ISO/TR 23845:2020
(Main)Biomimetics — Ontology-Enhanced Thesaurus (OET) for biomimetics
Biomimetics — Ontology-Enhanced Thesaurus (OET) for biomimetics
This document describes prototypes of the Ontology-Enhanced Thesaurus (OET) and the Keyword Explorer interface to OET. Although their design philosophy is described, this document focuses on their value and how they work.
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General Information
Standards Content (Sample)
TECHNICAL ISO/TR
REPORT 23845
First edition
2020-04
Biomimetics — Ontology-Enhanced
Thesaurus (OET) for biomimetics
Reference number
ISO/TR 23845:2020(E)
©
ISO 2020
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ISO/TR 23845:2020(E)
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ISO/TR 23845:2020(E)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Role of the knowledge infrastructure for biomimetics . 2
4.1 General . 2
4.2 Related work in the framework of the design processes of BID . 3
4.3 Positioning of OET in the context of the BID design process . 4
5 How Keyword Explorer works. 4
5.1 General . 4
5.2 A motivating example . 6
6 Ontology-Enhanced Thesaurus . 7
6.1 General . 7
6.2 Characteristics of biomimetics databases . 7
6.3 Basic design of a biomimetics database retrieval scheme . 7
6.4 Keyword translation and exploration . 9
6.4.1 General. 9
6.4.2 Keyword exploration: divergent thinking . 9
6.4.3 Keyword exploration: convergent thinking . 9
7 Ontologies in OET . 9
7.1 General . 9
7.2 Basic design of ontologies in OET . 9
7.3 Ontology of function .10
7.4 Concepts other than function .10
7.4.1 Taxonomy of organisms .10
7.4.2 Properties .10
7.4.3 Living environments .10
8 Implementation and evaluation of a prototype of Keyword Explorer .11
8.1 General .11
8.2 OET Versions .11
8.2.1 The demo version.11
8.2.2 The prototype version .11
8.3 Preliminary evaluation experiment .12
Annex A (informative) Ontology of function .15
Annex B (informative) How Keyword explorer works .18
Bibliography .19
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ISO/TR 23845:2020(E)
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
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ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the
different types of ISO documents should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www .iso .org/ directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of
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on the ISO list of patent declarations received (see www .iso .org/ patents).
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iso/ foreword .html.
This document was prepared by Technical Committee ISO/TC 266, Biomimetics.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www .iso .org/ members .html.
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ISO/TR 23845:2020(E)
Introduction
A thesaurus is often used to map terms between different knowledge domains. The Knowledge
Infrastructure for Biomimetics project was established to fill the gap between biology and technology.
The project originally planned to develop a biomimetic thesaurus and an ontology that would
complement such a thesaurus in situations where the thesaurus cannot deliver useful search terms
because concepts in the two domains are associated with keywords that lack explicit links. Although
work on the biomimetic thesaurus has been postponed, Ontology-Enhanced Thesaurus (OET) does not
require a thesaurus and can be used as a standalone tool. For more details see 5.2.
OET addresses a portion of this knowledge infrastructure. It is composed of an ontology of biomimetics
and an application named Keyword Explorer that provides an interface to the ontology. OET and
Keyword Explorer help designers, engineers, and other bio-inspired design (BID) practitioners by
mapping technical terms to biological terms that can then be used to search biological texts to identify
biological models (see Figure 3). For example, a traditional thesaurus may relate “stain-resistant” to
“self-cleaning” or “soil release”. A biomimetic thesaurus or internet keyword search may additionally
return “antifouling”. OET can identify organisms that share functions related to “antifouling” but not
directly associated with the term.
In Clause 4, after a brief overview of the current state of the art of tools and systems in biomimetics,
OET and Keyword Explorer are positioned in the related work. Clause 5 describes OET together with
its design rationale. In-depth description on the implemented ontology in OET is Clause 6 and Clause 7.
Clause 8 describes accessing and running the Keyword Explorer prototype in order to get feedback
from readers.
NOTE A publicly available version of Keyword Explorer and OET is available at http:// biomimetics .hozo
.jp/ OET/ demo .html as a web application — it only includes one function (antifouling) but demonstrates
the capabilities of the prototype version. The corresponding ontology can be inspected via “Browsing the
Biomimetics Ontology” in http:// biomimetics .hozo .jp/ OET/ , but it is not possible to download it. Paid users of
this document can download the prototype version.
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TECHNICAL REPORT ISO/TR 23845:2020(E)
Biomimetics — Ontology-Enhanced Thesaurus (OET) for
biomimetics
1 Scope
This document describes prototypes of the Ontology-Enhanced Thesaurus (OET) and the Keyword
Explorer interface to OET. Although their design philosophy is described, this document focuses on
their value and how they work.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at http:// www .electropedia .org/
3.1
data
minimum piece of information that is meaningful for its potential readers or users
Note 1 to entry: In many cases, data is a component of larger entity, a data set or a data base. Data can be text as in
research papers, simulation models, algorithms, numbers, pictures, figures, voice and video recordings.
3.2
database
set of almost any digital objects, which can be text, picture, sound, video, etc.
3.3
information retrieval service
set of software that allows users to retrieve information from databases (3.2)
Note 1 to entry: Quite often, ontologies or thesauri are incorporated in the information retrieval service.
3.4
index
set of key terms (usually arranged in alphabetical order) with pointers to the original source of each
term (includes books, research papers, or other forms of writing)
3.5
metadata
data (3.1) that provides information about other data
Note 1 to entry: The keywords in an index are metadata of the body of the text from which the keywords are
extracted.
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3.6
ontology
formal, structured, and explicit description of concepts in a domain of discourse and the relations
between them in the fields of knowledge management and artificial intelligence
Note 1 to entry: An ontology together with a set of individual instances of classes constitutes a knowledge base.
3.7
taxonomy
orderly classification of things or concepts into groups within a larger system, based on common
qualities
3.8
thesaurus
list of words arranged in groups based on similarity of meaning
4 Role of the knowledge infrastructure for biomimetics
4.1 General
In Figure 1, ISO 18457 and ISO 18458 cover the phases from “Analysis” to “Overall evaluation” and
ISO 18459 covers the phases from “Project/design of experiment” to “Prototype construction/
manufacturing.” The Knowledge Infrastructure for Biomimetics covers “Analysis” and “Analogy/
abstraction”, with OET focusing mainly on “Analogy/abstraction”. OET is a product of the Knowledge
Infrastructure project and is composed of an ontology and the Keyword Explorer interface.
Figure 1 — Simplified flow chart of a biomimetic development process
(adapted from ISO 18458)
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4.2 Related work in the framework of the design processes of BID
There are quite a few methods/tools for supporting innovative design based on biomimetics or bio-
inspired design (BID). They are classified according to two dimensions (see Table 1). The rows are
the phase of the targeted design process (“Analysis” and “Analogy/abstraction”) based on Wanieck’s
[1]
analysis . The columns are based on a qualitative assessment of the degree of guidance the tools
provide practitioners searching for BID solutions (low to high).
Table 1 — Classification of existing methods/tools
High guidance Low guidance
SAPPhIRE
BioTRIZ
Analysis phase DANE
E2B thesaurus
Biomimetic Ontology
SAPPhIRE
DANE iSEE
Analogy/abstraction
phases
Biomimetic Ontology AskNature
OET
[2]
Chakrabarti developed the SAPPhIRE system for automated analogical search of relevant ideas based
on a generic model for representing causality in natural and artificial systems. It helps develop novel,
analogical ideas for solving design problems using inspiration from both natural and technological
domains.
[3][4]
Goel’s system, named DANE (Design Analogy to Nature Engine) , facilitates analogical reasoning to
help practitioners find and understand biological systems relevant to the design context. Practitioners
can build structured representations of biological and technical systems using the Structure-Behavior-
Function (SBF) ontology to model the systems, extract relevant principles, and build a library for the BID
community. The current focus is on automatically extracting the SBF models directly from biological
[5]
texts . The plan is to incorporate augmented intelligence such that DANE becomes a collaboration
partner in the design process.
[6]
Hollermann developed a biomimetic methodology and tool for supporting creativity in product
innovation based on a general concept including a detailed guideline. It supports the identification of
biological models through the iSEE (iterative semantic examination) process.
[7][8][9]
BioTRIZ is an extension of the TRIZ methodology to better support BID. TRIZ was developed
[10]
through an analysis of successful patents and has shown that it can solve problems in a wide range
of topics, ranging from engineering through architecture to management. BioTRIZ consists of a set of
tools, rules, and techniques to help identify possible solutions in biology.
[11]
Vincent is developing a Biomimetics Ontology focusing on the trade-off as a central concept to bridge
the gap between biology and problem-solving in technology. The concept of the trade-off is defined
using the method of the TRIZ Contradiction Matrix. The ontology can identify trade-offs, suggest
biological analogues, and uncover principles for BID.
[12]
AskNature is a comprehensive database of several kinds of useful BID resources. It consists of four
primary types of interconnected information: (1) biological strategies, (2) inspired ideas related to BID,
(3) resources for learning/teaching BID, and (4) collections of themed clusters curated by the users.
Although it provides useful information for practitioners, these types of databases require constant
updating as new information becomes available and rely on the practitioner to drive the search process.
[13] [14] [15][16]
The work of Nagel and Cheong building on research by Stone and Shu's labs helps fill the
[17]
gap between functional terms used in engineering and biology. They employ the functional basis
which is widely accepted as a standardized representation of engineering product functionality in
[18]
USA. They built the Engineering-to-Biology (E2B) thesaurus for translating engineering function
and flow terms into meaningful biological functional terms to help address the terminology issues
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engineers face when working between the two domains, and assist in problem definition, inspiration
searching, biological functional modelling, and analogy formulation. Their work is different from OET
in two respects. Their thesaurus only deals with function/flow terms, whereas OET covers organisms,
features, and living environments, as well as functions. Moreover, they mainly provide biological
functional terms for information retrieval, while the Keyword Explorer provides candidate organisms
as possible solutions based on OET’s association-based inference.
OET provides practitioners with a simple way of identifying potentially relevant organisms for future
research, in comparison to SAPPhIRE and DANE that are based on models, and the Biomimetic Ontology
that is based on trade-offs.
4.3 Positioning of OET in the context of the BID design process
As biomimetics is a cross-disciplinary endeavour, it is crucial for all relevant disciplines to exchange
their accumulated knowledge and ideas. Each discipline has developed its unique set of concepts
and words that often have different meanings and usages. Without a proper translation mechanism,
communication among the disciplines will be hindered.
[1]
At a high level, practitioners of biomimetics doing “technology pull” often follow four steps :
1. Analysis: gain a deep understanding of the technical problem to be solved or situation to be
improved, which may involve functional analysis and abstraction as well as reformulating the
challenge to simplify the second step;
2. Analogy: identify relevant biological species, phenomena, or models that share similarities to the
technical challenge but suggest new ideas;
3. Abstraction: examine the relationship of the biological models to various aspects of the original
technology challenge to identify underlying strategies and principles which will aid in transfer of
the ideas;
4. Application: find an implementable solution to the original challenge.
In the case of “biology push”, the analysis step relates to the biological phenomenon or model. The
analogy step involves finding technical situations that share common drivers, while the abstraction
and application steps are similar to those in “technology pull.”
Access to biological databases is crucial, but these databases are organized using biological terms.
Therefore, translation of the technical terms into biological terms is required.
OET has been primarily developed for practitioners but could also be valuable to biologists.
5 How Keyword Explorer works
5.1 General
Keyword Explorer is an application program running on OET that explores concepts defined in OET
where each concept is used as a keyword to retrieve relevant information. The exploration mechanism
is association-based. See Figure 2 where rectangles denote concepts and links denote associations
(relations) defined in OET. When a user inputs "antifouling", Keyword Explore traverses the concepts in
OET like a person does association-based inference.
EXAMPLE "When it gets dirty, if it automatically cleans itself (self-cleaning), it would work for antifouling,
to self-clean, washing out the dirt would be also effective. To do so, covering its surface with water would work
well. Oh, it reminds me of hydrophilic property which in turn reminds me of water repellence. Rose petals are
well-known as their hydrophilic property which is opposite to water repellence. Rose petals realize those two
properties by their double structure of microscopic protrusions covered with a hydrophobic waxy material which
is also found on lotus leaves. To find something antifouling, would be effective to investigate organisms living in
the mud. Ummm, earthworms live in the mud and their surface looks clean".
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In such a way, users would be able to reach rose petal, lotus leaf, earthworm, and other organisms,
some of which could be useful. They can find papers about them and would be able to find more detailed
information as well as experts on the related topics.
To enable such association-based inference, different types of concepts are required. Antifouling and
self-cleaning are functions (the role played within the larger system); washing out and covering with
water are behaviours (how a function is manifested); water repellent and hydrophilic are properties
(inherent characteristics of the organism); rose petal, lotus leaf and earthworms are organisms; and
living in the mud is related to living environment. Furthermore, self-cleaning is a sub-function of the
antifouling function, while washing out and covering with water enable self-cleaning, which suggests
function-decomposition plays an important role in facilitating associative inference.
Figure 2 — Association-based inference by Keyword explorer
Ontologies play a key part in the knowledge infrastructure — Figure 3 shows how the ontologies in OET
fill the gap between engineering and biology. The two vertical ovals represent conventional thesauri in
the engineering and biology domains. Although each thesaurus works well in the respective domain,
they would be inadequate for biomimetics which spans these two domains. Although biomimetic
thesauri have been created, they have limitations as described in 6.4. Ontologies can enhance the utility
of conventional thesauri by bridging conceptual gaps between engineering and biology domains.
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Figure 3 — Bridging the gap by ontologies
5.2 A motivating example
Imagine Jane, an engineer working in a house construction company, who is asked by her boss to
[18]
invent a new eco-friendly idea for floors and walls. She finds the E2B thesaurus developed based
[19]
on the Functional Basis . Her background is engineering, so she searches for keywords such as “easy
to clean” or “stain-resistant”. She tries to translate these two engineering terms into biological terms
using E2B but fails because she is unfamiliar with the Functional Basis and the importance of creating
a functional model that would allow her to access the right engineering terms in E2B. As an alternative,
Jane could use the following two-step process.
The first step: Jane needs to find appropriate keywords that best capture her intention. At first,
she only knows “stain-resistant” or “easy to clean”, but she might find “antifouling” through internet
searches. When she types in “antifouling”, biomimetics databases will return relevant information if
they contain information about biological organisms that have antifouling properties. However, not all
information potentially useful for her is indexed by “antifouling” and is therefore inaccessible to her
because links to this information are missing.
The second step (keyword exploration): Keyword Explorer can help Jane solve the “missing links”
issue (6.4) by enabling her to explore the keyword space spanned by the OET. Keyword Explorer and
OET use association-based inference to identify functions and features related to “antifouling” (at the
centre of Figure 4), allowing her to find organisms such as sandfish as well as lotus and snail which
are not directly indexed by "antifouling". Jane can now conduct detailed searches using a broad range
of related keywords to find relevant information from biomimetics databases such as Google, Google
Scholar, and AskNature, as shown in the left pane in the figure.
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Figure 4 — Example of “antifouling” leading to sandfish via inferred keywords
The arrows in Figure 4 represent the direction in which information is linked in the ontology, rather
than the progress of association-based inference.
6 Ontology-Enhanced Thesaurus
6.1 General
In this clause, the Ontology-Enhanced Thesaurus (OET) in the context of the domain of biomimetics is
described. As described in 5.2, OET and "thesaurus" are well-modularized or loosely coupled so that
they can work independently. A thesaurus solves terminological problems while OET solves so-called
“missing link” problems described in 6.4. OET does not assume any particular thesaurus.
6.2 Characteristics of biomimetics databases
A biomimetics database is not an ordinary database which stores information about a single domain.
It is an interdisciplinary database comprising not only papers on biomimetics but also all kinds of
biological data, such as inventories, electron microscopy images, and experimental data.
Differences between domains cause a couple of problems including terminology differences.
Practitioners doing biomimetics may not be familiar with biological terms, and hence they need
substantial assistance in finding useful information about organisms that can be a source of creative
ideas for developing innovative products and services. The same challenge applies to biologists who are
not familiar with technical terms.
6.3 Basic design of a biomimetics database retrieval scheme
Figure 5 shows an overview of biomimetics databases with an advanced retrieval system which
incorporates the above-mentioned characteristics. Various databases and other online resources can
be integrated by adding metadata about them to the biomimetics ontology.
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Figure 5 — Overview of the future biomimetics databases
In Figure 5 “Retrieval system” corresponds to the Keyword Explorer, while “Biomimetics Ontology”
corresponds to the OET.
In general, users retrieving information from databases need to use appropriate keywords. Some
retrieval systems provide users with a thesaurus, a systematic collection of terms with their
relationships, to help the users with that task. A thesaurus is built using documents from the target
domain and its quality is dependent heavily on breadth and quality of these documents.
...
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