=Paper=
{{Paper
|id=Vol-416/paper-2
|storemode=property
|title=Semantic Web Service Selection with SAWSDL-MX
|pdfUrl=https://ceur-ws.org/Vol-416/paper1.pdf
|volume=Vol-416
|dblpUrl=https://dblp.org/rec/conf/semweb/KluschK08
}}
==Semantic Web Service Selection with SAWSDL-MX==
https://ceur-ws.org/Vol-416/paper1.pdf
Semantic Web Service Selection with
SAWSDL-MX
Matthias Klusch and Patrick Kapahnke
German Research Center for Artificial Intelligence
Stuhlsatzenhausweg 3, Saarbrücken, Germany
klusch@dfki.de, patrick.kapahnke@dfki.de
Abstract. In this paper, we present an approach to hybrid semantic
Web service selection of semantic services in SAWSDL based on logic-
based matching as well as text retrieval strategies. We discuss the princi-
ples of semantic Web service description in SAWSDL and selected prob-
lems for service matching implied by its specification. Based on the re-
sult of this discussion, we present different variants of hybrid semantic
selection of SAWSDL services implemented by our matchmaker called
SAWSDL-MX together with preliminary results of its performance in
terms of recall/precision and average query response time. For experi-
mental evaluation we created a first version of a SAWSDL service re-
trieval test collection called SAWSDL-TC.
1 Introduction
As a W3C recommendation dated August 28, 2007, the SAWSDL1 specifica-
tion proposes mechanisms to enrich Web services described in WSDL2 (Web
Service Description Language) with semantic annotations. However, there is no
SAWSDL semantic service matchmaker publicly available to the community yet.
To fill this gap, we initially adopt the ideas of semantic Web service matching
of our hybrid matchmakers OWLS-MX and WSMO-MX (see [8, 6]), for service
description languages OWL-S3 and WSML respectively, to this environment.
A detailed discussion of the SAWSDL specification, particularly addressing the
problems arising for semantic Web service selection, is also given.
In this paper, we present the first version of our hybrid SAWSDL Web service
matchmaker called SAWSDL-MX. It exploits both crisp logic-based matching
(subsumption reasoning) and IR-based (text retrieval) matching. Our prelimi-
nary experimental analysis shows, that in line with OWLS-MX and WSMO-MX,
hybrid matching can outperform both variants applied stand-alone in terms of
recall and precision.
The remainder of this paper is structured as follows. After a brief introduction
to SAWSDL and discussion of implied challenges of semantic service selection in
1
http://www.w3.org/TR/sawsdl/
2
http://www.w3.org/TR/wsdl/ and http://www.w3.org/TR/wsdl20/
3
http://www.daml.org/services/owl-s/1.1/
section 2, the hybrid matching approach of SAWSDL-MX is described in detail
in section 3. Section 4 presents the architecture and implementation details of
SAWSDL-MX. Preliminary results of our experimental evaluation of SAWSDL-
MX over a initial test collection SAWSDL-TC1 in terms of recall, precision and
average query response time are shown in 5. We comment on related work in
section 6 and conclude in section 7.
2 SAWSDL Services
In the following, a brief introduction of the semantically enabled service descrip-
tion language SAWSDL is given. Language specific problems for semantic service
discovery arising from the W3C recommendation and methods of resolution and
assumptions for avoiding them respectively are also discussed.
SAWSDL is designed as extension of WSDL enabling service providers to
enrich their service descriptions with additional semantic information. For this
purpose, the notion of model reference and schema mapping have been introduced
in terms of XML attributes that can be added to already existing WSDL elements
as depicted in figure 1. More precisely, the following extensions are used for
annotation:
Fig. 1. SAWSDL extensions of WSDL interface components
– modelReference: A modelReference points to one ore more concepts with
equally intended meaning expressed in an arbitrary semantic representation
language. They are allowed to be defined for every WSDL and XML Schema
element, though the SAWSDL specification defines their occurrence only
in WSDL interfaces, operations, faults as well as XML Schema elements,
complex types, simple types and attributes. The purpose of a model reference
is mainly to support automated service discovery.
– liftingSchemaMapping: Schema mappings are intended to support automated
service execution by providing rules specifying the correspondences between
semantic annotation concepts defined in a given ontology (the ”upper” level)
to the XML Schema representation of data actually required to invoke the
Web service using SOAP (the ”lower” level), and vice versa. A liftingSchema-
Mapping describes the transformation from the ”lower” level in XML Schema
up to the ontology language used for semantic annotation.
– loweringSchemaMapping: The reference tag loweringSchemaMapping des-
cribes the transformation from the ”upper” level of a given ontology to the
”lower” level in XML Schema.
Since the specification of SAWSDL does not restrict the developer of a seman-
tic service in SAWSDL to a particular ontology language, any service selection
has to cope with the implied semantic interoperability problem of both heteroge-
neous ontologies and heterogeneous ontology languages. Therefore, as an initial
starting point, we restricted our inital SAWSDL service matchmaker to ”under-
stand” only the standard OWL4 . More concrete, we assume for SAWSDL-MX
1.0 that model references in SAWSDL service offers and requests are pointing
to ontological concepts exlcusively defined in OWL-DL. That allows to apply
standard subsumption reasoning used for OWL-S matchmaking such as in [14,
4, 8]. Besides, there is no retrieval test collection for SAWSDL publicly available
yet, but for OWL-S, namely OWLS-TC, which we converted semi-automatically
into SAWSDL services such that we could use the resulting SAWSDL-TC for
initially evaluating our matchmaker.
Another problem with the SAWSDL specification with respect to service
matching is that so-called top-level annotation and bottom-level annotation are
defined as to be considered independent from each other. The term top-level
annotation describes the case, where a complex type or element definition of a
message parameter is described by a model reference as a whole. A bottom-level
annotation pursues the idea of semantically annotating the parts that are con-
tained inside the definition of a complex type or element. However, it remains
unclear how to evaluate matching between top-level and low-level annotated pa-
rameters, or which one to prefer if both levels are available. To circumvent this
problem, we decided to rely on top-level annotations of upper parameter type
definitions, and ignore bottom-level annotation in the first version of our match-
maker. In addition to that, element and type definition specifying a message
component can be annotated at the same time. The specification does not imply
a solution for this case either, so we decided to rely on the annotation directly
attatched to the referenced XML Schema object if available.
Further, multiple references to multiple ontologies defined in different lan-
guages and formats such as logic theories, plain text documents or structured
4
http://www.w3.org/2004/OWL/
Fig. 2. SAWSDL service example
thesauri can be used to describe the semantic of even the same element. There-
fore, a matchmaker, in principle, cannot know whether these different types
of semantic descriptions of the element are intended to be treated as comple-
mentary or equivalent. In the first case, how to aggregate the complementing
descriptions, in the latter case, which one to select best for further processing?
This opens up a wide range of pragmatic approaches to deal with this for service
matching. SAWSDL-MX 1.0 checks only the first model reference of an element.
However, different variants dealing with multiple model references connected to
a single object are topic of further development, since they are to be treated as
sets without order. One possible approach would be to check every combination
of request and service offer reference part and perform some kind of aggregation
afterwards.
To illustrate this problem by example, consider figure 2: A flight company
offers a WSDL Web service with different operations concerning flight booking
(BookFlight operation), account administration (omitted in the picture), and
so on. The BookFlight operation is defined to take information of the desired
flight (Flight input) and customer information in form of a tuple containing a
user name and appropriate password (Customer input) as input parameters and
delivers information about the ticket reservation (Ticket output).
To support automated Web service selection, this service is semantically an-
notated in compliance with the SAWSDL specification as shown in the figure.
In particular, the service developer of the flight company uses WSML-Core,
WSML-Rule and OWL-DL concept descriptions for service element annotation.
As a consequence, a matchmaker agent cannot perform single language-specific
reasoning and matching mechanisms but has to apply an appropriate combina-
tion of them instead. This problem can be straight-forwardly solved by use of
language mappings available for WSML-Core and OWL-DL5 but remains hard
to solve for comparing concepts in WSML-Rule and OWL-DL.
Further, in the example, the XML Schema description attached to the service
input element Customer contains annotations for the compound complex type
as well as the simple types (referenced by the elements contained in the complex
type, element nodes are omitted in the picture). How to handle this situation?
Selecting only one annotation level may neglect additional information while
looking at all references as a conjunction of ontological concepts can lead to
either logical inconsistencies, or is not possible due to incomparable description
languages. This problem is exaggerated in the example by providing multiple
references (multiple levels of annotations) for the same element. SAWSDL-MX
1.0 only checks the top-level annotation of the most generic element of the XML
Schema description of a service parameter.
3 Service Matching with SAWSDL-MX
In the following, we describe one approach to SAWSDL-service selection which
we implemented in an initial version of a matchmaker called SAWSDL-MX based
on the assumptions stated above. SAWSDL-MX performs service selection in
terms of logic-based, syntactic (text similarity-based) and hybrid matching of
I/O parameters defined for potentially multiple operations of a Web service
interface (signature matching)6 . As service requests, standard SAWSDL Web
service definition documents are used. This approach is particularly inspired by
the hybrid semantic service matchmakers OWLS-MX [8] and WSMO-MX [6] for
OWL-S and WSML.
3.1 Service Interface Matching
The matching process of SAWSDL-MX on the service interface level is performed
as follows. For every pair of service offer O and service request R, every com-
bination of their operations is evaluated by either logic-based matching, text
5
http://www.wsmo.org/TR/d16/d16.1/v0.21/
6
For SAWSDL-MX 1.0, we assume only one interface but multiple operations per
service. Extending the proposed service matching algorithm to services with even
multiple interfaces only requires additionally combined valuation of the respective
interface matching results. The restriction to signature matching for SAWSDL-MX
1.0 is due to the fact that, in SAWSDL, preconditions and effects can be added as
input and output model references only, which makes it hard for any matchmaker to
identify them as such in general, and before actually analyzing the name and content
of referenced models in particular.
retrieval-based matching, or both. The matching of operations is described in
more detail later.
In order to compute an optimal injective mapping of operations for service
offer and request, SAWSDL-MX applies bipartite graph matching, where nodes
in the graph represent the operations and the weighted edges are built from
possible one-to-one assignments with their weights derived from the computed
degree of operation match. If there exists such a mapping, then it is guaranteed
that there exists an operation provided by the service offer for every operation
a requester defined in her query. That is, there exists no request operation that
cannot be provided by the service offer, disregarding the quality of match at this
point.
As an example, consider the service request and service offer given in figure
3. Every request operation ROi (with i ∈ {1, 2}) is compared to every advertise-
ment operation Oj (with j ∈ {1, 2, 3}) with respect to logic-based filters defined
in the next section. In this example, RO1 exactly matches with O1 , but fails for
O2 and O3 . O3 is a weaker plug-in match for RO2 (the subsumed-by match of
RO2 with O2 is even weaker than a plug-in match). The best (max) assignment
of matching operations is {hRO1 , O1 i, hRO2 , O3 i}.
Fig. 3. Interface level matching of SAWSDL-MX
One conservative (min-max) option of determining the matching degree be-
tween service offer and request based on their pairwise operation matchings
is to assume the worst result of the best operation matchings, to guarantee
a fixed lower bound of similarity for every requested operation. This is what
SAWSDL-MX 1.0 is doing, so in this example shown in figure 3, the service offer
is considered a plug-in match for the request. Other possibilities are to merge the
operation matching results based on, for example, their average syntactic simi-
larity values, and to provide more detailed feedback to the user on the operation
matchings involved.
Please note that SAWSDL-MX aims at finding service matches solely based
on single service offer documents. The problem of semantic Web service com-
position is somehow related, but additional state-based planning strategies have
to be applied to solve this problem, which is out of the scope of this work. To
accomplish on that, a Web service composition planner like e.g. OWLS-XPlan
or SHOP2 could be considered (see [18, 19] for details).
3.2 Logic-based Operation Matching
As mentioned above, we assume for SAWSDL-MX 1.0 that model references in
SAWSDL service offers and requests are pointing to ontological concepts exlcu-
sively defined in OWL-DL or WSML-DL. That allows to apply standard sub-
sumption reasoning for description logics (see [20]). Therefore, the logic-based
operation matching part of SAWSDL-MX computes the degree of logic-based
match for a given pair of service offer operation OO and service request OR by
successively applying four filters of increasing degree of relaxation: Exact, Plug-
in, Subsumes and Subsumed-by, which are, in essence, adopted from those of
OWLS-MX 2.0 but modified in terms of an additional bipartite concept matching
to ensure an injective mapping between offer and request concepts, if required.
The reason of this modification is that previous experiments with OWLS-MX
showed that many logic-based only failures could have been avoided by this
additional constraint.
Exact match: Service operation OO exactly matches service operation OR ⇔
(∃ injective assignment Min : ∀m ∈ Min : m1 ∈ in(OO ) ∧ m2 ∈ in(OR ) ∧ m1 ≡
m2 ) ∧ (∃ injective assignment Mout : ∀m ∈ Mout : m1 ∈ out(OR ) ∧ m2 ∈
out(OO ) ∧ m1 ≡ m2 ). There exist a one-to-one mapping of perfectly matching
inputs as well as perfectly matching outputs. Assuming that an operation fullfills
a requesters need if every input can be satisfied and every requested output is
provided, the assignments only require to be injective (but not bijective), thus
additional available information not required for service invocation and addi-
tional provided outputs not explicitly requested are tolerated.
Plug-in match: Service operation OO plugs into service operation OR ⇔ (∃
injective assignment Min : ∀m ∈ Min : m1 ∈ in(OO )∧m2 ∈ in(OR )∧m1 w m2 )∧
(∃ injective assignment Mout : ∀m ∈ Mout : m1 ∈ out(OR ) ∧ m2 ∈ out(OO ) ∧
m2 ∈ lsc(m1 )). The filter relaxes the constraints of the exact matching filter by
additionally allowing input concepts of the service offer to be arbitrarily more
general than those of the service request, and advertisement output concepts to
be direct child concepts of the queried ones.
Subsumes match: Service operation OO subsumes service operation OR ⇔
(∃ injective assignment Min : ∀m ∈ Min : m1 ∈ in(OO ) ∧ m2 ∈ in(OR ) ∧ m1 w
m2 ) ∧ (∃ injective assignment Mout : ∀m ∈ Mout : m1 ∈ out(OR ) ∧ m2 ∈
out(OO ) ∧ m1 w m2 ). This filter further relaxes constraints by allowing service
offer outputs to be arbitrarily more specific than the request outputs (as opposed
to the plug-in filter, where they have to be direct children). Thus, a plug-in can
be seen as special case of a subsumes match resulting in a more fine-grained view
at the overall service ranking.
Subsumed-by match: Service operation OO is subsumed by service opera-
tion OR ⇔ (∃ injective assignment Min : ∀m ∈ Min : m1 ∈ in(OO ) ∧ m2 ∈
in(OR ) ∧ m1 w m2 ) ∧ (∃ injective assignment Mout : ∀m ∈ Mout : m1 ∈
out(OR )∧m2 ∈ out(OO )∧m2 ∈ lgc(m1 )). The idea of the subsumed-by matching
filter is to determine the service offers that the requester is able to provide with
all required inputs and at the same time deliver outputs that are at least closely
related to the requested outputs in terms of the inferred concept classification.
At this filtering step, services that offer equivalent or more specific outputs
already have been discovered. The subsumed-by filter additionally returns service
offers that provide more general output concepts, namely direct parents. These
may be of value for a user to know, though it depends on the granularity of the
matchmaker ontology. For example, it would not make sense to return a ser-
vice operation providing information on vehicles, if the user explicitly requested
information on a very special brand of a car which concept is inappropriately
modelled as a direct child of the concept vehicles in the ontology.
The overall algorithm for logic-based matching of operations considers the
filters in the following order based on the degree of relaxation: exact > plug-in >
subsumes > subsumed-by > fail. The notion of fail applies to cases where none
of the filtering tests succeeded.
3.3 Syntactic Operation Matching
In addition, SAWSDL-MX can perform syntactic-based matching based on se-
lected token-based text similarity measures. That is, a syntactic similarity value
is computed for every pair of service offer and request operation which is used to
rank operations with same logic-based matching degree. The implemented simi-
larity measures for SAWSDL-MX 1.0 are the same as for OWLS-MX, that are the
Loss-of-Information, the Extended Jaccard, the Cosine and the Jensen-Shannon
similarity measures. The architecture of SAWSDL-MX allows the integration of
other text similarity measures such as those provided by SimPack7 which is also
used in the iMatcher matchmaker [7].
The weighted keyword vectors of inputs and outputs for every operation are
generated by first unfolding the referenced concepts in the ontologies (as defined
for standard tableaux reasoning algorithms). The resulting set of primitive con-
cepts of all input concepts of a service operation is then processed to a weighted
keyword vector based on TFIDF weighting scheme, the same is done with its
output concepts. The text similarity of a service offer operation and a request
operation is the average of the similarity values of their input and output vectors
according to the selected text similarity measure.
7
http://www.ifi.uzh.ch/ddis/research/semweb/simpack/
3.4 Hybrid Operation Matching
Inspired by OWLS-MX [8], SAWSDL-MX combines logic-based and syntactic-
based matching to perform hybrid semantic service matching. There are different
options of combination: A compensative variant using syntactic similarity mea-
sures in cases where none of the logic-based filters applies helps to improve
the service ranking with respect to logic-based false negatives by re-considering
them again in the light of their computed syntactic similarity. An integrative
variant deals with problems concerning logic-based false positives by not taking
the syntactic similarity of concepts into account only when a logical matching
fails, but as a conjunctive constraint in each logical matching filter. Our ex-
periments showed that OWLS-MX 2.0 using the integrative variant performs
better than the original one with the complementary use of syntactic similarity.
However, SAWSDL-MX 1.0 inherited the compensative variant from OWLS-MX
1.0, that is, only the logic-based subsumed-by filter is modified to a hybrid fil-
ter by integrative checking of syntactic simliarity of concepts, and the syntactic
nearest-neighbour filter is compensative in the sense that it is only performed in
case all other filters fail.
Subsumed-by match: Service operation OO is subsumed by service opera-
tion OR ⇔ (∃ injective assignment Min : ∀m ∈ Min : m1 ∈ in(OO ) ∧ m2 ∈
in(OR ) ∧ m1 w m2 ) ∧ (∃ injective assignment Mout : ∀m ∈ Mout : m1 ∈
out(OR ) ∧ m2 ∈ out(OO ) ∧ m2 ∈ lgc(m1 )) ∧ simIR (OR , OO ) ≥ α. A subsumed-by
match computed by hybrid matching additionally requires the IR-based similar-
ity computed using one of the measures from IR = {LOI, ExtJacc, Cos, JS} to
be above a given threshold α. This helps to avoid logic-based false positives to
be introduced by the pure logic-based variant of this filter.
Nearest-neighbour match: This filter compensates logic-based false nega-
tives as described above. Its condition is simIR (OR , OO ) ≥ α and thus considers
all services not already catched in previous filter steps whose IR-based similarity
is above the threshold.
4 SAWSDL-MX Implementation
SAWSDL-MX 1.0 has been fully implemented in Java using the sawsdl4j8 API
(handling SAWSDL for WSDL 1.1) and the OWL API9 for access to SAWSDL
and OWL files, the DIG 1.110 as standard interface to handle SHOIQ knowl-
edge base queries, and the Pellet11 reasoner as inference engine for logic-based
matchmaking.
Figure 4 gives an broad overview of the overall system architecture. Basically,
SAWSDL-MX consists of the following components: SAWSDL Matching Engine,
Service Registry, Ontology Handlers, Local Matchmaker Ontology and Similarity
8
http://knoesis.wright.edu/opensource/sawsdl4j/
9
http://owlapi.sourceforge.net/
10
http://dig.sourceforge.net/
11
http://pellet.owldl.com/
Measures. These are described in more detail in the following. From the perspec-
tive of service providers, SAWSDL-MX allows the registration of SAWSDL Web
service offers at the service registry. For requesters, SAWSDL-MX provides an
interface for submitting queries by means of a SAWSDL document specifying
details about the desired service interface. After the service discovery process,
the SAWSDL-MX matching engine returns a ranked list of service offers that
match the query.
Fig. 4. SAWSDL-MX architecture
SAWSDL Matching Engine: The SAWSDL Matching Engine is the core
component of SAWSDL-MX. It provides several matching variants of SAWSDL-
MX 1.0 as described in previous sections: The Logic-based Matcher computes
service ranking by means of crisp-logic subsumption reasoning and the logic-
based matching filters described in section 3.2. The IR-based Matcher produces
the ranked results using syntactic similarity measures as described in section
3.3. Finally, the Hybrid Matcher performs the combined approach of logic-based
reasoning and syntactic similarity comparison as described in 3.4. The matching
engine component is designed to provide easy integration of additional matching
variants by means of Java interface implementation.
Service Registry: This component is the storage for service offers provided
by service providers. It is accessed by the matching engine to produce the ranked
results for a query.
Ontology Handlers: After the service registration process, the semantic
annotations of a SAWSDL service (by means of model references) are processed
using Ontology Handlers. Therefore, an appropriate handler able to parse and
reason about the referenced ontology is selected and the concepts are stored lo-
cally to facilitate logic-based reasoning as well as concept unfolding for IR-based
matching at query time. As for the matching engine component, the Ontology
Handlers package is designed to allow the proper integration of additional knowl-
edge representation formalisms by means of Java interfaces.
Local Matchmaker Ontology: This component is in fact part of the ontol-
ogy handlers in the actual implementation but depicted as seperate component
for reasons of clarity. The Local Matchmaker Ontology is a storage for all relevant
concepts referenced by registered service offers as proposed in [8]. However, since
SAWSDL allows the use of various knowledge representation formalisms, parts of
the component relevant for certain ontology handlers are directly covered inside
the handlers. In case of our current implementation of SAWSDL-MX, it consists
of the Pellet reasoner, which is accessed by handlers able to process descrip-
tion logic based ontology languages via DIG 1.1. Currently, only the OWL-DL
Handler is actually implemented, but expanding the system to WSML-DL is
straight-forward, since they rely on subsets of the SROIQ description language,
which is addressed by Pellet12 .
Similarity Measures: This package currently contains the four similar-
ity measures loss-of-information, extended Jaccard, cosine and Jensen-Shannon.
However, adding more variants for IR-based matching can be easily accomplished
again via interfaces. An proprietary document indexing structure based on hash
tables is also provided. The integration of additional syntactic similarity mea-
sures (e.g. from SimPack) and better indexing strategies is intended for following
versions of SAWSDL-MX.
5 Evaluation of Performance
The experimental evaluation of the retrieval performace of the first version
SAWSDL-MX focuses on measuring its recall and precision based on a first
SAWSDL test collection semi-automatically derived from OWLS-TC 2.213 us-
ing the OWLS2WSDL14 tool, as there is currently no standard test collection for
SAWSDL matchmaking available. OWLS2WSDL transforms OWL-S service de-
scriptions (and concept definitions relevant for parameter description) to WSDL
through syntactic transformation. The collection consists of 894 Web services
covering different application domains: education, medical care, food, travel,
communication, economy and weaponry. For this set of service offers, 26 queries
have been selected and relevance sets have been created for each of them. These
where subjectively defined as relevant according to the standard TREC defini-
tion of binary relevance [16]. As the creation of this test collection has been done
12
With exception of n-ary datatypes
13
http://projects.semwebcentral.org/projects/owls-tc/
14
http://projects.semwebcentral.org/projects/owls2wsdl/
by transforming OWL-S services contained in OWLS-TC 2.2, which provides
services containing only one atomic process per description, every SAWSDL ad-
vertisement only contains a single interface with a single operation (but possibly
multiple I/O’s). Therefore and because all automatically derived model refer-
ences exclusively point to OWL ontologies, this test collection can only be seen
as a first attempt towards a commonly agreed testing environment for SAWSDL
service discovery and our evaluation has to be considered as preliminary. The
performance measures used for evaluation are defined as follows:
|A ∩ B| |A ∩ B|
Recall = , P recision = ,
|A| |B|
where A is the set of all relevant documents for a request and B the set of
all retrieved documents for a request. The so-called F1-measure equally weights
recall and precision and is defined as:
(2 · P recision · Recall)
F1 = .
(Recall + P recision)
We adopt the prominent macro-averaging of precision. That is, we compute
the mean of precision values for answer sets returned by the matchmaker for
all queries in the test collection at standard recall levels Recalli (0 ≤ i < λ).
Ceiling interpolation is used to estimate precision values that are not observed
in the answer sets for some queries at these levels; that is, if for some query
there is no precision value at some recall level (due to the ranking of services
in the returned answer set by the matchmaker) the maximum precision of the
following recall levels is assumed for this value. The number of recall levels from
0 to 1 (in equidistant steps nλ , n = 1 . . . λ) we used for our experiments is λ = 20.
Thus, the macro-averaged precision is defined as follows:
1 X
P recisioni = × max{Po |Ro ≥ Recalli ∧ (Ro , Po ) ∈ Oq },
|Q|
q∈Q
where Oq denotes the set of observed pairs of recall/precision values for query
q when scanning the ranked services in the answer set for q stepwise for true
positives in the relevance sets of the test collection. For evaluation, the answer
sets are the sets of all services registered at the matchmaker which are ranked
with respect to their (totally ordered) matching degree.
The performance tests have been conducted on a machine with Windows
2000, Java 6, 1,7 GHz CPU and 2 GB RAM using SME2 15 as evaluation envi-
ronment.
As can be seen in figure 5(a), the hybrid variant utilizing cosine measure
performs best in both finding correct results among the top of the ranking as
well as returning positives at high precision towards full recall. It is followed by
pure IR-based service discovery (also using cosine measure), which is surpris-
ingly at first glance, since it is assumed by the semantic Web community that
15
http://projects.semwebcentral.org/projects/sme2/
(a) recall/precision (b) F1
Fig. 5. Performance of SAWSDL-MX
semantically enabled ressource retrieval should be able to outperform standard
information retrieval purely relying on syntactic information in general. How-
ever, as Wang et al. show in [17] exemplarily for OWL, the currently established
Web ontology landscape provides mainly poor specification of concepts in terms
of the used expressivity of description languages. In fact, many ontologies cur-
rently available are just simple taxonomies that do not rely on advanced features
provided by for example OWL-DL, thus IR-based matching techniques are often
good enough to compare service parameters. The crisp logic-based variant of
SAWSDL-MX performs worst with respect to precision. This is mainly due to
the problem with ontologies just described and due to the coarse-grained con-
cept descriptions available. Equal consideration of recall and precision using the
F1 measure yields the results given in figure 5(b), which recapitulates the ob-
servations. Regarding query response times, IR-based matching performs best,
namely 1,7 seconds on average per query, while crisp logic-based matching takes
4,7 seconds on average and their combination in hybrid matching is the slow-
est (6,4 seconds). These evaluation results are in line with the performance of
OWLS-MX and WSMO-MX and thus fortify the proposition that hybrid match-
ing outperforms pure logic-based as well as IR-based matching in terms of recall
and precision.
6 Related Work
To the best of our knowledge, there exist only very few implemented semantic
service discovery systems for SAWSDL. [10] presents a solution to SAWSDL Web
service discovery using UDDI registries called FUSION. In FUSION, any service
description is classified at the time of its publishing and then mapped to UDDI
to allow for fast lookups. In case of unknown semantic service requests reasoning
has to be done at query time. In contrast to SAWSDL-MX, each service offer
has only to satisfy one matching condition based on subsumption relationships
inferred by a reasoner, thus the ranking is not affected by different degrees of
logic-based match, neither does FUSION perform a syntactic or hybrid semantic
match. Like SAWSDL-MX 1.0, FUSION is strictly bound to OWL-DL, since
for each service, a semantic representation in terms of an individual of a pre-
defined OWL concept is constructed. Lumina [11] developed in the METEOR-S
project16 follows a similar approach based on a mapping of WSDL-S (and later on
SAWSDL respectively) to UDDI but performs syntactic service matching only.
For a survey of semantic service matchmakers in general, we refer the interested
reader to [9].
7 Conclusion
SAWSDL-MX performs hybrid semantic Web service matching for SAWSDL
operations based on both logic-based reasoning and IR-based syntactic similarity
measurement, and combines the results to provide a matching result for service
interfaces with multiple operations. The requester formulates queries in terms
of SAWSDL service interface descriptions and is presented a service ranking
containing service offers from the local registry. The version SAWSDL-MX 1.0
presented in this paper has been implemented and evaluated in terms of recall
and precision using a preliminary SAWSDL test collection called SAWSDL-TC1
which we derived from the existing collection OWLS-TC 2.2. As the experimental
results show, hybrid matching of SAWSDL services can outperform both logic-
based and IR-based matching in terms of precision at the cost of increased
average query response time.
We are currently working on several aspects of SAWSDL service discovery
and extensions of SAWSDL-MX. As SAWSDL is not restricted to semantically
represent service components using a fixed knowledge representation formal-
ism, the integration of additional ontology language support is intended. While
description logics have already been discussed for the first version SAWSDL-
MX 1.0, the support for languages originating from logic programming such as
WSML-Flight and WSML-Rule is subject to our future work.
Besides, inspired by the monolithic logic-based semantic service matchmaker
MaMaS [1, 2], we are currently working on an adaptive variant called SAWSDL-
MXA which exploits means of ontology patching such as concept contraction
and abduction combined with machine learning based on implicit feedback [5].
The semantic interoperability problem induced by the inevitable occurrence
of heterogeneous ontologies used for semantic service annotation can be ad-
dressed by appropriate ontology alignment techniques [13]. In SAWSDL-MX,
one option is to perform an additional matching of concept primitives (that
16
http://lsdis.cs.uga.edu/projects/meteor-s/
are left undefined in the matchmnaker ontology) in unfolded concepts to be
compared using a shared minimum vocabulary of requesters and providers like
WordNet17 , or by consistent introduction of additional equivalence axioms to
the local knowledge base of SAWSDL-MX [12].
SAWSDL-MX 1.0 and SAWSDL-TC1 are both publicly available at semweb-
central.org.
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17
http://wordnet.princeton.edu/