International Conference on Product Lifecycle Management
1
Development of an Ontology for Bio-Inspired Design
using Description Logics
Sungshik Yim†, Jamal O. Wilson*, David W.
Rosen*
*
The George W. Woodruff School of Mechanical Engineering
Georgia Institute of Technology, Atlanta, GA 30332-0405
†
3D Systems, Inc., Rock Hill, SC
404-894-9668
david.rosen@me.gatech.edu
Abstract:
In this paper, we present an ontology used to capture, retrieve, and reuse novel bioinspired design solutions and their associated physical architectures, behaviors, functions, and
strategies. By adapting these biological design solutions, nature’s technology can be leveraged in
the design of novel engineering systems. Primarily due to the lack of cross-domain knowledge and
a differing functional language, identification of relevant biological solutions by design engineers is
a difficult challenge. This challenge can be overcome through using a case-based approach,
whereby biological solutions are stored in a biological repository and are related to example
engineering solutions. Using the proposed ontology, the designer can store and efficiently retrieve
previously stored biological and/or engineering solutions, based on desired functions, architectures,
or behaviors. Therefore, the biological repository enables design knowledge capture and reuse in a
distributed environment for biomimetics.
The proposed ontology is encoded using a Description Logic (DL) known as ALCHI. Description
logics are a subset of first-order logic that have been used for information modeling in several
application areas, including engineering information management. They are used typically to
construct classification hierarchies that can be efficiently searched. We demonstrate the capability
of our DL model by: 1) demonstrating that the classification hierarchies that are computed match
our biomimetic ontology and 2) demonstrating the retrieval capability using a prototype
implementation of our biomimetic ontology.
Keyword : Biomimetics, Design Repository, Description Logics
1
Introduction
Bio-inspired design can be defined as the transfer of natural technologies to other
domains, such as engineering, materials science, design, etc. One of the key difficulties
in bio-inspired design is identification of relevant biological design solutions. In an effort
to systematize this process, we propose a case-based reasoning (CBR) approach to bioinspired design where a Strategy Repository is used to capture biological and engineering
design solutions and allow retrieval of these solutions in the conceptual design process.
This approach begins in the Conceptual Design phase of the systematic design process
(Pahl and Beitz, 1996), where the designer has identified a design function of interest. In
order to fulfill this function, the designer generates multiple functional solutions, or
design strategies used to fulfill the function of interest. The strategy repository is used to
store and efficiently retrieve previously stored biological and/or engineering solutions
Copyright © 200x Inderscience Enterprises Ltd.
Sungshik Yim, Jamal O. Wilson, David W. Rosen
and their associated design strategies. These strategies are then used as stimuli in concept
generation, aiding the designer in expanding and exploring more of his/her design space.
Several researchers have attempted to systematize the bio-inspired design process.
Vincent and coauthors (Vincent and Mann, 2002; Vincent, et al., 2006) seek to integrate
knowledge from nature into TRIZ, a systematic method for inventive problem solving
developed by Russian researchers. Researchers from the University of Toronto (Vakili
and Shu, 2001; Chiu and Shu, 2004; Chiu and Shu, 2005) proposed using a functional
keyword search through biological literature to identify potential analogies. Researchers
from the Rocky Mountain Institute/Biomimicry Guild and the University of Maryland
(Bruck, et al., 2006) have developed searchable databases of biological systems. Other
researchers (Chakrabarti, et al., 2005) have developed a searchable database containing
both natural and artificial systems.
Although the current approaches are useful in storing and providing access to
biological information in design, the generic keyword-based retrieval process often
suffers by either providing too many and/or irrelevant design results (Li, et al., 2008). By
structuring biological information using ontologies, we can more accurately and
efficiently relate design functions to biological systems and their respective strategies.
With their system, Li and co-authors (Li, et al., 2008) have shown these types of ontology
based searches outperform those of generic keyword searches. The purpose of this paper
is to present a novel ontology used to support bio-inspired concept generation during
conceptual design. Our ontology is constructed from separate function and strategy
hierarchies, composed from common noun and verb taxonomies that become integrated
into a single repository through the computation of subsumption relationships among the
various concepts. Description logics (DL) are proposed to correctly and consistently
represent the ontology. The following sections present information modeling, DL
encoding, and taxonomy computations of functions, strategies, and design solutions.
2
Information Modeling
Three taxonomies including function, strategy, and design solution are identified in this
research to properly classify design solutions. We define function as the purpose of a
specific system in the context of a larger system and strategy as the ‘means’ by which this
function is accomplished. Design solutions are defined as the biological or artificial
systems of interest. The following sections discuss the representation and classification
of function, strategy, and design solutions.
2.1 Engineering Functions
Function representation in this research consists of flow (input and output), verb, and
noun. Figure 1 presents the structure of a function and a corresponding example.
Figure 1
Function structure and corresponding example
Structure
Input
Verb Noun
Output
Signal
Example
Control Stiffness
Mechanical Energy
Development of an Ontology for Bio-Inspired Design using Description Logics
The example function in Figure 1 can be described as a system that controls stiffness, and
inputs signal and outputs mechanical energy. To systematically describe functions and
identify their relations, an ontology is realized for function descriptions. In order to
standardize functional representations, Stone and Wood (Stone and Wood, 2000)
proposed a Functional Basis, creating a standard taxonomy for functional verbs and
functional nouns (matter, energy, and signal). In this research, the functional basis is
used as a basis to realize our ontology. Due to the limited space, only samples of our
ontology are shown in Table 1 and 2.
Table 1
Sample Noun (signal, matter, and energy) Taxonomy, adapted from Stone and Wood
Class
Basic
Matter
Liquid
Solid
Measurable Quantity
Table 2
Energy
Sub-basic
Rigid Body
Flexible Body
Acoustic Energy
Mechanical Energy
Sample Verb Taxonomy (Stone and Wood, 2000)
Class
Basic
Synonyms
Branch
Separate
Switch, Divide, Release
Control Magnitude
Regulate
Control, Prevent, Enable
In Table 1, nouns are classified by class, basic, and sub-basic, with degree of
specification increasing from left to right. The noun taxonomy is constructed based on
their hierarchical relations. For example, acoustic energy is a type of energy and energy
is a type of measurable quantity. In Table 2, verbs are classified based on classes and
their sub-classes. The nouns are used to represent concepts and verbs are used to
describe the relations between these nouns.
2.2 Strategies
In this research, three types of strategies are defined, including abstract, biological, and
engineering. Table 3 presents an example. All three strategies describe function and the
corresponding behavior that satisfies the function. In our definition of strategy, function
is described first (ex: control stress transfer). Then, the corresponding behavior is
described following the word “by” (ex: by regulating component bonding in matter). Bio
and engineering strategies describe behaviors using vocabularies that are specific to the
bio and engineering domains, respectively.
2.3 Hierarchical Relationship
In this research, two types of hierarchies among functions, strategies, and design
solutions are identified. In the case of functions, hierarchical relations are determined by
their functional requirements (input, output, verb, and noun). Figure 2 illustrates two
types of hierarchies using example functions.
Sungshik Yim, Jamal O. Wilson, David W. Rosen
Table 3
Strategy Example
Strategy
Example
Abstract
Control stress transfer by regulating component bonding in matter
Bio
Control stress transfer by regulating fibril bonding in dermis of sea
cucumber
Engineering
Control stress transfer by regulating ferroelectric particle bonding
in Electrorheological fluid
Figure 2
Types of hierarchy
Type 1: Specialize I/O
f1
Signal
f2 Signal
Rigid body
Control Stress
Transfer
Control Stress
Transfer
Rigid body and
flexible body
f3
Signal
f4
Signal
f1 ⊃ f2
Type 2: Specialize Function
Control Measurable
Quantity
Control Stress Transfer
Rigid
body
Rigid
body
f3 ⊃ f4
A type 1 hierarchy is formed by modification of expressions without using taxonomic
structure of vocabularies in Tables 1 and 2; instead the inputs and/or outputs of the
functions are specialized. For example, the type 1 hierarchy in Figure 2 is formed by
addition of an output expression (flexible body is added to f1 to form f2, f1⊃ f2). A type
2 hierarchy is formed by the taxonomic structure of vocabularies in Table 1 and 2, where
the function statement itself is specialized. For example, the type 2 hierarchy in Figure 2
is formed by utilization of a more specific noun (stress transfer) in function f4. Through
those types of hierarchies, subsumption relations are identified as f1 ⊃ f2 and f3 ⊃ f4. In
constructing the function taxonomy, those two types and their combinations are used.
Based on the descriptions in Table 3, strategies can be structured into a hierarchy such
as Abstract ⊃ Bio, Abstract ⊃ Engineering and Bio ≠ Engineering through the
hierarchies discussed in Figure 2. In Table 3, the type 2 hierarchy is presented in the
utilization of nouns including matter (abstract), dermis of the sea cucumber (bio), and
electrorheological fluid (engineering). The strategies are also presented by the verb and
noun taxonomies in Tables 1 and 2.
2.4 Relations between functions and strategies
In this research, hierarchical relations among functions are identified to correctly relate
the given functions to relevant strategies. Tables 4 and 5 and Figure 5 present an
example of relating function to relevant strategies through the function taxonomy. Table
4 presents details of various functions and strategies. Figure 4 presents the function and
strategy taxonomies. Table 5 presents functions and their associated strategies.
Functions and strategies are associated through design solutions. For instance, the design
solution ‘Freezer’ relates function ‘f2.1’ to strategy ‘Freezer’. Functions and strategies in
Table 4 are constructed such that there are taxonomic structures among them, which are
illustrated in Figure 3. Strategies for more specific functions should satisfy functional
requirements for more generic functions. For example, strategy ER not only satisfies f3.1,
but also satisfies f1, f2, and f3. This is because ER can generate the rigid state of
electrorheological fluid, which is a type of rigid body and rigid body is a type of solid.
Development of an Ontology for Bio-Inspired Design using Description Logics
Similarly, strategies associated with function f2.1 should also be associated with
functions f1 and f2.
Table 4
Examples of Functions and Strategies descriptions
Function Description
Strategy
Description
f1
Input: Signal, Output: Solid
abstract 1
control stress transfer
f2
Input: Signal, Output: Rigid Body
abstract 2
transfer energy
f3
Input: Signal, Output: Rigid Body
and Flexible Body
Freezer
Transfer thermal energy from
liquid
f2.1
Input: Signal, Output: Rigid Body
Verb: Solidify, Noun: Liquid
Stereolithography Transfer radiative energy to
liquid that reacts to laser light
(SLA)
Control stress transfer by
Input: Signal, Output: Rigid Body Sea Cucumber
regulating fibril bonding in
and Flexible Body, Verb: Control,
dermis of sea cucumber
Noun: Stress Transfer
f3.1
Electrorheological Control stress transfer by
regulating
ferroelectric
Fluid (ER)
particle bonding in ER fluid
Control stress transfer by
Shape Memory
controlling thermal energy in
Polymer (SMP)
polymer
Figure 3
Example of function and strategy taxonomy
Function Taxonomy
Strategy Taxonomy
f1
abstract 1
abstract 2
f2
ER
f3
f2.1
f3.1
Table 5
SLA
freezer
Sea
Cucumber
SMP
Functions and their associated strategies
Function
Associated strategies
f1
abstract 1, abstract 2, freezer, SLA, Sea Cucumber, SMP, ER
f2
abstract 1, abstract 2, freezer, SLA, Sea Cucumber, SMP, ER
f3
abstract 1, Sea Cucumber, SMP, ER
f2.1
abstract 2, freezer, SLA
f3.1
abstract 1, Sea Cucumber, SMP, ER
Therefore, the subsuming function is associated with all the strategies that are associated
with subsumed functions. However, not all the associated strategies of a subsuming
function can be associated with subsumed functions. For instance, Freezer strategy can
be applicable to f3 and f3.1, however SLA strategy cannot be applicable to f3 and f3.1
Sungshik Yim, Jamal O. Wilson, David W. Rosen
because the process is irreversible (Jacobs, 1992). This is because the strategies of
subsuming functions address only part of the functional requirements of subsumed
functions. In short, strategies that satisfy more specific functions satisfy more generic
functions if and only if there are subsumption relations between specific and generic
functions.
2.5 Strategy Repository Structure
In this research, the strategy repository is structured by three taxonomies including
function, strategy, and design solutions. Figure 4 presents an example repository
structure.
Figure 4
Example Repository Structure
Function Taxonomy
Strategy Taxonomy
f1
abstract 1
abstract 2
f2
f3
SLA
freezer
f2.1
SMP
f3.1
Design solution:ds1,
function: f2.1,
strategy: freezer
ER
Sea
Cucumber
Design
solution:ds2,
function: f2.1,
strategy: SLA
Design solution:ds3,
function: f3.1,
strategy: Sea
Cucumber, SMP
Design solution:ds4,
function: f3.1, strategy:
Sea Cucumber, ER
As shown in Figure 4, the design solutions are represented by their functions and
strategies. Therefore, the design solutions maintain hierarchical relations with both
function and strategy taxonomies. Then, such hierarchical relationships can be used to
relate the given function or strategy to relevant design solutions. For example, if the
query matches f3, then ds3 and ds4 will be identified as relevant problems. If we assume
that ds3 and ds4 do not exist, then ds1 and ds2 can be further identified as relevant
problems. In this case, the design solutions only satisfy part of the functional
requirements of f3. The following sections discuss description logics implementations of
our ontology and taxonomy.
3
Description Logic Modeling
Description logics (Baader, Calvanese et al., 2002) are formalisms used to represent
domain-specific concepts and relationships between them. The basic family of attribute
language (AL) is defined as the grammar.
C, D Æ A
⊥
¬
(atomic concept)
(bottom
concept)
(top concept)
(atomic
negation)
∀ R.
∃
R.
(concept intersection)
(value restriction)
(limited existential quantification)
Development of an Ontology for Bio-Inspired Design using Description Logics
Atomic concepts denoted by C and D, and roles denoted by R are used along with the
listed constructors to build descriptions of concepts and relationships between them. The
expressive power of the language can be improved by augmenting it with any
combination of several constructors, including: U Æ C D (union of atomic concepts),
E Æ ∃ R.C (full existential quantification), N Æ ≥nR, ≤nR (number restriction), C Æ
¬ C (negation of arbitrary concepts), as well as others. This results in a description
logic named by a string of the form AL[U][E][N][C], such as ALE, ALUEC, or ALUENC.
DLs provide a formal syntax and semantics for describing knowledge within a domain
in terms of concepts and properties that specific individuals must satisfy. Standard
inferences for description logic concepts include satisfiability and subsumption.
Satisfiability refers to the logical soundness of a defined concept with respect to a
terminology. Subsumption tests whether a concept or role is a more general expression of
another concept or role. A concept C is subsumed by a concept D, denoted as C ⊂ D, if
every member of the set described by concept C must also be a member of the set
described by concept D.
4
Ontology Implementation
The ontology presented in Tables 1 and 2 are encoded by DL (ALCHI). The concepts in
Table 1 and verbs in Table 2 are represented as classes and roles respectively. Functions,
strategies, and design solutions are represented using those classes and roles. Table 6
presents DL encoding of functions, strategies and design solutions in mathematical
notations.
Table 6
Example of function, strategy, and design solutions DL encoding
Function, Strategy, Design
solution
Description Logics Encoding
f3.1
(∃controls.StressTransfer)
(∃hasInput.Signal)
(∃hasOutput.RigidBody)
(∃hasOutput.FlexibleBody)
ER
∃hasBehavioralModel.(∃controls.(
isControlledBy.ElectricField)
isIn.ElectrorheologicalFluid)))
Design solution ER
f3.1
StressTransfer
(∃
(∃
ER
As shown in Table 6, additional vocabularies (roles) such as isControlledBy,
hasBehavioralModel, hasInput, etc. are introduced to more accurately represent function,
strategy, and design solutions. The negation operator (┐) is introduced to represent
opposite concepts such as rigidity and flexibility. The role hierarchy is introduced to
represent the verb taxonomy (Table 2). Inverse role is introduced to represent inverse
relations of verbs such as controls and isControlledBy. Therefore, the expressivity of the
utilized DL is measured to be ALCHI.
To demonstrate DL’s consistent and correct subsumption computation capability,
classes and verbs in Tables 1, 2, and 4 are represented in DL and the repository structure
in Figure 4 is computed using subsumption in DL. Figure 5 presents the taxonomy
computation result of the repository using DL subsumption. The ontology representation
Sungshik Yim, Jamal O. Wilson, David W. Rosen
is created using ontology editor Protégé and subsumption is computed using DL reasoner
RacerPro (Protege, 2005; RacerPro, 2005).
Figure 5
Taxonomy Computation Results using Description Logics (ALCHI)
The taxonomy in Figure 5 exactly matches the repository structure presented in Figure 4.
The example in Figure 5 demonstrates feasibility of utilization of DL in structuring the
repository consistently and correctly.
5
Query Example
One of the primary goals of the designer in Conceptual Design is to generate a wide
variety of novel design ideas to fulfill a specific function of the artifact being designed.
The use of external stimuli in the form of biological and engineering strategies in ideation
can aid in generating these ideas. The strategy repository developed in this research can
be accessed by the designer to retrieve biological and engineering solutions and their
associated design strategies. In this example, we demonstrate how subsumption in DL
can be used to identify relevant design solutions and strategies in the repository. In this
research, a query can be a function, a strategy, or a combination of them. To demonstrate
the query example, several queries and their relevant design solutions are realized in
Table 7. In this example, Figure 5 is used as the repository. Then the query performance
is demonstrated by computing the correct repository taxonomy that includes nodes for
queries.
Results for the queries in Table 7 are determined by computing the subsumption
relations among the queries and the repository. Relevant design solutions are identified
from the repository in Figure 5 by detecting design solutions that are subsumed by the
given query. Hence, only the directly applicable design solutions for a given query are
identified as relevant design solutions in this example.
Query Q1 specifies input and output to be the same as f3.1 in Figure 6. However, its
verb and noun are different than f3.1. Hence, no design solutions are expected to be
relevant. The query Q2 is a generalized version of Q1 such that the verb and noun are
removed. Therefore, Q2 is the same as f3. Design solutions for SMP and ER are
identified to be relevant. The queries Q3 and Q4 are generalized by removing flexible
body and rigid body from the output, respectively. The query Q3 is the same as f2.
Hence, design solutions for SMP, ER, Freezer, and SLA are identified to be relevant. For
the query Q4, there is no direct subsumee of Q4 except f3. Hence, design solutions for
SMP and ER are identified as relevant. Query Q5 is realized to demonstrate how queries
can be further specialized by including strategies. Query Q5 is the same as Q3 with an
additional abstract strategy, abstract_1. Therefore, design solutions for SMP and ER are
Development of an Ontology for Bio-Inspired Design using Description Logics
identified as relevant. In short, we expect f3 ⊃ Q1, f3.1≠Q1, f3≡Q2, f2≡Q3, Q4 ⊃ f3,
f2≠Q4, f2 ⊃ Q5, and abstract_1⊃ Q5.
Table 7
Example Queries
Query
Descriptions
DL Representation
Relevant Design solutions
Q1
Input: Signal, Output: Rigid
Body and Flexible Body,
Verb: Transfer, Noun:
Mechanical Energy
None
(∃
controls.MechanicalEnergy)
(∃hasInput.Signal)
(∃hasOutput.RigidBody)
(∃
hasOutput.FlexibleBody)
Q2
Input: Signal, Output: Rigid
Body and Flexible Body
(same as f3 in Figure 6)
(∃ Design solutions for SMP,
(∃hasInput.Signal)
ER
hasOutput.RigidBody)
(∃hasOutput.FlexibleBody)
Q3
Input: Signal, Output: Rigid
Body (same as f2 in Figure 6)
(∃hasInput.Signal)
(∃
hasOutput.RigidBody)
Design solutions for SMP,
ER, Freezer, SLA
Q4
Input: Signal, Output:
Flexible Body
(∃hasInput.Signal)
(∃
hasOutput.FlexibleBody)
Design solutions for SMP,
ER
Q5
Same as Q3 with additional
abstract strategy: abstract_1
in Figure 6
(∃hasInput.Signal)
(∃ Design solutions for SMP,
ER
hasOutput.RigidBody)
(∃hasBehavioralModel.
(∃controls.StressTransfer))
The queries in Table 7 were encoded in DL and the repository structure was
computed with these queries. Figure 6 presents the computed taxonomy.
Figure 6
6
Repository with Queries
As shown in Figure 6, the queries are placed at correct positions. In other
words, the taxonomy shows f3 ⊃ Q1, f3.1≠Q1, f3≡Q2, f2≡Q3, Q4 ⊃ f3, f2≠Q4,
Sungshik Yim, Jamal O. Wilson, David W. Rosen
f2 ⊃ Q5, and abstract_1⊃ Q5. Therefore, all the queries are placed at the
correct positions as expected.Closure
In this paper, an ontology to support bio-inspired concept generation during Conceptual
Design was presented. The proposed ontology was developed using taxonomies for
biological and engineering functions and strategies, and subsumption relationships in DL.
Using test queries, DL was shown to correctly and consistently represent the proposed
ontology and retrieve relevant design solutions. For future work, grammar templates and
rules that achieve consistent representations are desired. By completing those templates
and rules, completeness and robustness of the retrieval method can be validated in the
domain of biomimetics. In all, this research will allow design knowledge in bio-inspired
design to be captured and reused for Conceptual Design.
Acknowledgment
The second author acknowledges support from Office of Naval Research and the David
and Lucille Packard Foundation fellowships.
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