=Paper=
{{Paper
|id=Vol-431/paper-28
|storemode=property
|title=Towards ontology interoperability through conceptual groundings
|pdfUrl=https://ceur-ws.org/Vol-431/om2008_poster10.pdf
|volume=Vol-431
|dblpUrl=https://dblp.org/rec/conf/semweb/DietzeD08
}}
==Towards ontology interoperability through conceptual groundings==
https://ceur-ws.org/Vol-431/om2008_poster10.pdf
Towards Ontology Interoperability
through Conceptual Groundings
Stefan Dietze, John Domingue
Knowledge Media Institute,
The Open University,
MK7 6AA, Milton Keynes, UK
{s.dietze, j.b.domingue}@open.ac.uk
Abstract. The widespread use of ontologies raises the need to resolve heterogeneities between
distinct conceptualisations in order to support interoperability. The aim of ontology mapping is,
to establish formal relations between a set of knowledge entities which represent the same or a
similar meaning in distinct ontologies. Whereas the symbolic approach of established SW
representation standards – based on first-order logic and syllogistic reasoning – does not
implicitly represent similarity relationships, the ontology mapping task strongly relies on
identifying semantic similarities. However, while concept representations across distinct
ontologies hardly equal another, manually or even semi-automatically identifying similarity
relationships is costly. Conceptual Spaces (CS) enable the representation of concepts as vector
spaces which implicitly carry similarity information. But CS provide neither an implicit
representational mechanism nor a means to represent arbitrary relations between concepts or
instances. In order to overcome these issues, we propose a hybrid knowledge representation
approach which extends first-order logic ontologies with a conceptual grounding through a set
of CS-based representations. Consequently, semantic similarity between instances –
represented as members in CS – is indicated by means of distance metrics. Hence, automatic
similarity-detection between instances across distinct ontologies is supported in order to
facilitate ontology mapping.
Keywords: Semantic Web, Ontology Mapping, Conceptual Spaces,
Interoperability.
1 Introduction
The widespread use of ontologies - formal specifications of shared conceptualisations
[10] - together with the increasing availability of representations of overlapping
domains of interest, raises the need to resolve heterogeneities [12][14] by completely
or partially mapping between different ontologies. With respect to [2][17], we define
ontology mapping as the process of defining formal relations between knowledge
entities which represent the same or a similar semantic meaning in distinct ontologies
[6][19]. In that, ontology mapping strongly relies on identifying similarities [1]
between entities across different ontologies. However, with respect to this goal,
several issues have to be taken into account. The symbolic approach - i.e. describing
symbols by using other symbols, without a grounding in the real world - of
established representation standards such as OWL1 or RDF-S2 which are based on
first-order logic (FOL) and syllogistic reasoning [8] leads to ambiguity issues and
1 http://www.w3.org/OWL/
2 http://www.w3.org/RDFS/
does not entail meaningfulness, since meaning requires both the definition of a
terminology in terms of a logical structure (using symbols) and grounding of symbols
to a conceptual level [3][16]. Therefore, concept representations across distinct
ontologies – even those representing the same real-world entities - hardly equal
another, since similarity is not an implicit notion carried within ontological
representations. But manual or semi-automatic identification of similarity
relationships – based on linguistic or structural similarities across ontologies
[13][7][9] – is costly. Consequently, representational facilities, enabling to implicitly
describe similarities across ontologies are required in order to support ontology
interoperability.
Conceptual Spaces (CS) [8] follow a theory of describing entities at the conceptual
level in terms of their natural characteristics similar to natural human cognition in
order to avoid the symbol grounding issue [3][16]. In that, CS consider the
representation of concepts as vector spaces which are defined through a set of quality
dimensions. Describing instances as vectors enables the automatic calculation of their
semantic similarity by means of spatial distance metrics, in contrast to the costly
representation of similarities through symbolic representations. However, several
issues still have to be considered when applying CS. For instance, CS do not
explicitly prescribe any applicable representation method. Moreover, CS provide no
means to represent arbitrary relations between concepts or instances, such as part-of
relations. In order to overcome the issues introduced above, we propose a two-fold
knowledge representation approach which extends FOL ontologies with a conceptual
grounding by refining individual symbolic concept representations as particular CS.
Consequently, similarity becomes an implicit notion of the representation itself,
instead of relying on manual or semi-automatic similarity detection approaches.
2 Conceptual Groundings for Ontological Concepts
With respect to the aforementioned issues, we argue that basing knowledge models on
just one theory alone might not be sufficient. Therefore, we propose a two-fold
representational approach – combining FOL ontologies with corresponding
representations based on CS – to enable similarity-based reasoning across ontologies.
In that, we consider the representation of a set of n concepts C of an ontology O
through a set of n Conceptual Spaces CS. Hence, instances of concepts are
represented as members in the respective CS. While still benefiting from implicit
similarity information within a CS, our hybrid approach allows overcoming CS-
related issues by maintaining the advantages of FOL-based knowledge
representations. In order to be able to represent ontological concepts within CS, we
formalised the CS model into an ontology, represented through OCML [15]. Hence, a
CS can simply be instantiated in order to represent a particular concept.
Referring to [8][18], we formalise a CS as a vector space defined through quality
dimensions di of CS. Each dimension is associated with a certain metric scale, e.g.
ratio, interval or ordinal scale. To reflect the impact of a specific quality dimension on
the entire CS, we consider a prominence value p for each dimension [8]. Therefore, a
CS is defined by CS n = {( p1d1 , p2 d 2 ,..., pn d n ) d i ∈ CS , pi ∈ P} , where P is the set of real
numbers. However, the usage context, purpose and domain of a particular CS strongly
influence the ranking of its quality dimensions what supports our position of
describing distinct CS explicitly for individual concepts. Please note that we do not
distinguish between dimensions and domains [8] but enable dimensions to be detailed
further in terms of subspaces. Hence, a dimension within one space may be defined
through another CS by using further dimensions [18]. In this way, a CS may be
composed of several subspaces, and consequently, the description granularity can be
refined gradually. Dimensions may be correlated. Information about correlation is
expressed through axioms related to a specific quality dimension instance.
A particular member M – representing a particular instance – in the CS is described
through valued dimension vectors vi like M n = {(v1 , v2 ,..., vn ) vi ∈ M }. With respect to
[18], we define the semantic similarity between two members of a space as a function
of the Euclidean distance between the points representing each of the members.
However, we would like to point out that different distance metrics, such as the
Taxicab or Manhattan distance [11], could be considered, dependent on the nature and
purpose of the CS. Given a CS definition CS and two members V and U, defined by
vectors v0, v1, …,vn and u1, u2,…,un within CS, the distance between V and U can be
n
ui − u v − v 2 where u is the mean of a dataset
calculated as dist (u, v) =
∑ p (( s
i =1
i )−( i
sv
))
u
U and su is the standard deviation from U. The formula above already considers the
so-called Z-transformation or standardization [4] which facilitates the standardization
of distinct measurement scales in order to enable the calculation of distances in a
multi-dimensional and multi-metric space.
Representing Ontological Concepts through Conceptual Spaces
The derivation of an appropriate space CSi to represent a particular concept Ci of a
given ontology O is understood a non-trivial task which primarily implies the creation
of a CS instance which most appropriately represents the real-world entity represented
by the symbolic concept representation. We foresee a transformation procedure
consisting of the following steps:
S1. Representing concept properties pcij of Ci as dimensions dij of CSi.
S2. Assignment of metrics to each quality dimension dij.
S3. Assignment of prominence values pij to each quality dimension dij.
S4. Representing instances Iik of Ci as members in CSi.
A specific CS is instantiated by applying a transformation function which is aimed at
instantiating all elements of a CS (S1 – S3). S1 aims at representing each concept
property pcij of Ci as a particular dimension instance dij together with a corresponding
prominence pij of a resulting space CSi:
{ } { }
trans : ( pci1 , pci 2 ,..., pcin ) pcij ∈ PC ⇒ ( pi1d i1 , pi 2 d i 2 ,..., pin d in ) d ij ∈ CS i , pij ∈ P
Please note that we particularly distinguish between data type properties and relations.
While the latter represent relations between concepts, these are not represented as
dimensions since such dimensions would refer to a range of concepts (instances)
instead of quantified metrics, as required by S2. In the case of relations, we propose to
maintain the relationships represented within the original ontology O without
representing these within the resulting CSi. In that, the complexity of CSi is reduced to
enable the maintainability of the spatial distance as appropriate similarity measure.
S2 aims at the assignment of metric scales (interval scale, ratio scale, nominal scale),
while S3 is aimed at assigning a prominence value pij to each dimension dij.
Prominence values should be chosen from a predefined value range, such as 0...1.
With respect to S4, one has to represent all instances Iki of a concept Ci as member
instances in the created space CSi. This is achieved by transforming all instantiated
properties piikl of Iik as valued vectors in CSi.
trans : {( piik1 , piik 2 ,..., piikn ) piikl ∈ PI l } ⇒ {(vik1 , vik 2 ,..., vikn ) vikl ∈ M ik }
Hence, given a particular CS, representing instances as members becomes just a
matter of assigning specific measurements to the dimensions of the CS. In order to
represent all concepts Ci of a given ontology O, the transformation function consisting
of the steps S1-S4 has to be repeated iteratively for all Ci which are element of O. The
accomplishment of the proposed procedure results in a set of CS instances which each
refine a particular concept together with a set of member instances which each refine
a particular instance.
3 Conclusion
In order to facilitate ontology mapping, we proposed a hybrid representation approach
based on a combination of FOL ontologies and multiple concept representations in
individual CS. Representing concepts following the CS theory enables representation
of instances as vectors in a respective CS and consequently, the automatic
computation of similarities by means of spatial distances. A CS-based representation
is supported through a dedicated CS formalisation, i.e. a CS ontology, and a formal
method on how to derive CS representations for individual concepts. Within proof-of-
concept prototype applications, e.g. [5], an OCML [15] representation of the proposed
hybrid representational model was utilized to validate the applicability of the
approach. Following our two-fold representational approach supports implicit
representation of similarities across heterogeneous ontologies, and consequently,
provides a means to facilitate ontology mapping. Moreover, our approach overcomes
certain individual issues posed by each of the two approaches. Whereas traditional
ontology mapping methodologies rely on mechanisms to semi-automatically detect
similarities at the concept and the instance level, our approach just requires a common
agreement at the concept level since similarity information at the instance level is
implicitly defined.
However, the authors are aware that our approach requires a considerable amount
of additional effort to establish CS-based representations. Future work has to
investigate this effort in order to further evaluate the potential contribution of the
approach proposed here. Moreover, further issues related to CS-based knowledge
representations still remain. For instance, whereas defining instances, i.e. vectors,
within a given CS appears to be a straightforward process, the definition of the CS
itself is not trivial at all and dependent on subjective perspectives. With regard to this,
CS do not fully solve the symbol grounding issue but to shift it from the process of
describing instances to the definition of a CS. Nevertheless, distance calculation relies
on the fact that resources are described in equivalent (or mapped) geometrical spaces.
However, we would like to point out that the increasing usage of upper level
ontologies and the progressive reuse of ontologies, particularly in loosely coupled
organisational environments, leads to an increased sharing of ontologies at the
concept level. As a result, our proposed hybrid representational model becomes
increasingly applicable by further enabling similarity-computation at the instance-
level towards the vision of interoperable ontologies.
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