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

Abstract

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.

TechNet: Technology semantic network based on patent data

“…We evaluated the performances of the , the vocabulary of the pre-trained GloVe word vectors based on Wikipedia and Gigaword (400 thousand words) (Pennington et al, 2014), and the vocabulary in the semantic network of Shi et al (2017) based on engineering paper publication data.…”

“…These terms closely related to "wireless charger" represent technical concepts regarding functions, components, configurations or working mechanisms. By contrast, neither WordNet (Fellbaum, 2012;Miller et al, 1990), ConceptNet (Speer et al, 2016;Speer & Havasi, 2012;Speer & Lowry-Duda, 2017) nor the semantic network of Shi et al (2017) contain the "wireless charger" term. In particular, we checked Google Knowledge Graph's term recommendations for "wireless charger", and the results are more related to consumer brands and products that have wireless charging capabilities ( Table 6).…”

“…In a recent effort, Shi et al (2017) fetched and analysed nearly one million engineering papers from ScienceDirect since 1997 and one thousand design posts from blogs and design websites, such as Yanko Design, to create a large semantic network in the engineering and design contexts. Although our 1 https://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html interests are aligned, it is unclear if their mixed data of different types; e.g., academic papers and design blogs, and the sole ScienceDirect publication data (for 20 years) can provide inclusive and balanced representation of engineering knowledge in different domains.…”

Predicting Part Mass, Required Support Material, and Build Time via Autoencoded Voxel Patterns

“…However, we are not aware of any attempt to utilize these algorithms to provide design-centric feedback to assist novice designers. More generally, research on data-driven methodologies for engineering design is on the rise [32] and has included text-based design descriptions [33,34], patent text [35][36][37], and even online reviews [38,39]. However, the rise of online communities for solid modeling and engineering design (such as GrabCAD and Thingiverse) has made entirely new sources of data available for data mining and machine learning approaches.…”

A Further Exploration of the Three Driven Approaches to Combinational Creativity

“…However, it is challenging to generate new ideas that are creative. A variety of methods and tools have been explored to support designers in creative idea generation, for example, conventional ones such as brainstorming (Osborn, 1979) and SCAMPER (Eberle, 1996), advanced methods such as Deign-by-Analogy (Goldschmidt, 2001) and Bio-inspired design (Chakrabarti and Shu, 2010;Helms et al, 2009); computational tools such as DANE (Goel et al, 2012;Vattam et al, 2011), the B-Link (Chen et al, 2017;Shi et al, 2017), and the Retriever (Han et al, 2018b). In order to provide operational insights, it is significant to explore the crucial factors that could lead to ideas that are more creative.…”