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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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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ii © ISO 2020 – All rights reserved
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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

© ISO 2020 – All rights reserved iii
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ISO/TR 23845:2020(E)
Foreword

ISO (the International Organization for Standardization) is a worldwide federation of national standards

bodies (ISO member bodies). The work of preparing International Standards is normally carried out

through ISO technical committees. Each member body interested in a subject for which a technical

committee has been established has the right to be represented on that committee. International

organizations, governmental and non-governmental, in liaison with ISO, also take part in the work.

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

any patent rights identified during the development of the document will be in the Introduction and/or

on the ISO list of patent declarations received (see www .iso .org/ patents).

Any trade name used in this document is information given for the convenience of users and does not

constitute an endorsement.

For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and

expressions related to conformity assessment, as well as information about ISO's adherence to the

World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www .iso .org/

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.
iv © ISO 2020 – All rights reserved
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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.
© ISO 2020 – All rights reserved v
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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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ISO/TR 23845:2020(E)
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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