Nearly every artifact of the modern engineering design process is digitally recorded and stored, resulting in an overwhelming amount of raw data detailing past designs. Analyzing this design knowledge and extracting functional information from sets of digital documents is a difficult and time-consuming task for human designers. For the case of textual documentation, poorly written superfluous descriptions filled with jargon are especially challenging for junior designers with less domain expertise to read. If the task of reading documents to extract functional requirements could be automated, designers could actually benefit from the distillation of massive digital repositories of design documentation into valuable information that can inform engineering design. This paper presents a system for automating the extraction of structured functional requirements from textual design documents by applying state of the art Natural Language Processing (NLP) models. A recursive method utilizing Machine Learning-based question-answering is developed to process design texts by initially identifying the highest-level functional requirement, and subsequently extracting additional requirements contained in the text passage. The efficacy of this system is evaluated by comparing the Machine Learning-based results with a study of 75 human designers performing the same design document analysis task on technical texts from the field of Microelectromechanical Systems (MEMS). The prospect of deploying such a system on the sum of all digital engineering documents suggests a future where design failures are less likely to be repeated and past successes may be consistently used to forward innovation.
Semantic knowledge management system for design documentation with heterogeneous data using machine learning
Design documentation is presumed to contain massive amounts of valuable information and expert knowledge that is useful for learning from the
past successes and failures. However, the current practice of documenting design in most industries does not result in big data that can support a
true digital transformation of enterprise. Very little information on concepts and decisions in early product design has been digitally captured, and
the access and retrieval of them via taxonomy-based knowledge management systems are very challenging because most rule-based classification
and search systems cannot concurrently process heterogeneous data (text, figures, tables, references). When experts retire or leave a design unit,
industry often cannot benefit from past knowledge for future product design, and is left to reinvent the wheel repeatedly. In this work, we present
AI-based Natural Language Processing (NLP) models which are trained for contextually representing technical documents containing texts, figures
and tables, to do a semantic search for the retrieval of relevant data across large corpora of documents. By connecting textual and non-textual
data through the use of an associative database, the semantic search question-answering system we developed can provide more comprehensive
answers in the context of users’ questions. For the demonstration and assessment of this model, the semantic search question-answering system is
applied to the Intergovernmental Panel on Climate Change (IPCC) Special Report 2019, which is more than 600 pages long and difficult to read
and understand, even by most experts. Users can input custom queries relating to climate change concerns and receive evidence from the report
that is contextually meaningful. We expect this method can transform current repositories of design documentation of heterogeneous data forms
into structured knowledge-bases which can return relevant information efficiently as well as can evolve to embody manageable big data for the
true digital transformation of design.
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- Award ID(s):
- 1854833
- NSF-PAR ID:
- 10340890
- Editor(s):
- Anwer, Nabil
- Date Published:
- Journal Name:
- 32nd CIRP Design Conference (CIRP Design 2022) - Design in a changing world
- Volume:
- 109
- Page Range / eLocation ID:
- 95-100
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Motivation Figures in biomedical papers communicate essential information with the potential to identify relevant documents in biomedical and clinical settings. However, academic search interfaces mainly search over text fields.
Results We describe a search system for biomedical documents that leverages image modalities and an existing index server. We integrate a problem-specific taxonomy of image modalities and image-based data into a custom search system. Our solution features a front-end interface to enhance classical document search results with image-related data, including page thumbnails, figures, captions and image-modality information. We demonstrate the system on a subset of the CORD-19 document collection. A quantitative evaluation demonstrates higher precision and recall for biomedical document retrieval. A qualitative evaluation with domain experts further highlights our solution’s benefits to biomedical search.
Availability and implementation A demonstration is available at https://runachay.evl.uic.edu/scholar. Our code and image models can be accessed via github.com/uic-evl/bio-search. The dataset is continuously expanded.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1016/j.procir.2022.05
