=Paper=
{{Paper
|id=Vol-1302/paper7
|storemode=property
|title=An Ontology Design Pattern for Material Transformation
|pdfUrl=https://ceur-ws.org/Vol-1302/paper7.pdf
|volume=Vol-1302
|dblpUrl=https://dblp.org/rec/conf/semweb/VardemanKCJFHBTBH14
}}
==An Ontology Design Pattern for Material Transformation==
https://ceur-ws.org/Vol-1302/paper7.pdf
An Ontology Design Pattern for Material
Transformation
Charles Vardeman1 , Adila A. Krisnadhi2,3 , Michelle Cheatham2 , Krzysztof
Janowicz4 , Holly Ferguson1 , Pascal Hitzler2 , Aimee P. C. Buccellato1 ,
Krishnaprasad Thirunarayan2 , Gary Berg-Cross5 , and Torsten Hahmann6
1
University of Notre Dame,
2
Wright State University,
3
University of Indonesia
4
University of California, Santa Barbara
5
Spatial Ontology Community of Practice (SOCOP), USA
6
University of Maine
Abstract. In this work we discuss an ontology design pattern for mate-
rial transformations. It models the relation between products, resources,
and catalysts in the transformation process. Our axiomatization goes
beyond a mere surface semantics. While we focus on the construction
domain, the pattern can also be applied to chemistry and other domains.
1 Introduction & Motivation
According to the United Nations, the construction industry and related support
industries are leading consumers of natural resources. Consumption of these
natural resources result in the emission of energy, and thus carbon and other
greenhouse gases, which are then“embodied” in the consumption process. Ef-
forts have been made to quantify these emissions through measures of embodied
energy, carbon and water but are lacking due to poor quality of data sources,
lack of understanding of uncertainty in the data, lack of geospatial attributes
necessary for proper calculation of embodied properties, understanding regional
and international variation in data, incompleteness of secondary data sources
and variation in manufacturing technology that lead to significant variation cal-
culated values [2]. One methodology for quantification of embodied energy is
through input-output life cycle analysis utilizing process data that compile a
life cycle inventory of a construction product. By analyzing a “cradle to grave”
path of individual building components, the embodied energy sequestered in all
building materials during all processes of construction, in on-site construction
and final demolition and disposal of a buildings constituent components gives a
measure of total embodied energy for a given structure. Sources of embodied en-
ergy include the amount of the energy consumed in construction, prefabrication,
assembly, transportation of materials to a building structure, initial manufactur-
ing building materials, in renovation and refurbishment of the structure through
it’s lifetime [1]. The Green Scale Project1 is studying the feasibility of creating a
1
http://www.greenscale.org
geospatial-temporal knowledge base (KB) which facilitates mapping of national
energy and fuel production to individual construction site localities and con-
struction material manufacture localities as linked open data. Such a knowledge
base would facilitate the calculation of embodied energy for a given construc-
tion component as a query of the embodied energy required for manufacture
and transportation of it’s constituent parts. This KB will use ontology design
patterns to formally describe the transportation and transformation processes.
Transportation of a manufacturing component from location to location and
the energies associated with that transportation can be modeled via the Seman-
tic Trajectory pattern (STODP) [3]. The remaining contribution to the total
embodied energy is the energy required for transformation or assembly of one
or more components into the desired manufactured artifact.
In this work we discuss the development of a Material Transformation pat-
tern2 to contextualize this transformation process from raw components and the
required equipment to a final manufactured artifact. Chaining this pattern with
STODP will facilitate understanding of a complete manufacturing process from
raw material extraction to assembly of all components needed for that product.
The presented work was done in two 2-day sessions involving domain experts
from architecture, computational chemistry, and geography, as well as ontology
engineers at GeoVoCampDC20133 and GeoVoCampWI20144 . We present a full
axiomatization that goes beyond mere surface semantics [4] (e.g., a simple type
hierarchy). During the development, several competency questions that a domain
expert may ask were discussed. These include:
– “What material resources were required to produce a product?”
– “Where did the transformation take place?”
– “What was the time necessary for the transformation?”
– “What materials or conditions were necessary for the transformation to occur?”
2 Material Transformation Pattern
The Material Transformation pattern is visualized in Fig. 1, including the ex-
tension with entities relevant for representing energy information, which are
green-colored and use dashed line. For formalization, we use the Description
Logic (DL) notation, which can easily be encoded using syntax of the OWL
2. The core part of the pattern is intended to describe change(s) that occur
between the input material of the transformation and its output. In this core
part, the MaterialTransformation class represents concrete instances of ma-
terial transformation. We distinguish inputs of a material transformation into
Resource, which represents types of material that may undergo a change (into
a different type of material) in the transformation, and Catalyst, which rep-
resents types of material needed by the transformation, but remain unchanged
by it. A MaterialTransformation has some Resource as input (1), and some
Product, which is also some type of material, as output (2). Axiom (5) asserts
2
http://ontologydesignpatterns.org/wiki/Submissions:Material Transformation
3
http://vocamp.org/wiki/GeoVoCampDC2013
4
http://www.ssec.wisc.edu/meetings/geosp sem/
Fig. 1. Material Transformation Pattern with Energy Information
that every Resource, Catalyst and Product is some MaterialType, while (6)
and distinguishes Resource from Catalyst. Axiom (3) and (4) assert that a
MaterialTransformation occurs in a spatial Neighborhood5 and a time inter-
val, modeled using the Interval class from the W3C’s OWL Time ontology6 .
MaterialTransformation v ∃hasInput.Resource (1)
MaterialTransformation v ∃hasOutput.Product (2)
MaterialTransformation v ∃occursInNeighborhood.Neighborhood (3)
MaterialTransformation v ∃occursAtTimeInterval.time:Interval (4)
Resource t Catalyst t Product v MaterialType (5)
Resource u Catalyst v ⊥ (6)
We express changes occurring within a material transformation, using first-order
logic, that it has an input that is not part of the output (7); and an output that
is not part of the input, in a formula analogous to (7).
∀x(MaterialTransformation(x) → ∃y(hasInput(x, y) ∧ ¬hasOutput(x, y))) (7)
These formulas, however, cannot be expressed in the OWL framework, but
there are extensions of DL that can express them. For example, using boolean
constructors on properties [5], axiom (7) is expressed in DL as:
MaterialTransformation v ∃(hasInput u ¬hasOutput).>
Meanwhile, for the remaining properties of the core part of the pattern, we
assert the guarded domain and range restrictionsas exemplified for the hasInput
property in (8) and (9) below. Such guarded restrictions are preferable over the
unguarded versions (i.e., of the form dom(P ) v A and range(P ) v B) as they
introduce weaker ontological commitments and thus foster reuse.
∃hasInput.Resource v MaterialTransformation (8)
MaterialTransformation v ∀hasInput.Resource (9)
5
Neighborhood provides a toplogical definition for specifing nearness. This could be
specified in different ways such as using positional coordinates, a bounded area on
a map, or a named region such as a place, city or factory.
6
http://www.w3.org/TR/owl-time/
For the scenario where we need to calculate the embodied energy in the
output of a material transformation, we can extend the pattern with additional
energy information as depicted in Fig. 1. In the axiomatization, we then assert
that a MaterialTransformation needs some Energy (10), while each material
type has some embodied energy (11). Energy itself is abstracted as an instance
of the Energy class, which has some energy value and unit.
MaterialTransformation v ∃needsEnergy.Energy (10)
MaterialType v ∃hasEmbodiedEnergy.Energy (11)
Energy v ∃hasEnergyValue.EnergyValue (12)
EnergyValue v ∃hasEnergyUnit.EnergyUnit (13)
u ∃asNumeric.xsd:double
EnergyUnit v ∃asLiteral.xsd:string (14)
Embodied energy in the output as a result of a material transformation can be
calculated by aggregating embodied energy of the input and catalyst, together
with energy requirement of the material transformation itself. This cannot be
done within OWL, but is relatively straightforward to implement in the appli-
cation as all the necessary information are easily retrievable from the populated
pattern. Furthermore, if the application allows updates on the data populating
the pattern, we can chain two instantiations of this pattern and include STODP.
3 Conclusion and Future Work
Although it is beyond the scope of the present work, the Material Transformation
pattern should be sufficiently generic to describe other types of transformation
processes ranging from chemical reactions to creation-annihilation events in high
energy physics. We believe the pattern to be of general use to broader product
life cycle inventories outside the construction domain.
Acknowledgements. We are grateful for the inputs from Lamar Henderson, Deb-
orah MachPherson, Laura Bartolo, and Damian Gessler to improve the pattern.
Vardeman, Buccellato and Ferguson would like to acknowledge funding from
the University of Notre Dame’s Center for Sustainable Energy, School of Ar-
chitecture, College of Arts and Letters and Center for Research Computing in
support of this work. Gary Berg-Cross acknowledges funding from the NSF grant
0955816, INTEROP-Spatial Ontology Community of Practice. Vardeman would
like to acknowledge funding from NSF grant PHY-1247316 “DASPOS: Data and
Software Preservation for Open Science.” Adila Krisnadhi, Michelle Cheatham,
and Pascal Hitzler acknowledge support by the National Science Foundation un-
der award 1017225 “III: Small: TROn – Tractable Reasoning with Ontologies.”
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