Idea generation with Technology Semantic Network

Abstract

There are growing efforts to mine public and common-sense semantic network databases for engineering design ideation stimuli. However, there is still a lack of design ideation aids based on semantic network databases that are specialized in engineering or technology-based knowledge. In this study, we present a new methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design. The core of the methodology is to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas. We demonstrate the effectiveness in general, and use strategies and ideation outcome implications of the methodology via a case study of flying car design idea generation.

Semantic Networks for Engineering Design: A Survey

“…In a benchmark comparison with other existing semantic network databases, including WordNet, ConceptNet, and B-Link, TechNet presented superior performances in term retrieval and inference tasks in the specific context of technology and engineering (Sarica et al, 2020). TechNet has been utilized to augment patent search (Sarica et al, 2019a), technology forecasting (Sarica et al, 2019b), idea generation (Sarica et al, 2021) and evaluation (Han et al, 2020) Table 2 summarizes the engineering design studies using semantic networks, highlighting the semantic networks employed and their applications. The (Goel et al, 2012) WordNet Concept association and expansion (Linsey et al, 2012) Executing queries (Siddharth and Chakrabarti, 2018) WordNet and FBS (Function-Behaviour-Structure) knowledge cell library Knowledge retrieval (Hu et al, 2017) WordNet and word2vec…”

“…In a benchmark comparison with other existing semantic network databases, including WordNet, ConceptNet, and B-Link, TechNet presented superior performances in term retrieval and inference tasks in the specific context of technology and engineering (Sarica et al, 2020). TechNet has been utilized to augment patent search (Sarica et al, 2019a), technology forecasting (Sarica et al, 2019b), idea generation (Sarica et al, 2021) and evaluation (Han et al, 2020) Table 2 summarizes the engineering design studies using semantic networks, highlighting the semantic networks employed and their applications. The (Goel et al, 2012) WordNet Concept association and expansion (Linsey et al, 2012) Executing queries (Siddharth and Chakrabarti, 2018) WordNet and FBS (Function-Behaviour-Structure) knowledge cell library Knowledge retrieval (Hu et al, 2017) WordNet and word2vec Triplets classification (Cheong et al, 2017) ConceptNet Knowledge retrieval and reasoning (Yuan and Hsieh, 2015) Concept association and expansion (Han et al, 2018a) Knowledge retrieval and reasoning (Han et al, 2018b) Query expansion; ideation aid (Chen and Krishnamurthy, 2020) Hand-built extracting thematic relations in domain-specific knowledge Knowledge retrieval and reasoning (Georgiev et al, 2017) Total technology space map (TSM) Knowledge retrieval and reasoning (Luo et al, 2018; B-Link Cross-domain knowledge associations (Chen et al, 2019) TechNet Prior art search, design knowledge discovery and representation, idea generation and evaluation (Sarica et al, 2019a;2019b;Han et al, 2020) SAPPhIRE model ontology Knowledge retrieval (Acharya and Chakrabarti, 2020)…”

Design Knowledge Representation With Technology Semantic Network

“…Network metrics have provided a medium to derive useful design-related insights from the structure of the graphs, and various layout methods have provided ways of representing the design-related data in an easily comprehensible way (Lim et al, 2016;. For example, network visualizations have been utilized to represent the whole technology space to support innovation and competitive intelligence (Luo et al, 2017(Luo et al, , 2018Sarica, Yan, et al, 2020), show the relations between components and subsystems to evalute designs (He and Luo, 2017;Pasqual and De Weck, 2012;Sosa et al, 2007) and inform design decisions (Kim and Kim, 2012;Sosa et al, 2007), discover the patterns of design activities (Alstott et al, 2017;Cash et al, 2014;Cash and Štorga, 2015), reveal the structure of design document repositories to guide retrievals (Fu et al, 2013;Luo et al, 2021), and represent mind maps (Camburn, Arlitt, et al, 2020;Camburn, He, et al, 2020) and concept networks (Chen et al, 2019;Chen and Krishnamurthy, 2020;Liu et al, 2020;Sarica et al, 2019Sarica et al, , 2021Shi et al, 2017;Song, Evans, et al, 2020;Souili et al, 2015) for design ideation uses. On the other hand, a few studies explored other visualization methods such as word-clouds (He, Camburn, Liu, et al, 2019; based on design description texts.…”

Deriving Design Knowledge Graph for Complex Sociotechnical Systems Using the AIA Design Thinking

“…Prior studies have suggested the advantages of using semantic network and knowledge graphs in healthcare service design [10], engineering design [11][12][13], and indoor scene design [14]. For instance, Sarica et al [15] proposed a methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design, which aims to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas; Liang et al [14] proposed a knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design and further developed a weakly supervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning; Luo et al [16] harnessed data-driven design and rapid ideation techniques to introduce a data-driven computer-aided rapid ideation process using the cloud-based InnoGPS system, which integrates an empirical network map of all technology domains based on the international patent classification which are connected according to knowledge distance based on patent data, with a few mapbased functions to position technologies, explore neighborhoods, and retrieve knowledge, concepts, and solutions in the near or far fields for design analogies and syntheses. Chen et al [17] proposed an integrated approach for enhancing design ideation by applying artificial intelligence and data mining techniques, which consists of a semantic ideation network and a visual concepts combination model to provide inspiration semantically and visually based on computational creativity theory.…”