment level, the emergence of ontology-based techniques provides opportunities and effective mechanisms to process information at semantic level and extract and refine the relations between individual concepts from massive unstructured documents (Glier et al, 2014, Lim and Tucker, 2016, Lan et al, 2016, which can be subsequently structured and stored into design ontology-based systems (Rezgui et al, 2011, Chang et al, 2010, Liu et al, 2013 for knowledge representation and retrieval. This structured representation of design elements and associations can significantly help facilitate the information sharing among designers (Dong and Agogino, 1997).…”

Section: Automatic Ontology Constructionmentioning

confidence: 99%

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

Shi

Chen

Han

et al. 2017

Journal of Mechanical Design

121

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With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

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“…Similarly, the degree of incoming edges across groups measures the possibility that a people is a manager of the design process as managers usually interact with people from different departments. Equation ( 7) calculates the possibility that a participant is a manager s manager p ð Þ ¼ fr numðneiðpÞ À coopðpÞÞ fr numðPÞ Ã X p 0 2neiðpÞÀcoopðpÞ wðp; p 0 Þ (7) where P denotes all the involved people, neiðpÞ indicates the people who are directly connected to p in the social network graph, coopðpÞ are the people in the same group with p, wðp; p 0 Þ is the interaction strength of two people, and function fr numðÞ calculates the number of unique group labels in a set of participants.…”

Section: Role Analysismentioning

confidence: 99%

“…In our previous study, we have proposed a layered text mining system which aims to discover process model from design documents recording past design processes [7]. As an extension, this paper presents a methodology for learning critical processrelevant design knowledge from the past via an enriched process mining approach.…”

Section: Introductionmentioning

confidence: 99%

Learning From the Past: Uncovering Design Process Models Using an Enriched Process Mining

Lan

Liu

2018

Journal of Mechanical Design

Self Cite

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Design documents and design project footprints accumulated by corporate information technology systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Most of the existing design information extraction systems focus on either organizing design documents for efficient retrieval or extracting relevant product information for product optimization. Different from traditional systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. Particularly different from conventional techniques that deal with timestamps or event logs only, a new process mining approach that is able to directly process textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is analyzed to extract multifaceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multiyear multidisciplinary design project.

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Discovering a Hierarchical Design Process Model Using Text Mining

Cited by 3 publications

References 0 publications

Extraction of Formal Manufacturing Rules from Unstructured English Text

Extraction of Formal Manufacturing Rules from Unstructured English Text

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

Learning From the Past: Uncovering Design Process Models Using an Enriched Process Mining

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