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Research Papers

Function Semantic Representation (FSR): A Rule-Based Ontology for Product Functions

[+] Author and Article Information
Seung-Cheol Yang

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109scyang@umich.edu

Lalit Patil

Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801lpatil@illinois.edu

Debasish Dutta

Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801dduttta@illinois.edu

J. Comput. Inf. Sci. Eng 10(3), 031001 (Aug 31, 2010) (8 pages) doi:10.1115/1.3462927 History: Received August 04, 2009; Revised January 31, 2010; Published August 31, 2010; Online August 31, 2010

Defining or understanding a product in terms of its functions facilitates a wide variety of tasks such as design synthesis, modeling, and analysis. However, the lack of a semantically correct formal representation of product functions creates a barrier to their effective capture, exchange, and reuse. This paper presents Function Semantics Representation, a rule-based ontological formalism that is consistent with the Semantic Web standards to capture different components of a product function. In particular, the Semantic Web Rule Language is used to overcome limitations in using the basic Web Ontology Language ontology to explicitly capture advanced semantics essential to completely represent product functions. This enables support for an effective reasoning mechanism to develop and validate the product function (or functional model). We present examples that demonstrate consistency checking and the ability to retrieve functionally similar products from a repository.

Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Representing disjoint flows in RDF/XML. Bold terms highlight important elements in the description.

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Figure 2

Every individual flow is different from every other flow. Some terms are in bold to highlight important elements in the description.

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Figure 3

Partial RDF/XML code for definition of function. Some terms are in bold to highlight important elements in the description.

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Figure 4

Partial taxonomy of the FSR. The small triangles indicate corresponding subconcepts not shown in the figure.

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Figure 5

Definition of hasMagnitude in OWL. Some terms are in bold to highlight important elements in the definition.

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Figure 6

Counting the number of properties using abox:hasNumberOfPropertyValues for the function, import

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Figure 7

Partial RDF/XML syntax showing SWRL rule for the function, import. Corresponding human-readable syntax is shown in Fig. 6

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Figure 8

Using abox:hasClass to determine type of flow in identification of the function, signal

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Figure 9

Using tbox:sameAs to detect the same types of flows in the function, distribute

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Figure 10

Using tbox:differentFrom to detect the different types of flows in the function, convert

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Figure 11

Using SWRLB built-ins to compare values of different properties and detect the function, position

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Figure 12

Partial functional model of a relay used as a part of an ABS brake system. Picture and data taken from Ref. 28.

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Figure 13

Result of consistency checking indicates that the function, actuate of the relay in Fig. 1 is invalid. The Jess rule-engine forces it to be of the two types, convert and actuate simultaneously and the pellet reasoner indicates that the model is inconsistent.

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Figure 14

Partial functional model of a hypothetical water kettle used to demonstrate semantic retrieval of functionally similar products

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Figure 15

SQWRL query to search a product containing convert, change, and store function (so that it is similar to our hypothetical water kettle)

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Figure 16

Functionally similar instances retrieved into the SQWRL query tab in protégé

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