Approaches to Automatically Extract Affordances from Patents

Chiarello, Filippo; Cirri, Ilenia; Melluso, Nicola; Fantoni, Gualtiero; Bonaccorsi, Andrea; Pavanello, Tommaso

doi:10.1017/dsi.2019.255

Cited by 6 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is noteworthy that in their paper may not encompass all essential design entities. Some entities, such as "misuse," "affordance," and "wirk elements" are marginally extracted with NLP, despite the existence of preliminary works attempting to recognize these entities in textual data (Chiarello et al, 2019;Melluso et al, 2021). Another difficulty to be considered is related to certain entities can be considered atomic (a single irreducible unit) and others not.…”

Section: Atoms and Moleculesmentioning

confidence: 99%

Towards the extraction of semantic relations in design with natural language processing

Giordano,

Consoloni,

Chiarello

et al. 2024

Proc. Des. Soc.

View full text Add to dashboard Cite

Natural Language Processing (NLP) has been extensively applied in design, particularly for analyzing technical documents like patents and scientific papers to identify entities such as functions, technical feature, and problems. However, there has been less focus on understanding semantic relations within literature, and a comprehensive definition of what constitutes a relation is still lacking. In this paper, we define relation in the context of design and the fundamental concepts linked to it. Subsequently, we introduce a framework for employing NLP to extract relations relevant to design.

show abstract

Section: Atoms and Moleculesmentioning

confidence: 99%

Towards the extraction of semantic relations in design with natural language processing

Giordano,

Consoloni,

Chiarello

et al. 2024

Proc. Des. Soc.

View full text Add to dashboard Cite

show abstract

“…The extraction of design relevant information from technical texts is a task that is affecting latest research endeavours in different academic fields, such as Engineering Design and Artificial Intelligence. The work done by the academic community in using text mining for engineering design purposes is currently demonstrating promising results (Chiarello et al 2019a;Chiarello et al 2020;Fantoni et al 2020). For example, NLP-based methodologies are used to detect and extract information about the functions, the physical behaviours and the states of the system directly from the text of a patent in an automatic way (Fantoni et al 2013).…”

Section: Extracting Technical Design Knowledge From Textmentioning

confidence: 99%

Detecting Bad Design and Bias From Patents

Melluso

Pardelli²,

Fantoni

et al. 2021

Proc. Des. Soc.

Self Cite

View full text Add to dashboard Cite

The representation of the product use context is a well established design practice in Engineering Design. Recently, design theory is studying the product interaction involving several cognitive aspects such as the possible conditions in which a wrong interaction occurs. The aim of this paper is to find a quantitative evidence of the causes of these misuses. In particular, this study focuses on the detection of bad design and biases.In this paper, we propose a method that helps to the automatic detection of bad design and biases from patents. The method is based on an approach that defines syntactic rules to detect sentences containing these artifacts. These rules are defined based on an exploratory analysis of the explicit mention of “bad design” and “bias” and then, tested with multiple experiments on a sample of patents. The results give a first quantitative evidence of the presence of bad design and biases in patents and consequently of their importance in the design theory. In particular, it is provided a fine grain analysis of the linguistic structure of sentences containing these artifacts helping designers in detecting automatically them from patents.

show abstract

“…The aforementioned studies are mainly aimed at alleviating the problem of this paper and lack in-depth research on the issue. In this study, the new knowledge graph connecting data science and engineering design inspires [15][16][17][18], and we propose a new industrial safety knowledge graph integrating matter of fact, which is based on industrial safety reports and can improve the effective comprehensiveness of safety reports. First, because the knowledge structure of different processes in different process industries is different [19], for example, the "low-low interlock" of the oil and gas inlet process and the "open valve" of the vaporization process are different, we analyze and summarize and creatively develop a general safety knowledge standardization framework for industrial safety report extraction in a top-down manner.…”

Section: Introductionmentioning

confidence: 99%

A Study on a Knowledge Graph Construction Method of Safety Reports for Process Industries

et al. 2023

View full text Add to dashboard Cite

There are some representative reports in industrial safety engineering, such as the Hazard and Operability Analysis and Pre-Hazard Analysis; however, a large amount of industrial safety knowledge in the report has not been fully explored. In order to reuse and release the value of industrial safety knowledge, this paper constructs a new industrial safety knowledge extraction framework. The framework combines the asset management shell to summarize the knowledge concept entities of machine description language and model description language. According to the safety report template, the framework also constructs a new industrial safety knowledge-mapping standard structure. Specifically, firstly, considering that the knowledge structure of safety reports is different in different processes of the process industry, this paper innovatively proposes a general industrial safety knowledge-mapping standard structure, which provides a practical solution for the integration of industrial knowledge representation problems in different processes. Secondly, based on the research progress of named entities, this paper presents an industrial named entity extraction method (INERM) for the process industry. This method designs an entity weight model to calculate the entity weight of each sentence, and adds part-of-speech weight to improve the entity extraction algorithm, which alleviates the problem that the existing entity extraction methods cannot reasonably use the semantic information and context of word. Finally, we construct a triple of industrial safety knowledge based on the rules and store it in Neo4j. In this paper, four semantic-type templates and five semantic relation templates are constructed based on the new industrial safety knowledge map standardization construction process of the process industry. The comparative experiments show that the accuracy of the INERM on the test set is improved by 17 percentage points on average compared with other key entity extraction algorithms. A total of 1329 entities are constructed in the directional application example of the fluid transportation process, which provides a large number of references for the safety of the fluid transportation process and is more conducive to improving the safety guarantee of the fluid transport process.

show abstract

Approaches to Automatically Extract Affordances from Patents

Cited by 6 publications

References 21 publications

Towards the extraction of semantic relations in design with natural language processing

Towards the extraction of semantic relations in design with natural language processing

Detecting Bad Design and Bias From Patents

A Study on a Knowledge Graph Construction Method of Safety Reports for Process Industries

Contact Info

Product

Resources

About