Computer-aided biomimetics : semi-open relation extraction from scientific biological texts

Engineering inspired by biology – recently termed biom* – has led to various ground-breaking technological developments. Example areas of application include aerospace engineering and robotics. However, biom* is not always successful and only sporadically applied in industry. The reason is that a systematic approach to biom* remains at large, despite the existence of a plethora of methods and design tools. In recent years computational tools have been proposed as well, which can potentially support a systematic integration of relevant biological knowledge during biom*. However, these so-called Computer-Aided Biom* (CAB) tools have not been able to fill all the gaps in the biom* process. This thesis investigates why existing CAB tools fail, proposes a novel approach – based on Information Extraction – and develops a proof-of-concept for a CAB tool that does enable a systematic approach to biom*. Key contributions include: 1) a disquisition of existing tools guides the selection of a strategy for systematic CAB, 2) a dataset of 1,500 manually-annotated sentences, 3) a novel Information Extraction approach that combines the outputs from a supervised Relation Extraction system and an existing Open Information Extraction system. The implemented exploratory approach indicates that it is possible to extract a focused selection of relations from scientific texts with reasonable accuracy, without imposing limitations on the types of information extracted. Furthermore, the tool developed in this thesis is shown to i) speed up a trade-off analysis by domain-experts, and ii) also improve the access to biology information for non-exper

Computer-Aided Biomimetics : Semi-Open Relation Extraction from scientific biological texts

Engineering inspired by biology – recently termed biom* – has led to various groundbreaking technological developments. Example areas of application include aerospace engineering and robotics. However, biom* is not always successful and only sporadically applied in industry. The reason is that a systematic approach to biom* remains at large, despite the existence of a plethora of methods and design tools. In recent years computational tools have been proposed as well, which can potentially support a systematic integration of relevant biological knowledge during biom*. However, these so-called Computer-Aided Biom* (CAB) tools have not been able to fill all the gaps in the biom* process. This thesis investigates why existing CAB tools fail, proposes a novel approach – based on Information Extraction – and develops a proof-of-concept for a CAB tool that does enable a systematic approach to biom*. Key contributions include: 1) a disquisition of existing tools guides the selection of a strategy for systematic CAB, 2) a dataset of 1,500 manually-annotated sentences, 3) a novel Information Extraction approach that combines the outputs from a supervised Relation Extraction system and an existing Open Information Extraction system. The implemented exploratory approach indicates that it is possible to extract a focused selection of relations from scientific texts with reasonable accuracy, without imposing limitations on the types of information extracted. Furthermore, the tool developed in this thesis is shown to i) speed up a trade-off analysis by domain-experts, and ii) also improve the access to biology information for nonexperts

A scientific information extraction dataset for nature inspired engineering

Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.Comment: Published in Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). Updated dataset statistics, results unchange

Trade-offs in Computer-aided Biomimetics

Biomimetics, the application of mechanisms observed in nature to inform technical solutions, is inherently cross-disciplinary. For the most part, however, practitioners are only expert in one domain, e.g., engineering. Being a layman in the other domain, biology, can make it hard and time-consuming to find and understand relevant information. Computer-Aided Biomimetics (CAB) involves the development of computational tools to overcome this domain-expertise mismatch. Finding a bridge between engineering and biology has been challenging. Although a plethora of methodological approaches have been proposed to bridge the engineering and biology domains, Biomimetics remains adventitious and research intensive. We give an overview of previous research efforts in CAB and motivate our approach that revolves around the resolution of biological trade-offs. This is a unique approach, as previous work has always aimed to extract engineering functions from biological texts. We describe our novel CAB system that extracts trade-offs, a within-domain concept to indicate a dialectical relation between two or more biological traits. We provide a description of our dataset for the extraction of trade-offs from biology research papers, as well as our state-of-the-art Relation Extraction system. The dataset consists of over 1.5k sentences taken from biology research papers, describing a trade-off or similar high-level relation between two or more concepts. Furthermore, we provide an in-depth analysis of the information extracted by our CAB system from a corpus of 10k biology research papers. We show in a qualitative analysis that our system extracts key concepts and relations from biology research papers that are relevant to Biomimetics. Unique to our approach is that our system makes it feasible to collect a comprehensive list of the system parameters and solution principles used in biology. This enables statistical analysis, such as finding the distribution of fundamental principles among the resolution of various trade-offs. Notably, the solutions to trade-offs differ little over various hierarchical levels of biology. This makes our finding relevant to any research that aims to find desired, but underutilized, properties observed in nature

Robust Unconventional Interaction Design and Hybrid Tool Environments for Design and Engineering Processes

This paper investigates how and whether existing or current design tools, assist and support designers and engineers in the early-phases of ideation and conceptualization stages of design and engineering processes. The research explores how fluidly and/or congruously technology affords cognitive, emotive, gesture-based shape-and-form transformation and stimulates externalization within a hybrid design tool environment (HDTE). Meta-cognitive, emotive, gestural, sensorial, multi-dimensional interaction through exploration, translation and manifestation within a contextual blended environment is studied to enhance representation, stimulate choice-architecture and foster decision-making. Current and novel hybrid design tool developments and experiments illustrate the promise of hybridization for natural computing and unobtrusive design-tools (HDT) and cyber-physical systems (CPS). Put into perspective; a proposed framework of robust interaction design (IxD), gamification and affective computing (e.g. emotion) to improve and intensify user-experience (UX) and user-engagement (UE) is presented. The paper concludes by considering the allowance for possible novel routes to increase the scope and forging of links on prevailing frames of human-computer interaction (HCI)