=Paper=
{{Paper
|id=Vol-1461/WOP2015_pattern_abstract_1
|storemode=property
|title=A Minimal Ontology Pattern for Life Cycle Assessment Data
|pdfUrl=https://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_1.pdf
|volume=Vol-1461
|dblpUrl=https://dblp.org/rec/conf/semweb/JanowiczKHSWRTM15
}}
==A Minimal Ontology Pattern for Life Cycle Assessment Data==
https://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_1.pdf
A Minimal Ontology Pattern for Life Cycle
Assessment Data
Krzysztof Janowicz1 , Adila A. Krisnadhi2 , Yingjie Hu1 , Sangwon Suh1 , Bo
Pedersen Weidema3 , Beatriz Rivela4 , Johan Tivander5 , David E. Meyer6 , Gary
Berg-Cross7 , Pascal Hitzler2 , Wesley Ingwersen6 , Brandon Kuczenski1 , Charles
Vardeman8 , Yiting Ju1 , and Michelle Cheatham2
1
University of California, Santa Barbara, USA
2
Wright State University, USA
3
Aalborg University, Denmark
4
inViable, Spain
5
Chalmers, Sweden
6
US Environmental Protection Agency, USA
7
SOCoP, USA
8
University of Notre Dame, USA
Abstract. Life Cycle Assessment (LCA) studies the environmental im-
pact of products taking into account their entire life-span and produc-
tion chain. This requires gathering data from a variety of heterogeneous
sources into a Life Cycle Inventory (LCI). LCI preparation involves the
integration of observations and engineering models with reference data
and literature results from around the world, from different domains, and
at varying levels of granularity. Existing LCA data formats only address
syntactic interoperability, thereby ignoring semantics. This leads to a va-
riety of challenges, e.g., difficulties in reproducing assessments published
in the literature. In this work, we present an ontology pattern that spec-
ifies key aspects of LCA/LCI data models, namely the notions of flows,
activities, agents, and products, as well as their properties.
1 Introduction and Motivation
Life Cycle Assessment (LCA) is concerned with analyzing the environmental
impact of products, taking into account the complete production chain and the
entire life-span of the product. For instance, assessing the impacts of operating a
solar array goes beyond the pure manufacturing and assembly of the photovoltaic
modules. It also includes transportation emissions, installation emissions, oper-
ation emissions, and the final disposal emissions. Such assessment first requires
the gathering of all relevant data from different sources into a so-called Life Cycle
Inventory (LCI), followed by the actual assessment of the environmental impacts
based on the gathered data, used models, and the literature. Understanding the
complex impact of products is crucial for arriving at, and maintaining, a sustain-
able world where human needs are met without causing harm to the environment
or impacting the ability of future generations to meet their needs. As such, LCA
is a highly interdisciplinary field that requires synthesizing information from a
variety of discipline-specific studies. This interdisciplinarity can be challenging
because the vocabularies often vary between and even within fields of study. This
can create significant problems for data sharing in LCA when data from multiple
sources are translated, merged, and managed as a single life cycle inventory.
Current LCA models do not facilitate semantic interoperability [6]. While
the standardized LCA data formats, e.g., Ecospold and ILCD, do allow for the
exchange of data, such formats alone do not guarantee that a dataset from one
source can be integrated with another source as there is no consensus on the
meaning of central nomenclature for LCA. In other words, the data models
are not backed with explicit conceptual models. Ignoring differences in these
underlying assumptions, i.e., settling with syntactic interoperability alone, is
likely to cause erroneous and unreproducible results.
Many of the significant challenges to data management in LCA practice [11]
and interpretation [10, 12] arise from the lack of protocols and mechanisms to en-
sure mutual comparability and consistency of data sets and results. While Linked
Data and ontologies hold great promise for addressing such issues, semantic tech-
niques have only recently been introduced [2, 9] and thus not yet impacted LCA
practice. Efforts to develop semantically enriched LCA databases [1] and prod-
uct models [15] have had limited scope. Given the high interdisciplinarity and
granularity within LCA, arriving at a shared monolithic domain model seems
like a distant goal. Thus, in this work we introduce a minimal ontology design
pattern[3] for LCA data to act as a common core.
2 Competency Questions
Developing an ontology requires use cases that capture recurring domain or
cross-domain problems. These uses cases can guide the design of the ontology
and help in its evaluation. One approach are co-called competency questions [4].
These are (often informal) queries that the ontology should be able to answer
and that act as requirements for its axiomatization. To give a simple example,
if a typical subject matter expert would make a distinction between two classes
B and C of a common subclass A, then an ontology that does not introduce
B would not be considered as suitable. The following listing shows examples of
competency questions that have been identified by LCA experts.
– Is flow x a reference product (e.g., electricity from a power plant)?
– How long will a flow or activity persist (e.g., the emission of landfill gas)?
– To which compartment does an elementary flow belong to (e.g., soil)?
– What is the location of the agent performing the activity in study x (e.g.,
where is the coal power plant located for which the emissions of electricity
production were assessed in the research study)?
3 The Content Ontology Design Pattern
The proposed ontology design pattern1 is meant to form a common core for the
semantic description of key elements of life cycle inventories. It does neither cover
1
OWL file at: http://descartes-core.org/ontologies/lca/1.0/LCAPattern.owl
the process of carrying out life cycle assessments, e.g., how data is gathered or
how system boundaries are defined, nor does it provide the variety of spatial, tem-
poral, and thematic attributes used to scope inventory items, e.g., to express the
fact that coal extraction may have varying impacts depending on the geographic
region and used technology [14]. Instead, our pattern aims at fostering inter-
operability between existing data models, specifications, and software, with the
intent to act as a joint building block for the rapidly increasing interest in seman-
tics within the broader LCA community. Due to lack of space, we only discuss
a few selected axioms here. An overview of the pattern is depicted in Fig. 1.
Fig. 1: Concept map for the pattern.
Estimating the environmental impact of a certain product requires an un-
derstanding of all impacts accumulated during its creation, lifetime, and de-
commissioning. With respect to the solar panel example introduced before, the
creation of the solar arrays requires multiple activities such as the transportation
of resources, the generation of electric power by a coal power plant necessary to
manufacture certain parts of the panels, or the disposal of polluted sludge accu-
mulated during the production. In other words, the Eco-efficiency of solar panels
depends on the activities involved in all stages of their life-cycle. Each activity
is performed by at least one agent such as an coal power plant that performs the
generation of electricity (Eq. 1). An activity is located via the location of the
agent performing it (Eq. 2). Activities also have a temporal extent and can have
a variety of properties such as the amount of electricity generated, and so forth.
Activity v ∀perf orms− Agent u ∃perf orms− Agent (1)
−
perf orms ◦ hasLocation v hasLocation (2)
Flows are streams of material or energy that can act as the inputs and outputs
of activities. In our running example, coal is an input to the activity of electric
power generation, while CO2 emissions are an output. Typically, emissions are
an undesired product of power generation, and, thus, must be distinguished
from reference products (Eq. 3) such as the produced electricity, which is also
an outcome of the activity. Note that while flows and their activities both have
temporal extents, these extents can differ substantially. An activity such as waste
disposal may take hours, while the resulting emissions may continue for years.
Ref erenceP roduct v P roduct (3)
Besides their role as products, flows can be categorized into elementary flows
or intermediate flows (Eq. 4, 5). The first case describes material or energy that is
entering the system from the environment without any previous transformation
by humans or is leaving the system by being released into the environment
without further human transformation (Eq. 7) [7]. The environment is typically
described in terms of compartments such as air, water, or soil (Eq. 6). In contrast,
intermediate flows occur between processes of the studied system.
IntermediateF low u ElementaryF low v ⊥ (4)
ElementaryF low u Ref erenceP roduct v ⊥ (5)
{air, water, soil} v Compartment (6)
F low u ∃hasCompartment.{air, water, soil} v ElementaryF low (7)
Guarded domain and range restriction (see Eq. 8, 9 for examples) enable us
to infer additional facts about agents, activities, flows, locations, and so forth.
∃isInputOf.Activity v F low (8)
F low v ∀isInputOf.Activity (9)
4 Relation to Other Patterns and Ontologies
The pattern can be related to a number of other ontologies and ontology design
patterns. Details about properties and their values can be modeled via align-
ments to the GeoLink property pattern [8]. The location of an agent can be
expressed using GeoSPARQL, e.g., to represent the spatial footprint of a region.
The temporal extent can be modeled via OWL:Time. As discussed before, the
pattern models activities and flows, e.g., by describing their duration, inputs,
outputs, roles, and so forth. Equally relevant for LCA is the temporal and spa-
tial extent that scopes the impact assessments, e.g., to state that a certain level
of emissions was representative of Western Europe during the 1980s. This can be
modeled using the LCA scope ontology [14]. Our pattern and the scope ontol-
ogy can be aligned using their common Flow class. The pattern can further be
aligned to the material transformation pattern [13] that deals with products as
outcomes of transformations. Units are handled using the QUDT ontology [5].
5 Coal Power Plant Example
Here, we outline an example for emissions from a coal power plant to show how
the pattern answers the competency questions. In this case, CO2 emission is a
Flow as is the used coal. Electric power generation is modeled as an Activity, the
CO2 emission as its output (isOutputOf ), and coal as its input (isInputOf ). The
power plant generating (performs) said electricity is of type Agent and has a cer-
tain location. Thereby the activity can be located as well. The CO2 emission is an
ElementaryFlow with air as its compartment. In contrast, electric power (which
is also an output of the power generation activity) is the ReferenceProduct. The
activity and the flows can have a temporal extent (hasTemporalExtent).
6 Conclusion and Future Work
In this work, we briefly outlined the motivation behind developing a pattern as
common building block for LCA/LCI. We presented a few of the used axioms
and gave examples from the domain of power generation. The described work is
the joint result of a VoCamp on LCA that brought together leading international
domain experts with ontology engineers in March of 2015 at UCSB. Future work
will focus on introducing the pattern to a larger audience and integrating it with
the ongoing ontology projects at the US Environmental Protection Agency.
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