TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing

Jang, Hye-Jin; Jeong, Yujin; Yoon, Boram

doi:10.1016/j.eswa.2020.114042

Cited by 26 publications

(9 citation statements)

References 34 publications

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“…While ontology-based approaches are recommended over common-sense lexicon (e.g., WordNet), it is necessary to rely on domain-specific language models and generic- design- and technical-oriented lexicon to identify general terms (e.g., rough surface). Although such supports are hard to build, there has been recent progress in the literature that adopts patent databases to develop a generic lexicon (Sarica, Luo, and Wood 2020; Jang, Jeong, and Yoon 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Using a web-based tool called TechNet, 31 they facilitate a search for these terms (Sarica et al 2021) and utilise the embeddings of these to construct a similarity network (Sarica and Luo 2021). To create an engineering alternative to WordNet, Jang, Jeong, and Yoon (2021) collect 34,823 automotive patents (IPC B60). They examine the dependency patterns in abstracts and claim to extract dependency relations that form the TechWord network.…”

Section: Reviewmentioning

confidence: 99%

“…A design knowledge graph includes facts like <'stapler', 'comprises', 'leaf spring'>, < 'hammer', 'push', 'staple' > that could be utilised or generated in the design process. A design knowledge graph carries informative as well as reasoning advantages over networks (Han et al 2021) that provide pairwise statistical (Sarica, Luo, and Wood 2020), semantic (Casakin and Georgiev 2021) and syntactic (Jang, Jeong, and Yoon 2021) relationships among a large collection of design terms (lexicon).…”

Section: Design Knowledge Graphmentioning

confidence: 99%

See 2 more Smart Citations

Natural language processing in-and-for design research

2022

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We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

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Section: Discussionmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Section: Design Knowledge Graphmentioning

confidence: 99%

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Natural language processing in-and-for design research

2022

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“…Since the development TRIZ [1], patent documents have enabled various applications in engineering design research [2], education [3], and practice [4], while also aiding business applications concerning innovation [5], intellectual property and knowledge management [6]. For instance, domain concepts extracted from patent documents, such as a range of terms [7], topics [8], facts [9], are useful in design-cum-learning environments, form building blocks for domain ontologies [10]- [12], and also indicate TRIZ trends for technology forecasting [13]. In view of business applications, studying the emergent properties of citation and inventor networks offers insights that are useful for bibliometric [14] and management studies [15].…”

Section: Motivationmentioning

confidence: 99%

Enhancing patent retrieval using text and knowledge graph embeddings: a technical note

Siddharth

Luo

2022

Journal of Engineering Design

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Patent retrieval influences several applications within engineering design research, education, and practice as well as applications that concern innovation, intellectual property, and knowledge management etc. In this article, we propose a method to retrieve patents relevant to an initial set of patents, by synthesizing state-ofthe-art techniques among natural language processing and knowledge graph embedding. Our method involves a patent embedding that captures text, citation, and inventor information, which individually represent different facets of knowledge communicated through a patent document. We obtain text embeddings using Sentence-BERT applied to titles and abstracts. We obtain citation and inventor embeddings through TransE that is trained using the corresponding knowledge graphs. We identify using a classification task that the concatenation of text, citation, and inventor embeddings offers a plausible representation of a patent. While the proposed patent embedding could be used to associate a pair of patents, we observe using a recall task that multiple initial patents could be associated with a target patent using mean cosine similarity, which could then be utilized to rank all target patents and retrieve the most relevant ones. We apply the proposed patent retrieval method to a set of patents corresponding to a product family and an inventor's portfolio.

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“…On another hand, for the reason that SAO structure can not only contains the extracted noun phrases information (i.e., "S" and "O"), but also can effectively maintain the internal relations between those elements (i.e., "A"), this paper uses the SAO triplet structure to form the extended P&S semantic pattern, where "S" represents the technical problem to be solved in an engineering case abstract, and "AO" represents the solution of the problem based on a background "context" causes and with a "result". Meanwhile, the internal relation chains of SAO structure have been classified into four types in the active-passive and clause-modifying phrase views [23] as shown in the Table 1.…”

Section: ) Knowscion Extraction: Knowwords Constructionmentioning

confidence: 99%

A Heuristic Grafting Strategy for Manufacturing Knowledge Graph Extending and Completion Based on Nature Language Processing: KnowTree

Dong

Jiang

2021

IEEE Access

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Applied to search, question answering, and semantic web of close-or-open domain, knowledge graph (KG) is known for its incompleteness subject to the rapid knowledge growing pace. Inspired by the agricultural grafting technology to fruit variety, this paper proposes a heuristic knowledge grafting strategy (HGS) for manufacturing knowledge graph (MKG) named KnowTree extending and completion with natural language processing (NLP) mining engineering cases document. Based on similarity analysis, firstly the grafting related definitions and mechanisms (completeness, relatedness, connectivity and reutilization) are defined. Then, focused on the four mechanisms, HGS takes a pair same engineering documents as input. KnowWords is built as a collection of KnowScion, and each scion is mined from engineering documents based on the SAO structure network, whose importance is evaluated by SAORank counting the in-out degree of centrality. On another hand, the KnowRoot system is designed based on the extended P&S ontology model to characterize the structure of abstract document into four subspace of knowledge: know-what (problem), know-why (context), know-how (solution) and know-with (result), where a pre-trained language representation model K-BERT is used to classify the KnowScion candidates into the designed KnowRoot system with a fine-tuning classification task. In the knowledge grafting process, the connection unit is constructed based on the extracted domain knowledge triples of the K-BERT model, where the head element of a triple is from the KnowScion candidate set KnowWords satisfying the threshold value, the tail element is from the domain MKG to be extended, and a connection factor is used to evaluate the relationship of union combination. To the goal of knowledge reuse, the path based reasoning rules are designed for KnowTree reutilization. Finally, take the latest engineering case abstract (ECA) in whitegoods domain as resources, a case study is conducted to validate the proposed HGS strategy.INDEX TERMS knowledge graph extending and completion, heuristic grafting strategy (HGS), NLP

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TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing

Cited by 26 publications

References 34 publications

Natural language processing in-and-for design research

Natural language processing in-and-for design research

Enhancing patent retrieval using text and knowledge graph embeddings: a technical note

A Heuristic Grafting Strategy for Manufacturing Knowledge Graph Extending and Completion Based on Nature Language Processing: KnowTree

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