Environmental Engineering Reference
In-Depth Information
1. As the same concept might be used for different meanings, assuming unspeciied
information, for example “crop yield” in a model as a single value and “crop
yield” in a database as a series of values over space and time;
2. As different concepts might be used, which have the same meaning, for example
“derivative” and “rate”;
3. As concepts might be used with an ambiguous meaning, for example the word
“scenario” (Schoemaker
1993);
4. As relationships between concepts might be understood in a different
way, for example the spatial relationship of inclusion of farm within an
“agri-environmental zone”, and the relationship between the same farm and
an administrative area.
The challenge in integrated modelling is the conceptual integration. To achieve
this, we need explicit semantics and a shared conceptualization. For this we
need to tackle the different perceptions and interpretations of people involved.
Different modelling approaches, different formalisms and last but certainly
not least, the different integration requirements and ambitions need to be taken
into account.
To enable semantic interoperability in integrated modelling, the problem of
semantic conflicts or semantic heterogeneity needs to be solved.
The Need for a Common Ontology
One of the many definitions of ontology is from Neches et al.
(1991)
“an ontology
defines the basic terms and relations comprising the vocabulary of a topic area as
well as the rules for combining terms and relations to define extensions to the
vocabulary”. According to Gruber
(1993)
, an ontology is “an explicit and formal
specification of a conceptualization”. A conceptualization is explained as an
abstract model of a phenomenon, identifying the relevant attributes. A conceptu-
alization is an abstract, simplified view of the world that we wish to represent.
Every knowledge base or knowledge-based system is committed to some
conceptualization (Gruber
1995)
. Formal specification refers to the fact that the
language semantics are machine readable, and follow a mathematical framework
for logical reasoning. Often this is done by use of W3C OWL (Ontology Web
Language, Patel-Schneider et al.
2004)
. A formal specification helps to communicate
the definition of terms in a context independent way and formal language semantics
allows some automated consistency checks.
An ontology helps to formalize the knowledge captured in and/or between models,
in order to subsequently facilitate model development, testing and documentation
(Scholten et al.
2007)
and increase model re-usability and exchangeability (Rizzoli
et al.
2008
; Villa et al.
2009)
. Besides this it separates knowledge captured in the
model from the actual implementation in a modelling language or in software e.g.
Java, FORTRAN, Matlab, STATA, etc. (Gruber
1993
; Villa et al.
2006)
or from the
data in a database (Zander and Kächele
1999)
.
