Computer-Aided Biomimetics

Kruiper, Ruben; Chen-Burger, Jessica; Desmulliez, Marc Phillipe Yves

doi:10.1007/978-3-319-42417-0_13

Cited by 10 publications

(10 citation statements)

References 29 publications

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“…This is because most engineers know little biology or characteristics of animals and plants. A plethora of biomimetic design methods have therefore been proposed to provide this support [9].…”

Section: The Problem Of Finding Relevant Biological Systemsmentioning

confidence: 99%

“…Computational systems, on the other hand, especially those that aim to reason over biological and engineering information or map information across domains, require more formal definitions of terms that may indicate functions. Moreover, a biologist may not define a function to the satisfaction of an engineer, which complicates searches and can lead to misapplication of terminology and wrong or simplified interpretations of biological functions [9,36]. Section 3.3 elaborates on definitions and the use of the term function in engineering, biology and biomimetics.…”

Section: The Issues Underlying the Search Problemmentioning

confidence: 99%

“…Furthermore, representing a biological system at different levels of abstraction is generally found to be beneficial [9]. Gramann for example proposes an associative check list to support transfer of biological functions to technical functions, aiming to support analysis and synthesis in a typical system engineering procedure [32].…”

Section: The Issues Underlying the Search Problemmentioning

confidence: 99%

“…Section 2 addresses the support that Computer-Aided Biomimetics (CAB) tools should provide following a literature review [1,5,6,7,8,9]. The search for biological information is thought to be most comprehensive and effective if biological texts are used [10].…”

Section: Introductionmentioning

confidence: 99%

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Towards a Design Process for Computer-Aided Biomimetics

et al. 2018

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Computer-Aided Biomimetics (CAB) tools aim to support the integration of relevant biological knowledge into biomimetic problem-solving processes. Specific steps of biomimetic processes that require support include the identification, selection and abstraction of relevant biological analogies. Existing CAB tools usually aim to support these steps by describing biological systems in terms of functions, although engineering functions do not map naturally to biological functions. Consequentially, the resulting static, functional view provides an incomplete understanding of biological processes, which are dynamic, cyclic and self-organizing. This paper proposes an alternative approach that revolves around the concept of trade-offs. The aim is to include the biological context, such as environmental characteristics, that may provide information crucial to the transfer of biological information to an engineering application. The proposed design process is exemplified by an illustrative case study.

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Section: The Problem Of Finding Relevant Biological Systemsmentioning

confidence: 99%

Section: The Issues Underlying the Search Problemmentioning

confidence: 99%

Section: The Issues Underlying the Search Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards a Design Process for Computer-Aided Biomimetics

et al. 2018

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“…This problem is even more pronounced in inter-disciplinary fields of study, where experts in a target domain often lack the deeper knowledge of a source domain (Carr et al, 2018). A specific example is biomimetics, an engineering problem-solving process in which one draws on analogous biological solutions (Kruiper et al, 2016). A major issue is that engineers (target domain) know little biology (source domain) or characteristics of plants or (Burgess et al, 2006).…”

mentioning

confidence: 99%

In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts

Kruiper¹,

Vincent²,

Chen-Burger³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of the information captured, and Open IE systems do not perform well on the long and complex sentences encountered in scientific texts. In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain. First, we present the Focused Open Biological Information Extraction (FO-BIE) dataset and use FOBIE to train a state-ofthe-art narrow scientific IE system to extract trade-off relations and arguments that are central to biology texts. We then run both the narrow IE system and a state-of-the-art Open IE system on a corpus of 10k open-access scientific biological texts. We show that a significant amount (65%) of erroneous and uninformative Open IE extractions can be filtered using narrow IE extractions. Furthermore, we show that the retained extractions are significantly more often informative to a reader. 1

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