Bridging between Sensor Measurements and Symbolic
Ontologies through Conceptual Spaces
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 increasing availability of sensor data through a variety of sensor-driven
devices raises the need to exploit the data observed by sensors with the help of formally
specified knowledge representations, such as the ones provided by the Semantic Web. In order
to facilitate such a Semantic Sensor Web, the challenge is to bridge between symbolic
knowledge representations and the measured data collected by sensors. In particular, one needs
to map a given set of arbitrary sensor data to a particular set of symbolic knowledge
representations, e.g. ontology instances. This task is particularly challenging due to the
potential infinite variety of possible sensor measurements. Conceptual Spaces (CS) provide a
means to represent knowledge in geometrical vector spaces in order to enable computation of
similarities between knowledge entities by means of distance metrics. We propose an ontology
for CS which allows to refine symbolic concepts as CS and to ground instances to so-called
prototypical members described by vectors. By computing similarities in terms of spatial
distances between a given set of sensor measurements and a finite set of prototypical members,
the most similar instance can be identified. In that, we provide a means to bridge between the
real-world as observed by sensors and symbolic representations. We also propose an initial
implementation utilizing our approach for measurement-based Semantic Web Service
discovery.
Keywords: Sensor Data, Conceptual Spaces, Semantic Sensor Web, Vector
Spaces.
1 Introduction
Current and next generation wireless communication technologies will encourage
widespread use of well-connected sensor-driven devices which in fact produce sensor
data by observing and measuring real-world environments. This has already lead to
standardisation efforts aiming at facilitating the so-called Sensor Web, such as the
ones by the Sensor Web Enablement Working Group1 of the Open Geospatial
Consortium (OGC)2. The increasing availability of sensor data raises the need to
merge such data with formally specified knowledge representations, such as the ones
1 http://www.opengeospatial.org/projects/groups/sensorweb
2 http://www.opengeospatial.org/
provided by Semantic Web (SW) standards such as OWL [22] or RDF [23]. However,
whereas sensor data usually relies on measurements of perceptual characteristics to
describe real-world phenomena, ontological knowledge presentations represent real-
world entities through symbols. The symbolic approach – i.e. describing symbols by
using other symbols, without a grounding in perceptual dimensions of the real world –
leads to the so-called symbol grounding problem [2] and 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 perceptual level
[2][13].
In that, in order to facilitate the vision of the Semantic Sensor Web (SSW) [18] the
challenge is to bridge between formal symbolic knowledge representations and the
measured data collected by sensors by mapping a given set of arbitrary sensor data to
a particular set of symbolic representations. This task is particularly challenging due
to the potential infinite variety of possible data sets.
Conceptual Spaces (CS) [8] follow a theory of describing knowledge in
geometrical vector spaces which are described by so-called quality dimensions to
bridge between the perceived and the symbolic world. Representing instances as
vectors, i.e. members, in a CS provides a means to compute similarities by means of
spatial distance metrics. However, several issues still have to be considered when
applying CS. For instance, CS as well as sensor data provide no means to represent
arbitrary relations between data sets, such as part-of relations.
In order to overcome the issues introduced above, we propose a two-fold
knowledge representation approach which extends symbolic knowledge
representations through a refinement based on CS. This is achieved based on an
ontology which allows to refine symbolic concepts as CS and to ground instances to
so-called prototypical members, i.e. prototypical vectors, in the CS. The resulting set
of CS is formally represented as part of the ontology itself. By computing similarities
in terms of spatial distances between a given set of sensor measurements and the
finite set of prototypical members, the most similar instance can be identified. In that,
our approach provides a means to bridge between the real-world - as measured by
sensor data - and symbolic representations.
The remainder of the paper is organized as follows: Section 2 introduces the
symbol grounding problem in the context of sensor data, while our representational
approach based on CS is proposed in Section 3. In Section 4, we introduce an
implementation of our approach based on an existing SWS reference model and we
introduce first proof-of-concept prototype in Section 5. Finally, we discuss and
conclude our work in Section 6.
2 Sensor Data, Symbol Grounding and Spatial Representations
This section motivates our approach by introducing the so-called symbol grounding
problem in the context of the SSW and introduces some background knowledge on
metric-based spatial knowledge representation.
2.1. Sensor Data and the Symbol Grounding Problem
Sensor data usually consists of measurements which describe observations of
phenomena in real-world environments. In order to ensure a certain degree of
interoperability between heterogeneous sensor data, recent efforts, such as the
OpenGIS Observations and Measurements Encoding Standard (O&M)3, propose a
standardized approach to represent observed measurements based on a common XML
schema. However, in order to provide comprehensive applications capable of
reasoning in real-time on observed real-world phenomena, i.e. the contextual
knowledge produced by sensor-driven devices, one needs to bridge between the
measurements provided by sensors and the formally specified knowledge as, for
instance, exploited by the Semantic Web [18]. Figure 1 illustrates the desired
progression from observed real-world phenomena, e.g. a certain color, to
measurements provided by sensors, e.g. measurements of the hue, saturation and
lightness (HSL) dimensions, to symbolic knowledge entities such as a particular
OWL individual representing a specific color.
...
01010010100… {211; 169; 127}
11100010001… {228; 197, 8}
10001110100… {237; 177; 73}
...
Observed real-world Sensor-data based on measurements Ontological Knowledge
parameter (e.g. color) (e.g. HSL values) (e.g. OWL individual of particular color)
Fig. 1. Envisaged progression from real-world observations to ontological representations
through sensor data.
However, whereas sensor data usually relies on measurements of perceptual
characteristics to describe real-world phenomena, ontological knowledge
presentations represent real-world entities through symbols what leads to a
representational gap. Hence, 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 or perceptual dimensions what is known as the symbol
grounding problem [2] – of established SW representation standards, leads to
ambiguity issues and 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 perceptual level [2][13]. Moreover, describing the complex
notion of any specific real-world entity in all its facets through symbolic
representation languages is a costly task and may never reach semantic
meaningfulness.
Hence, in order to facilitate the vision of the SSW, the challenge is, to map a given
set of sensor observation data to semantic (symbolic) instances which most
appropriately represent the observed real-world entity within an ontology. In this
3 http://www.opengeospatial.org/standards/om
respect, it is particularly obstructive that a potentially infinite amount of real-world
phenomena, i.e. measurement data, needs to be mapped to a finite set of knowledge
representations, e.g. ontological concepts or instances.
2.2. Exploiting Measurements through spatial Knowledge Representations
Sensor data usually consists of sets of measurements being observed from the
surrounding environment. In that, spatially oriented approaches to knowledge
representation which exploit metrics to describe knowledge entities naturally appear
to be an obvious choice when attempting to formally represent sensor data.
Conceptual Spaces (CS) [8] follow a theory of describing entities in terms of their
quality characteristics similar to natural human cognition in order to bridge between
the perceived and the symbolic world. CS foresee the representation of concepts as
multidimensional geometrical Vector Spaces which are defined through sets of quality
dimensions. Instances are supposed to be represented as vectors, i.e. particular points
in a CS. For instance, a particular color may be defined as point described by vectors
measuring HSL or RGB dimensions. Describing instances as points within vector
spaces where each vector follows a specific metric enables the automatic calculation
of their semantic similarity by means of distance metrics such as the Euclidean,
Taxicab or Manhattan distance [11] or the Minkowsky Metric [19]. Hence, semantic
similarity is implicit information carried within a CS representation what is perceived
as one of the major contribution of the CS theory. Soft Ontologies (SO) [10] follow a
similar approach by representing a knowledge domain D through a multi-dimensional
ontospace A, which is described by its so-called ontodimensions. An item I, i.e. an
instance, is represented by scaling each dimension to express its impact, presence or
probability in the case of I. In that, a SO can be perceived as a CS where dimensions
are measured exclusively on a ratio-scale.
However, several issues have to be taken into account. For instance, CS as well as
SO do not provide any notion to represent any arbitrary relations [17], such as part-of
relations which usually are represented within symbolic knowledge models.
Moreover, it can be argued, that representing an entire knowledge model through a
coherent CS might not be feasible, particularly when attempting to maintain the
meaningfulness of the spatial distance as a similarity measure. In this regard, it is
even more obstructive that the scope of a dimension is not definable, i.e. a dimension
always applies to the entire CS/SO [17].
3 Grounding Ontological Concepts in Conceptual Spaces
We propose the grounding of ontologies in multiple CS in order to bridge between the
measurements provided by sensor-driven devices and symbolic representations of the
SW.
3.1. Approach: Spatial Groundings for Symbolic Ontologies
We claim that CS represent a particularly promising model when being applied to
individual concepts instead of representing an entire ontology in a single CS. By
representing instances as so-called prototypical members in CS, arbitrary sensor-data
can be associated with specific ontology instances in terms of the closest – i.e. the
most similar – prototypical member representation.
We propose a two-fold representational approach – combining SW vocabularies
with corresponding representations based on CS – to enable similarity-based
matchmaking between a given set of sensor data and ontological representations. 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. Instances of concepts are represented as prototypical
members in the respective CS. The following Figure 2 depicts this vision:
Ontology O1
Concept C1x
is-a is-a
refined-as-cs
Instance I1i Instance I1j
d1
refined-as-prototypical-member refined-as-prototypical-member
d2
d3
Conceptual Space CS1x
Fig. 2. Representing ontology instances through prototypical members in CS.
While benefiting from implicit similarity information within a CS, our hybrid
approach allows overcoming CS-related issues by maintaining the advantages of
ontology-based knowledge representations and provides a means to ground
knowledge entities to cognitive dimensions based on measurements. To give a rather
obvious example, a concept describing the notion of a geospatial location could be
grounded to a CS described through quality dimensions such as its longitude and
latitude. In previous work [3][4], we provided more comprehensive examples, even
for rather qualitative notions, such as particular subjects or learning styles.
Provided our refinement of ontology concepts as CS and of instances as
prototypical members, a given set of sensor data which measures the quality
dimensions of a particular CSi represents a vector v in CSi which can be mapped to an
appropriate ontology instance I in terms of the spatial distance of the prototypical
member of I and v. Figure 3 illustrates the approach based on the color example
introduced in Section 2.1. While measurements obtained from sensors are well-suited
to be represented as vectors, i.e. members, in a CS, we facilitate similarity-based
computation between a given set of sensor data and sets of prototypical members
which represent ontological instances. For instance, the example in Figure 3 depicts
the utilisation of a CS based on the HSL dimensions to map between color
measurements obtained through sensors and prototypical members representing
certain color instances. Based on the spatial distance between one measured color
vector and different prototypical members, the closest vector, i.e. the most similar
one, can be identified. In that, CS provide a means to bridge between observed sensor
data and symbolic ontological representations.
...
01010010100… {211; 169; 127} L
11100010001… {228; 197, 8}
S
H
10001110100… {237; 177; 73}
...
Sensor-data based on measurements Similarity-based mapping through Ontological Knowledge
(e.g. HSL values) Conceptual Color Space (e.g. OWL individual of particular color)
Fig. 3. Similarity-based mapping between distinct sets of sensor-based color measurements and
ontological color instances based on a common CS using the HSL dimensions.
3.2. A formal Ontology to represent Conceptual Spaces
In order to be able to refine and represent ontological concepts through CS, we
formalised the CS model into an ontology, currently being represented through
OCML [12]. Hence, a CS can simply be instantiated in order to represent a particular
concept.
Referring to [16][8], 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. Therefore, a CS
is defined by
CS n = {( p1d1, p2d 2 ,..., pnd n ) di ∈ CS , pi ∈ ℜ}
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. This
clearly 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 [16]. In this way, a CS may be composed of several subspaces and
consequently, the description granularity can be refined gradually. Dimensions may
be correlated. For instance, when describing an apple the quality dimension
describing its sugar content may be correlated with the taste dimension. Information
about correlation is expressed through axioms related to a specific quality dimension
instance.
A particular (prototypical) member M – representing a particular instance – in the
CS is described through valued dimension vectors vi:
M n = {(v1 , v 2 ,..., vn ) vi ∈ M }
With respect to [16], 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. Hence, with respect to [16], 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
[13] 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. Please note, as mentioned in Section 2.2,
different distance metrics could be applied depending on the nature and purpose of the
CS.
3.3. Representing Ontologies 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 aims at the creation of a CS
instance which most appropriately represents the real-world entity represented by Ci.
We particularly 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.
Given the formal ontological representation of the CS model (Section 3.2), we are
able to simply instantiate a specific CS by applying a transformation function
trans : C i ⇒ CS i
which is aimed at instantiating all elements of a CS, such as dimensions and
prominence values (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 ∈ PCi ⇒ ( pi1di1 , pi 2 di 2 ,..., pin din ) dij ∈ CSi , pij ∈ ℜ
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. Therefore, 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. The assignment of metric scales to dimensions (S2) which naturally are
described using quantitative measurements, such as size or weight, is rather
straightforward. In such cases, interval scale or ratio scale, could be used, whereas
otherwise, a nominal scale might be required. S3 is aimed at assigning a prominence
value pij – chosen from a predefined value range – to each dimension dij. Since the
assignment of prominences to quality dimensions is of major importance for the
expressiveness of the similarity measure within a CS, most probably this step requires
incremental ex-post re-adjustments until a sufficient definition of a CS is achieved.
With respect to S4, one has to represent all instances Iki 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 Iik as valued vectors
in CSi.
trans : {( piik1, piik 2 ,..., piikn ) piikl ∈ PI ik } ⇒ {(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. Please note that applying the procedure proposed here requires
additional effort which needs to be further investigated within future work.
4 Implementation - Exploiting Sensor Data for Semantic Web
Service Discovery
In previous work [3][4], we applied our two-fold approach to Semantic Web Services
(SWS) technology [6] which aims at the automated discovery, orchestration and
invocation of Web services based on comprehensive semantic annotations of services.
Current results of SWS research are available in terms of reference models such as
OWL-S [14], SAWSDL4 or WSMO [24]. In [3][4], our CS representation was
deployed to refine instances which are part of SWS annotations in order to enable
interoperability between heterogeneous SWS and SWS requests. In contrast, here we
propose the utilization of our CS-based representational approach to facilitate
interoperability between observations and measurements provided by sensors and
symbolic SWS representations based on extensions which are described in this
section.
The representational model described above had been implemented by and aligned
to established SWS technologies based on WSMO [24] and the Internet Reasoning
Service IRS-III [1]. Further details on the IRS-III Service Ontology and its extension
through our CS formalisation can be found in [5]. However, please note that in
principle the representational approach described above could be applied to any SWS
reference model and is particularly well-suited to support rather light-weight
approaches such as SAWSDL or WSMO Lite [21].
4 http://www.w3.org/2002/ws/sawsdl/spec/
In order to facilitate the representational approach described in Section 3, we
aligned the CS Ontology (Section 3.2) with the IRS-III Service Ontology to allow for
the refinement of individual concepts – 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.
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:Prototypical Member refined-as irs:Instance uses irs:Goal
Fig. 4. Core concepts of the CS Ontology aligned to the IRS-III Service Ontology.
Figure 4 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 3.3, service capabilities are refined in multiple CS. To
take into account the representational gap between measurement data as provided by
sensors and symbolic SWS goal representations, we introduced a novel way of
requesting goal achievements through IRS-III. Instead of simply invoking a goal by
providing the goal achievement request SWSi, including the actual input data, we also
foresee the on-the-fly provisioning of underlying assumptions in terms of sets of
measurements, i.e. vectors {V1, V2,…, Vn}, which in fact describe the actual contextual
environment of the request.
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 functionalities:
F1. Instantiation of member Mi in CSO for each Vi provided as part of SWSi
F2. Similarity computation between goal request SWSi and potentially relevant
SWS
Given the ontological refinement of SWS descriptions into CS as introduced in
Section 3.2 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 goal 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 3.2. 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, prototypical members
M(SWSj) – which refine capabilities of SWSj and are represented in one of the
conceptual spaces 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 prototypical member of all M(SWSj) which is represented
in the same space MSj as vl are computed. In case one set of prototypical 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
Dist(SWSi,SWSj) contains a set of distances {dist1..distn} where 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
∑ (distk )
(
Sim( SWSi , SWS j ) = Dist ( SWSi , SWS j ) )−1
=
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.
5 Application: Measurement-based SWS discovery of Weather
Forecast Web Services
Our measurement-based SWS discovery approach (Section 4) was actualised within
an initial proof-of-concept prototype application which mediates between different
weather forecast Web services. This example use case illustrates how measurements
can be dynamically mapped to symbolic representations, SWS in this case, by means
of similarity-computation within CS.
Here, SWS1, SWS2 and SWS3 provide weather forecast information for different
locations. Each service has distinct constraints, and thus distinct SWS descriptions. In
detail, SWS1 is able to provide forecasts for France and Spain while SWS2 and SWS3
are providing forecasts for the United Kingdom. All services show different Quality
of Service (QoS) parameters. Three distinct service ontologies O1, O2, and O3 had
been created, each defining the capability of the respective service by using distinct
vocabularies. For example, SWS2 considers concepts representing the notions of
location and QoS together with corresponding instances (see also Table 1):
{(country , QoS ), (UK , QoS 2)} ⊂ O2 ⊂ SWS 2
By applying the representational approach proposed in Section 3, each concept of the
involved heterogeneous SWS representations had been refined as a shared CS, while
instances - defining the capabilities of available SWS - were defined as prototypical
members. For example, a simplified CS (CS1: Location Space in Figure 5) was
utilized to refine geographical notions (e.g. country) by using two dimensions
indicating the geospatial position of the location:
{( p1l1 , p 2 l2 )} = {(latitude , longitude )} = CS 1
The two dimensions latitude and longitude are equally ranked, and hence, a
prominence value of 1 has been applied to each dimension. Note that each of the
depicted concepts and instances, such as O2:UK and O3:UK, are distinct and
independent from each other, and thus might show heterogeneities, such as distinct
labels, for instance United Kingdom and Great Britain. In the case of O2:UK and
O3:UK, these two instances are refined by two distinct prototypical members:
L1 (SWS 2 ) = {(v1 = 55.378051, v2 = -3.435973) vi ∈ CS1 } and
L1 (SWS3 ) = {(v1 = 55.378048, v2 = -3.435963) vi ∈ CS1 }. Each member has been defined by
different individuals applying similar, but non-equivalent geodata.
In addition, a second space (CS2: QoS Space in Figure 5) has been defined by three
dimensions – latency (in ms), throughput (number of Web services), availability (in
%): {( p1 r1 , p 2 r2 , p 3 r3 )} = {(latency , throughput , availabili ty )} = CS 2
SWS Ontology O1 SWS Ontology O2
O1:Country O1:QoS O2:Country O2:QoS
is-a is-a is-a is-a
O1:France O1:QoS-1 O2:UK O2:QoS-2
CS1 Location Space CS2 QoS Space
SWS Ontology O3 SWS Request Ontology O4
O3:Country O3:QoS O4:City O4:QoS
is-a is-a is-a is-a
O3:UK O3:QoS-3 O4:Toulouse O4:QoS-4
Fig. 5. Grounding assumptions of distinct weather forecast SWS to common CS.
Potential service consumers define a goal (e.g. SWS4 in Figure 5) together with the set
of input parameters and the underlying assumptions in terms of measurements. After
accomplishment of F.1, i.e. the dynamic instantiation of members in their
corresponding CS to represent the sensor data provided with the actual goal request
SWS4, all involved goals and SWS were grounded in the same set of CS as depicted in
Figure 5.
In that, assumptions of available SWS had been described independently in terms
of simple conjunctions of instances which were individually refined in shared CS as
shown in Table 1. As shown in Table 1, the request SWS4 assumes a SWS which
provides weather forecast for the location UK (L1(SWS4)) and ideal QoS (Q1(SWS4))
demanding zero latency but high throughput and availability.
Table 1. Assumptions of involved SWS and SWS requests described in terms of vectors in MS1
and MS2.
Assumption
Ass SWSi = ( L1SWSi ∪ L2 SWSi ∪ .. ∪ LnSWSi ) ∪ (Q1SWSi ∪ Q2 SWSi ∪ .. ∪ QmSWSi )
Members Li in CS1 (locations) Members Cj in CS2 (QoS)
L1(SWS1)={(46.227644, 2.213755)}
SWS1 Q1(SWS1)={(155, 2, 91)}
L2(SWS1)={(40.463667, -3.74922)}
SWS2 L1(SWS2)={(55.378051, -3.435973)} Q1(SWS2)={(15, 50, 98)}
SWS3 L1(SWS3)={(55.378048, -3.435963)} Q1(SWS3)={(78, 5, 95)}
SWS4 L1(SWS4={(55.378048, -3.435963)} Q1(SWS4)={(0,100,100)}
Though no exact SWS matches these criteria, at runtime similarities are calculated
between SWS4 and the related SWS (SWS1, SWS2, SWS3) through the similarity-based
discovery function described in Section 4. This led to the calculation of the following
similarity values:
Table 2. Automatically computed similarities between SWS request SWS4 and available SWS.
Similarities
SWS1 0.010290349
SWS2 0.038284954
SWS3 0.016257476
Given these similarities, our introduced goal achievement method automatically
selects the most similar SWS (i.e. SWS2 in the example above) and triggers its
invocation.
6 Discussion and Conclusions
In order to contribute to the vision of the SSW, i.e. the convergence of sensor data and
formal knowledge representations as part of the Semantic Web, we proposed a
representational model which grounds ontological representations in CS to overcome
the symbol grounding problem. The latter is perceived to be as one of the major
obstacles towards the SSW. While ontological instances are represented as
prototypical members within a CS, arbitrary sensor data which measures the
dimensions of the CS can be associated with the most appropriate instance by
identifying the most similar, i.e. the closest, prototypical member to the vector which
represents the sensor data. Our approach is facilitated through a dedicated CS
Ontology which allows to refine any arbitrary concept (instance) as CS (prototypical
member). In that, our representational model allows to bridge between sensor
measurements and symbolic knowledge representations by means of similarity
computation between vectors within CS.
In addition, we implemented our approach by applying it to the field of SWS and
utilising it for measurement-based SWS discovery while bridging between symbolic
SWS representations and sensor-based measurement data. Therefore, we extended the
matchmaking algorithm of an existing SWS Broker, IRS-III, with new capabilities
allowing for measurement-based matchmaking based on our two-fold representational
model. A first proof-of-concept prototype application utilises our approach to enable
measurement-based discovery of weather forecast Web services based on measured
parameters such as the geospatial location and the service QoS.
The proposed approach has the potential to further support interoperability between
heterogeneous sensor data and symbolic knowledge representations. While our
approach supports automatic mapping between ontology instances and sensor-based
measurements it still requires a common agreement on shared CS. In addition,
incomplete similarities are computable between partially overlapping CS.
However, the authors are aware that our 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. 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, such as DOLCE [9] or SUMO [15], and emergence of common schemas
for sensor data such as the OpenGIS Observations and Measurements Encoding
Standard, leads to an increased sharing of ontologies at the concept level. As a result,
our proposed hybrid representational model becomes increasingly applicable by
further contributing to the vision of the SSW.
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