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
