=Paper=
{{Paper
|id=Vol-298/paper-9
|storemode=property
|title=Ontologies as contexts for constraint-based reasoning
|pdfUrl=https://ceur-ws.org/Vol-298/paper9.pdf
|volume=Vol-298
|dblpUrl=https://dblp.org/rec/conf/context/WallaceND07
}}
==Ontologies as contexts for constraint-based reasoning==
https://ceur-ws.org/Vol-298/paper9.pdf
Ontologies as Contexts for Constraint-Based Reasoning
Richard J. Wallace, Tomas E. Nordlander, and Ioannis Dokas
Cork Constraint Computation Centre and Department of Computer Science
University College Cork, Cork, Ireland
email: r.wallace,t.nordlander,i.dokas @4c.ucc.ie
Abstract. We give a brief description of a system we are currently building that
fulfils the promise of our title. This description also indicates why we are inter-
ested in the topic of contexts and ontologies.
1 Introduction
Many problems involve finding a solution subject to certain constraints. Constraint
programming is an important technique for modelling such problems, which supports
combinations of inference and search that allow more efficient solving. However, this
cleverness is obtained at a cost, since these methods normally required highly skilled
specialists as well as a lengthy period of time for modelling and crafting of solvers.
The question we are interested in here is whether these methods can be embedded so
that a layperson using such a system can make use of these methods without having to
deal with them directly. In this case it is necessary that a constraint model - with at least
a simple form - be built automatically, and that the solving process proceed without
the user being aware of the particular kind of representation involved. What the user is
aware of is the original problem, stated in everyday terms that he or she is familiar with.
In order to bring about this result, we are interesting in using ontologies to repre-
sent the problem in a simple, qualitative manner, which can also serve as the basis for
deducing a more abstruse CSP model. The ontology, therefore, provides the context
for constraint modelling and constraint-based reasoning. (In fact, it can provide many
contexts as the following section will show.)
2 Description of an Ontology-and-Constraint Based System
We are presently trying to implement our ideas in a product selection system called the
Matchmaker. Product selection is carried out using a form of interaction in which the
system presents individual products and the user critiques them. (This is called a “sug-
gestion/correction cycle”.) Internally, the problem of product selection is represented as
a constraint satisfaction problem, but with each critique the representation is updated to
conform to the additional constraints revealed on this cycle. (This scheme is presented
in more detail in [1].)
The Matchmaker program is written in Java, and Protege-OWL is being used to
build the product ontology. In its final form, the system will be web-based and the
system therefore falls under the heading of e-commerce.
Conceptually, a product is not only an item of interest but is also possibly an object,
an artefact, an organisation, etc., and is associated with some type of setting, or even a
specific location. In order to carry out the matchmaking process in a ‘knowledgeable’
manner, a product must be viewed in all of these ways. At the same time, there are
restrictions on the degree of detail or the level of technical sophistication that can be
handled efficiently; hopefully, there are also limits on what the user needs to know in
order to make a satisfactory selection.
The resulting conceptualisation of a product can be thought of as a set of contexts
in which the item in question is embedded. A straightforward and efficient way to rep-
resent these various contexts is through an ontology. In this case, if we start with a
particular product type, we can reach associated concepts by means of a class hierar-
chy of the usual type, as well as deducing relevant relations (“properties” in the usual
parlance) involving these classes. Our expectation is that such context-supported inter-
action will help us achieve the kind of interaction envisaged for the Matchmaker system
in a sufficiently compelling fashion.
3 Ontologies to CSPs
Another major challenge necessitated by our approach is connecting the ontology to the
constraint solver in a way that supports the computational requirements, while being
transparent to the user. This is related to another motivation for using ontologies, which
is to allow users to work with familiar concepts, without having to think in terms of the
rather abstruse language of constraint programming and constraint satisfaction. In the
implementation we are building, this is done by translating relevant ontology classes
into variables, following rules that support the CSP formalism, in particular, the rule of
single assignments to a variable.
An alternative approach, that we will consider in the future, is to use a CSP ontology
to support the mapping between subject-matter concepts and the CSP representation.
Some work along these lines has been done by other authors [2].
4 Future Vistas
The use of ontologies to provide conceptual contexts for constraint solving and to
‘shield’ users from constraint programming details is a general idea that may be useful
in a number of application areas. Obvious candidates are systems for scheduling and
configuration.
References
1. Freuder, E.C., Wallace, R.J.: Suggestion strategies for constraint-based matchmaker agents.
International Journal on Artificial Intelligence Tools 11 (2002) 3–18
2. Sleeman, D., Chalmers, S.: Assisting domain experts to formulate and solve constraint satis-
faction problems. In: Proc. 15th Internat. Conf. on Knowledge Engineering and Knowledge
Management, LNCS No. 4248, Berlin, Springer (2006) 27–34