=Paper=
{{Paper
|id=Vol-460/paper-5
|storemode=property
|title=Enriching Service Semantics through Conceptual Vector Spaces
|pdfUrl=https://ceur-ws.org/Vol-460/paper05.pdf
|volume=Vol-460
}}
==Enriching Service Semantics through Conceptual Vector Spaces==
https://ceur-ws.org/Vol-460/paper05.pdf
Enriching Service Semantics through
Conceptual Vector Spaces
Stefan Dietze1, John Domingue1
1
Knowledge Media Institute,
Open University,
MK7 6AA, Milton Keynes, UK
{s.dietze, j.b.domingue}@open.ac.uk
Abstract. Semantic Web Services (SWS) aim at the automated discovery and
orchestration of Web services on the basis of comprehensive, machine-
interpretable semantic descriptions. In that, SWS strive for automated
interoperability and reusability of heterogeneous services through matchmaking
of semantic capability and interface descriptions. However, to do so,
established SWS reference models build on the general assumption that either
(a) SWS providers subscribe to a common vocabulary to annotate their services
or (b) alignments between distinct vocabularies are established. This is due to
the fact that SWS descriptions are lacking sufficient meaningfulness to
automatically infer relationships between syntactically different semantic
annotations. In order to address these issues and to overcome the need for (a)
and (b), we propose a representational approach which allows to enrich
standard SWS descriptions through vector spaces, which are represented as a
dedicated ontology being aligned with existing SWS standards. As a result,
similarities between instances used to annotate SWS become automatically
computable by means of spatial distances. Hence, our approach significantly
contributes to solve the interoperability problem between heterogeneous SWS
as well as SWS reference models.
Keywords: Semantic Web Services, Interoperability, Vector Spaces.
1 Introduction
The ongoing shift to service-orientation in software development leads to an
increasing availability of a broad variety of Web services, ranging from SOAP-based
ones to rather light-weight approaches based on REST [9] or XML-RPC [25]. This
raises the need to automatically discover and orchestrate appropriate services for a
given need. Semantic Web Services (SWS) [8] aim at addressing this challenge on the
basis of comprehensive, machine-interpretable semantic descriptions. Since Web
services usually are provided by distinct and independent parties, the actual Web
service interfaces as well as their semantic representations are highly heterogeneous.
This strongly limits the interoperability and re-usability of services. In order to cope
with heterogeneity, established SWS reference models such as WSMO [26], OWL-S
50 Proceedings of ONTOSE 2009
[16] or SAWSDL1 build upon the assumption, that either (a) SWS providers
subscribe to a common vocabulary to annotate their services or (b) alignments
between distinct vocabularies used by different SWS are established while somehow
automatic mediation approaches are still limited and underdeveloped [17]. This is due
to the fact that SWS descriptions are lacking sufficient meaningfulness to
automatically infer relationships – particularly semantic similarity [1] relationships –
between independent and syntactically different semantic annotations, such as
concepts and instances which are part of different SWS. However, since this is a
fundamental requirement to enable matchmaking across heterogeneous SWS [22][27],
large-scale interoperability is not facilitated.
In this paper, we propose a representational approach which enriches the
expressiveness of SWS approaches with formal representations following the
Conceptual Spaces (CS) [10] approach. In particular, we propose an ontology which
is aligned to SWS reference models and facilitates a grounding of SWS descriptions
into multiple vector spaces. We will demonstrate that refining heterogeneous SWS
descriptions in multiple shared CS supports computation of semantic similarities and
implicitly facilitates matchmaking and discovery of heterogeneous SWS.
The remainder of the paper is organized as follows: Section 2 introduces the SWS
matchmaking problem, while our representational approach based on refinement of
SWS ontologies in CS is proposed in Section 3. In Section 4, we introduce application
of our approach to an existing SWS reference model. Finally, we discuss and
conclude our work in Section 6.
2 Semantic Web Services and the Matchmaking Problem
We report below some abstract definitions of SWS as used throughout the remainder
of the paper, together with background information on current matchmaking and
mediation approaches.
Semantic Web Services: a SWS description (either the description of the Web
service or the description of the service request) is formally represented within a
particular ontology that complies with a certain SWS reference model such as OWL-S
[16] or WSMO [26]. By adopting a common formalisation of an ontology [6], we
define a populated service ontology O – as utilised by a particular SWS representation
– as a tuple:
O = {C , I , P, R, A} ⊂ SWS
With C being a set of n concepts where each concept Ci is described through l(i)
concept properties pc. I represents all m instances where each instance Iij represents a
particular instance of a concept Cj and consists of l(i) instantiated properties pi
instantiating the concept properties of Cj. Hence, the properties P of an ontology O
represent the union of all concept properties PC and instantiated properties PI of O.
Given these definitions, we would like to point out that properties here exclusively
refer to so-called data type properties. Hence, we define properties as being
distinctive to relations R. The latter describe relations between concepts and instances.
1 http://www.w3.org/2002/ws/sawsdl/spec/
Proceedings of ONTOSE 2009 51
In addition, A represents a set of axioms which define constraints on the other
introduced notions. Since certain parts of a SWS ontology describe certain aspects of
the Web service (request), such as its capability Cap, interface If or non-functional
properties Nfp [4], a SWS ontology can be perceived as a conjunction of ontological
subsets:
Cap ∪ If ∪ Nfp ⊂ SWS
The semantic capability description consists of further subsets, describing the
assumptions As, effects Ef, preconditions Pre and postconditions Post. However,
given the lack of a clear distinction between assumption/effect and pre-/postcondition,
we prefer the exclusive usage of assumptions/effects:
As ∪ Ef = Cap ⊂ O ⊂ SWS
SWS discovery as a similarity computation problem: SWS discovery across
distributed SWS requires semantic level mediation, i.e. the mediation between
heterogeneous SWS descriptions to overcome the need for either manual mappings or
the subscription to a common vocabulary. That is perceived to be a fundamental
requirement to further exploit SWS approaches on a Web scale. SWS discovery
requires to identify SWS which are best suitable to satisfy a certain request. In that, in
order to identify whether a particular SWS S1 is potentially relevant for a given
request S2, a SWS broker has to compare the capabilities of S1 and S2, i.e. it has to
identify whether the following holds true:
As 2 ⊂ As1 ∪ Ef 2 ⊂ Ef 1
However, in order to compare distinct capabilities of available SWS which each
utilise a distinct vocabulary, these vocabularies have to be aligned. For instance, to
compare whether an assumption expression As1 ≡ ¬I1 ∪ I 2 of one particular SWS1 is
the same as As2 ≡ I 3 ∪ ¬I 4 of another SWS2, where Ii represents a particular instance,
matchmaking engines have to perform two steps: (a) identification of relationships
between concepts/instances involved in distinct SWS representations; (b) evaluation
whether the semantics of the two SWS expressions match each other. Whereas current
SWS execution environments exclusively focus on (b), SWS discovery also requires
mediation between different ontologies, as in (a), and could also be perceived as a
particular instantiation of the ontology mapping problem [27][3]. I.e. following [6] the
goal is, to establish formal relations between a set of knowledge entities E1 from an
ontology O1 – used to represent a particular SWS S1 – with entities E2 which represent
the same or a similar semantic meaning in a distinct ontology O2 (SWS S2). In that,
SWS discovery strongly relies on identifying semantic similarities [1] between
entities across different SWS ontologies. Hence, the identification of similarities is a
necessary requirement to solve the discovery problem for multiple heterogeneous
SWS representations [27][21]. However, while similarity detection across distinct
SWS representations requires semantic meaningfulness, the symbolic approach – i.e.
describing symbols by using other symbols, without a grounding in the real world – of
established SWS representation standards, leads to ambiguity issues and does not
fully entail semantic meaningfulness [5][14]. Moreover, describing the complex
notion of specific SWS capabilities in all their facets is a costly task and may never
reach semantic completeness.
Given the lack of inherent similarity representation, current approaches to ontology
mapping could be applied to facilitate SWS mediation. These approaches aim at semi-
52 Proceedings of ONTOSE 2009
automatic similarity detection across ontologies mostly based on identifying
linguistic commonalities and/or structural similarities between entities of distinct
ontologies [3][7][15]. However, such approaches require manual intervention, are
costly and error-prone, and hence, similarity-computation remains as central
challenge. In our vision, instead of semi-automatically formalising individual
mappings or subscribing to common vocabularies, methodologies to automatically
compute or implicitly represent similarities across distinct SWS representations are
better suited to facilitate SWS interoperability.
3 Enriching Service Semantics through Conceptual Vector Spaces
To overcome the issues introduced in the previous Section, we propose a
representational approach enriching SWS representations through multiple vector
spaces following the Conceptual Spaces (CS) [9] theory. CS represent entities in
terms of their quality characteristics similar to natural human cognition in order to
bridge between the neural and the symbolic world [9]. In that, CS are represented
through multidimensional geometrical vector spaces where instances are supposed to
be represented as vectors, i.e. particular points in a CS. Describing instances as
vectors which each vector follow a specific metric enables the automatic calculation
of their semantic similarity by means of distance metrics such as the Euclidean,
Taxicab or Manhattan distance [12] or the Minkowsky Metric [22]. However, CS do
not provide any notion to represent any arbitrary relations [20], such as part-of
relations which usually are represented within symbolic knowledge models.
3.1 Conceptual Groundings for SWS
We propose a representational approach which combines symbolic SWS
representation with groundings in multiple CS (Figure 1) to enable the implicit
representation of semantic similarities across heterogeneous SWS representations
provided by distinct agents.
Agent 1 Agent 2
SWS Ontology O1 SWS Ontology O2
Concept c1x Concept c2x
is-a is-a
refined-as-cs refined-as-cs
Instance i1i Instance i2i
refined-as-member d1 refined-as-member
d2
d3
Conceptual Space CSx
Fig. 1. Representing heterogeneous SWS representations through shared Conceptual Spaces.
Proceedings of ONTOSE 2009 53
Hence, we facilitate similarity based mediation at the semantic level and consequently
support the SWS discovery task. Whereas CS allow the representation of semantic
similarity as a notion implicit to a constructed knowledge model, it can be argued,
that representing an entire SWS through a coherent CS might not be feasible,
particularly when attempting to maintain the meaningfulness of the spatial distance as
a similarity measure. Therefore, we claim that CS are a particularly promising model
when being applied to individual concepts – as part of SWS descriptions – instead of
representing an entire ontology in a single CS. In that, we would like to highlight that
we consider the representation of a set of n concepts C of a SWS ontology O through
a set of n CS (Figure 1). Hence, instances of concepts are represented as members (i.e.
vectors) in the respective CS. While still taking advantage from implicit similarity
information within a CS, our hybrid approach – combining SWS descriptions with
multiple CS – allows overcoming CS-related issues by maintaining the advantages of
ontology-based SWS representations.
Please note that our approach relies on the agreement on a common set of CS for a
given set of distinct SWS ontologies, instead of a common agreement on the
ontologies themselves. Hence, whereas in the latter case two agents have to agree on a
common ontology at the concept and instance level, our approach requires just
agreement at the concept level, since instance similarity becomes an implicit notion.
Moreover, we assume that the agreement on ontologies at the concept level becomes
an increasingly widespread case, due to, on the one hand, increasing use of upper-
level ontologies such as DOLCE [11], SUMO [23] or OpenCyc2 which support a
certain degree of commonality between distinct ontologies. On the other hand, SWS
ontologies often are provided within closed environments, for instance, virtual
organisations, where a common agreement to a certain extent is ensured. In such
cases, the derivation of a set of common CS is particularly applicable and
straightforward.
In order to refine and represent SWS descriptions within a CS, we formalised the
CS model into an ontology (CSO)3, currently being represented through OCML [13].
The ontology enables the instantiation of a set of CS to represent a given set of
concepts as part of SWS descriptions. Referring to [19], 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 [19]. Therefore, a CS is defined by
CS n = {( p1d1, p2d2 ,..., pndn ) di ∈ CS, pi ∈ ℜ}.
However, usage context, purpose and domain of a particular CS strongly influence the
ranking of its quality dimensions. This clearly supports our position of describing
distinct CS explicitly for individual concepts. Please note that dimensions could be
detailed further in terms of subspaces. Hence, a dimension within one CS may be
defined through another CS by using further dimensions. In such a case, the particular
quality dimension dj is described by a set of further quality dimensions. In this way, a
CS may be composed of several subspaces and consequently, the description
2 http://www.opencyc.org/
3 http://people.kmi.open.ac.uk/dietze/ontologies/conceptual-spaces.lisp
54 Proceedings of ONTOSE 2009
granularity can be refined gradually. Furthermore, dimensions may be correlated what
is expressed through axioms related to a specific quality dimension instance.
A member M – representing a particular instance – of the CS is described through a
set of valued dimension vectors vi:
M n = {(v1 , v2 ,..., vn ) vi ∈ M }
With respect to [19], 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, different distance metrics, such as the Taxicab or Manhattan
distance [12], 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 calculated as:
n
ui − u v −v 2
dist (u, v) = ∑ p (( s
i =1
i )−( i
sv
))
u
where u is the mean of a dataset U and su is the standard deviation from U. The
formula above already considers the so-called Z-transformation or standardization
[22] which facilitates the standardization of distinct measurement scales utilised by
different quality dimensions in order to enable the calculation of distances in a multi-
dimensional and multi-metric space.
3.2 Representing SWS Capabilities through Conceptual Spaces
Following our vision, the provisioning of SWS representations is a highly
heterogeneous and distributed procedure that is accomplished autonomously by
distinct agents. In particular, we distinguish two groups of involved agents: (C1)
distributed SWS providers and consumers and (C2) centralised SWS maintainers. The
existence of C2 is implied by the broker-based nature of SWS technologies.
Specifically, the overall procedure of providing SWS following our approach is
based on the following steps:
S1. Provisioning of a central SWS runtime environment (C2).
S2. Provisioning of SWS representations Sn (C1).
S3. Providing appropriate CSi for each distinct real-world entity represented within
an available SWS ontology O.
S3.1. Representing concept properties pcij of Ci as dimensions dij of CSi (C2).
S3.2. Assignment of metrics to each quality dimension dij (C2).
S3.3. Assignment of prominence values pij to each quality dimension dij (C2).
S3.4. Representing all instances Iik of Ci as members in CSi (C1).
Whereas S1 and S2 are foreseen within the SWS vision in general, S3 represents an
additional activity aiming at providing the representational facilities required to
realise our mediation approach. Referring to our formalisations of O and CS (Sections
2 and 3), we are able to simply instantiate a specific CSi by applying a transformation
function
trans : Ci ⇒ CS i
Proceedings of ONTOSE 2009 55
The function is aimed at instantiating all elements of a CS, such as dimensions and
prominence values (S3.1 – S3.3). In particular, S3.1 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 ∈ PCi ⇒ ( pi1d i1 , pi 2 d i 2 ,..., pin d in ) d ij ∈ CS i , pij ∈ P }
We particularly distinguish between data type properties and relations. The latter
represent relations between concepts, but these are not represented as dimensions
since such dimensions would refer to a range of concepts (instances) instead of
quantified metrics, as required by S3.2. Hence, we propose to maintain the
relationships represented within the original SWS 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.
The assignment of metric scales to dimensions (S3.2) which naturally are described
using quantitative measurements, such as size or weight, is rather straightforward. In
such cases, interval scale or ratio scale are used. Otherwise, the respective dimensions
need to be refined by means of subspaces (Section 3.1) until appropriate metric scales
can be assigned. Since different dimensions might have distinct impact on the entire
space CSi, S3.3 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.
Since the assignment of prominences to quality dimensions is of major importance for
the expressiveness of the similarity measure within a space, most probably this step
requires incremental ex-post re-adjustments until a sufficient definition of a CS is
achieved.
With respect to S3.4, each SWS provider (C1) has to represent all instances Iik of a
concept Ci as member instances in the created space CSi:
trans : I ik ⇒ M ik
This is achieved by transforming all instantiated properties piikl of Iki as valued vectors
in CSi.
{
trans : {( piik1 , pcik 2 ,..., pcikn ) piikl ∈ PI ik } ⇒ (vik1 , vik 2 ,..., vikn ) vikk ∈ 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. The
accomplishment of the proposed procedure, particularly S3, results in a set of CS
(member) instances where each CS (member) instance refines a particular concept
(instance) of the SWS ontology. Please note that applying the procedure proposed
here requires an additional effort.
4 Similarity-based SWS Discovery for WSMO and IRS-III
The representational model described above had been implemented by and aligned to
established SWS technologies based on WSMO [26] and the Internet Reasoning
Service IRS-III [2]. However, please note that in principle the representational
approach described above could be applied to any SWS reference model and is
56 Proceedings of ONTOSE 2009
particularly well-suited to support rather light-weight approaches such as SAWSDL
or WSMO Lite [24].
4.1 The IRS-III Service Ontology
The IRS-III Service Ontology – represented through OCML [13] – provides semantic
links between the knowledge level components describing SWS and the conditions
related to their use. It is based on WSMO [26][8] and contains the following main
items:
• Goal-related information. A goal represents the user perspective of the required
functional capabilities and includes a description of the requested Web service
capability.
• Web service functional capabilities. They represent the provider perspective of
what the service does in terms of inputs, output, pre-conditions and post-
conditions, assumptions and effects. Pre-/postconditions and assumptions/effects
are expressed by logical expressions that constrain the state or the type of inputs
and outputs.
• Web service interface. The interface is defined by choreography and orchestration.
The choreography specifies how to communicate with a Web service. A grounding
describes how the semantic declarations are associated with a syntactic
specification, such as WSDL. The orchestration of a Web service specifies the
decomposition of its capability in terms of the functionality of other Web services.
• Mediators. A mediator specifies which top elements are connected and which type
of mismatches can be resolved between them.
irs:Domain
uses
uses
used-mediator irs:OO-mediator
irs:GG-mediator
has-mapping-rule : mapping-rule
has-mediation-service : wsmo:Goal has-target-component used-mediator
can-solve-goal has-interface
has-source-component wsmo:Goal irs:Web Service irs:Interface
has-input-role : Role is-suitable-for-goal has-orchestration : Orchestration
has-output-role : Role has-choreography : Choreography
has-postcondition : KappaExpression
has-effect : KappaExpression
has-capability
has-source-component
irs:Capability
irs:WG-Mediator
used-mediator has-assumption : KappaExpression
has-mediation-service : wsmo:Goal has-effect : KappaExpression
has-precondition : KappaExpression
has-postcondition : KappaExpression
Fig. 2. IRS-III Service Ontology – core concepts and relations.
While the IRS-III Service Ontology considers Meta-classes for the top-level SWS
concepts individual SWS definitions (goals, mediators, Web services) are defined as
subclasses rather than instances. A class better captures, indeed, the concept of a
reusable service description and taxonomic structures can be used to capture the
constitution of a particular domain. At invocation time, particular instances of the
respective goal, mediator and Web services automatically generated.
Proceedings of ONTOSE 2009 57
4.2 Introducing Similarity-based SWS Selection based on Conceptual Spaces
In order to facilitate the representational approach described in Section 3, we aligned
the CSO (Section 3) with the IRS-III Service Ontology to allow for the refinement of
individual concepts – being used as part of formal SWS descriptions – as formally
expressed CS. In that, instances being used to represent SWS characteristics such as
interfaces or capabilities can be refined as vectors to enable similarity computation
between individual SWS and SWS requests.
cs:Quality Dimension cs:Conceptual Space irs:Concept irs:Web Service
uses refined-as uses
values member-in instance-of can-solve-goal
cs:Valued Vector uses cs:Member refined-as irs:Instance uses irs:Goal
Fig. 3. Core concepts of the CS Ontology aligned to the IRS-III Service Ontology.
Figure 3 depicts the core concepts of CSO and their alignment with the IRS-III
Service Ontology. Concepts (instances) as being used by IRS service or goal
descriptions are refined as CS (members) within the CSO. In that, following the
procedure proposed in Section 3.2, service capabilities are refined in multiple CS.
In order to facilitate automated similarity computation between SWS and SWS
requests, we extended the matchmaking capabilities of IRS-III through a set of
additional functions which introduce similarity computation as part of the SWS
selection and matchmaking algorithm. Given the ontological refinement of SWS
descriptions into CS as introduced in Section 3 this new functionality enables to
automatically achieve IRS-III goals without being restricted to complete matches
between a particular goal achievement request and the available SWS. When
attempting to achieve a goal, our new function is provided with the actual SWS
request SWSi, named base, and the SWS descriptions of all x available services that
are potentially relevant for the base – i.e. linked through a dedicated mediator:
SWS i ∪ {SWS 1 , SWS 2 ,..., SWS x }
Each SWS contains a set of concepts C={c1..cm} and instances I={i1..in}. We first
identify all members M(SWSi) – in the form of valued vectors {v1..vn} refining the
instance il of the base as proposed in Section 3. In addition, for each concept c within
the base the corresponding conceptual space representations MS={MS1..MSm} are
retrieved. Similarly, for each SWSj related to the base, members M(SWSj) – which
refine capabilities of SWSj and are represented in one of the CS CS1..CSm – are
retrieved:
CS ∪ M ( SWS i ) ∪ {M ( SWS 1 ), M ( SWS 2 ),..., M ( SWS x )}
Based on the above ontological descriptions, for each member vl within M(SWSi), the
Euclidean distances to any member of all M(SWSj) which is represented in the same
space MSj as vl are computed. In case one set of members M(SWSj) contains several
members in the same MS – e.g. SWSj targets several instances of the same kind – the
algorithm just considers the closest distance since the closest match determines the
appropriateness for a given goal. For example, if one SWS supports several different
locations, just the one which is closest to the one required by SWSi determines the
appropriateness.
Consequently, a set of x sets of distances is computed as follows
Dist(SWSi)={Dist(SWSi,SWS1), Dist(SWSi,SWS2) .. Dist(SWSi,SWSx)} where each
58 Proceedings of ONTOSE 2009
Dist(SWSi,SWSj) contains a set of distances {dist1..distn} and any disti represents the
distance between one particular member vi of SWSi and one member refining one
instance of the capabilities of SWSj. Hence, the overall similarity between the base
SWSi and any SWSj could be defined as being reciprocal to the mean value of the
individual distances between all instances of their respective capability descriptions
and hence, is calculated as follows:
−1
⎛ n ⎞
⎜ ∑ (dist k ) ⎟
( −1
Sim( SWSi , SWS j ) = Dist ( SWSi , SWS j ) = ⎜
⎜
)
k =1
n
⎟
⎟
⎜ ⎟
⎝ ⎠
Finally, a set of x similarity values – computed as described above – which each
indicates the similarity between the base SWSi and one of the x target SWS is
computed:
{Sim ( SWS i , SWS1 ), Sim ( SWS i , SWS 2 ),.., Sim ( SWS i , SWS x )}
As a result, the most similar SWSj, i.e. the closest associated SWS, can be selected
and invoked. In order to ensure a certain degree of overlap between the actual request
and the invoked functionality, we also defined a threshold similarity value T which
determines the similarity threshold for any potential invocation.
A first prototypical application – accessible through an AJAX-based interface4 –
deploying the above functionality has been developed. The application supports
similarity-based selection between a number of Web services which deliver video
material and make use of the youtube-API5 as well as the data feeds provided by
BBC- Backstage6.
5 Discussion and Conclusions
We proposed a representational model which enriches the expressiveness of SWS
technologies with metric-based representation in CS. As a result, the semantic
meaningfulness of SWS representations is increased allowing to automatically infer
about similarity-relationships between instances as used by heterogeneous SWS. We
introduced a formal ontology which is aligned to the IRS-III Service Ontology and
could potentially be utilised in the context of other established SWS reference models
such as SAWSDL or OWL-S. In that, our two-fold representational approach
provides a means to facilitate SWS interoperability. In addition, we extended the
matchmaking algorithm of an existing SWS Broker, IRS-III, with new capabilities
allowing for rather similarity-based matchmaking – based on our two-fold
representational model – to overcome the need for strict complete SWS matchmaking.
Furthermore, our approach is supported by a formal method on how to derive CS
representations for individual concepts of any arbitrary SWS representations.
The proposed approach has the potential to significantly reduce the effort required
to support interoperability between distinct heterogeneous SWS ontologies by
overcoming the need to either subscribe to a common vocabulary or to align distinct
4 http://kmi-lisp05.open.ac.uk/demo
5 http://code.google.com/intl/en/apis/youtube/
6 http://backstage.bbc.co.uk/
Proceedings of ONTOSE 2009 59
SWS ontologies. While our approach supports automatic similarity-computation
between SWS ontology instances it requires a common agreement on shared CS.
However, incomplete similarities are computable between partially overlapping CS.
Given the nature of our approach – aiming at mediating between sets of
concepts/instances which are used to annotate particular SWS – we argue that our
solution is particularly applicable to SWS frameworks which are based on rather
light-weight service semantics such as WSMO-Lite [24] or OWL-S [16]. Moreover,
by representing SWS through vectors which are independent from the underlying
representation language, we claim that our approach also has the potential to bridge
between SWS across concurrent SWS reference models and modeling languages.
However, the authors are aware that the proposed approach requires considerable
effort to establish CS-based representations. Future work has to investigate on this
effort in order to further evaluate the potential contribution of the proposed approach.
Moreover, while overcoming issues introduced in Section 2, further issues remain.
For example, whereas defining instances, i.e. vectors, within a given CS appears to be
a straightforward process of assigning specific quantitative values to quality
dimensions, the definition of the CS itself is not trivial and dependent on individual
perspectives. Moreover, whereas semantics of instances are grounded to metrics
within a CS, the quality dimensions themselves are subject to ones interpretation what
might lead to ambiguity issues. Nevertheless, distance calculation relies on the fact
that resources are described in equivalent geometrical spaces. However, particularly
with respect to the latter, traditional ontology and schema matching methods could be
applied to align heterogeneous spaces. In addition, 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 (SWS) 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 SWS.
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