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 [email protected] 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. References Baader, F., D. Calvanese, D. McGuinness, D. Nardi and P. F. Patel-Schneider (2002). The Description Logic Handbook, Cambridge University Press. Bruck, H. A., A. L. Gershon, I. Golden, S. K. Gupta, L. S. G. Jr., E. B. Magrab and B. W. Spranklin (2006). New Educational Tools and Curriculum Enhancements for Motivating Engineering Students to Design and Realize Bio-Inspired Products. Design and Nature III: Comparing Design in Nature with Science and Engineering. W. I. o. T. C. A. 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