A QUERY EXPANSION METHODOLOGY IN A COOPERATION
OF INFORMATION SYSTEMS BASED ON ONTOLOGIES
Guillermo Valente Gómez Carpio, Lylia Abrouk and Nadine Cullot
LE2I, UMR CNRS 5158, University of Burgundy, Dijon, France
Keywords: Query expansion, Ontology, Cooperation of Information Systems.
Abstract: The great development of Internet technologies and the emergence of the semantic web have led to the
specification of architectures and tools to describe and to allow the “relevant” sharing of heterogeneous
information sources. Shared data can be annotated and mapped to an agreed representation of their semantic
using ontologies. Ontology is a representation of a domain of interest which is agreed by a community of
people. The aim of the paper is to propose a user’s query expansion methodology in a heterogeneous
cooperation of information systems. We propose a complete architecture called OWSCIS (Ontology and
Web Services Cooperation of Information Sources) of cooperation of information systems based on the use
of reference ontology and several local ontologies. This paper highlights the query expansion methodology
in this architecture. The objective is to help and guide the user during the query process by analysing his
query and using the usual behaviours of the users to predict his need.
1 INTRODUCTION
AND MOTIVATION
The emergence of the semantic web as a mean to
share “understandable” data from heterogeneous
sources is still a great challenge. The main idea is to
be able to annotate and map information sources
data to an ontology to express their semantic and to
transparently query the cooperation.
• Information System Cooperation. Lots of
works have been developed from classical
databases information systems cooperation to
more dynamic cooperation of information
sources. We can note the evolution of the
information systems cooperation architectures
from federated to mediator-wrapper
architectures and some more semantic-based
architectures using ontologies to express the
understanding of the shared knowledge.
• Ontologies. They can be viewed as an
agreed representation of a domain of interest
for a community of people, usable to share
data. The information sources can be
annotated and mapped to one or several
ontologies to describe their semantic allowing
the transparent querying of the cooperation.
• Cooperation Querying. The aim of
cooperation is to give users the opportunity to
transparently query the cooperation. This
querying can be done using the agreed
representation of the knowledge i.e. the
ontology.
We can mention some examples of relevant
systems which aim at defining such cooperation
environment such as MOMIS (Beneventano and
Bergamaschi, 2004), MECOTA (Stuckenschmidt
and Wache, 2000), Infosleuth (Nodine et al., 2000)
or Kraft (Preece et al., 2000).
The transparent querying of a cooperation raises
a lot of complex problems such as: (i) the choice of
the language to pose the query (natural language, list
of terms, formal language, etc.), (ii) the query
decomposition to access to the data of the different
sources of the cooperation, (iii) the results re-
composition to answer the initial query and also (iv)
the need to help and guide the users to build their
queries.
To motivate our work on query expansion
methodology, we first present its context of use with
a brief presentation of our architecture of
cooperation called OWSCIS (Ontology and Web
Service based Cooperation of Information Sources).
256
Carpio G., Abrouk L. and Cullot N.
A QUERY EXPANSION METHODOLOGY IN A COOPERATION OF INFORMATION SYSTEMS BASED ON ONTOLOGIES.
DOI: 10.5220/0001825302560262
In Proceedings of the Fifth International Conference on Web Information Systems and Technologies (WEBIST 2009), page
ISBN: 978-989-8111-81-4
Copyright
c
 2009 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

1.1 OWSCIS Architecture Overview
The OWSCIS architecture (Ontology and Web
Service based Cooperation of Information Sources),
is based on ontologies and web services
technologies to allow the cooperation of distributed
and heterogonous information sources. In this
architecture, information sources are encapsulated in
modules called “Data Providers”. An information
source may be a relational database or an XML
document, and a data provider may contain one or
more information sources. A data provider wraps its
information source(s) to a local ontology which
expresses the semantic of information sources. Local
ontologies are also mapped to a reference ontology
that semantically describes a given domain of
interest and covers all participant local ontologies.
Most of the architecture components are
encapsulated in web services, each of them performs
a specific task including: inter-ontology mapping,
query processing and query-results visualization.
Figure 1: OWSCIS Architecture.
Figure 1 gives an overview of this architecture.
The query web service allows end users to submit
their queries in terms of the reference ontology. The
query is expressed in sparql language; it is analyzed
and decomposed into a set of sub-queries over the
relevant data providers. The results returned from
the different data providers are recomposed and sent
to the visualization web service. The sparql query is
also analyzed to be enriched during the querying
process.
The following section gives an overview of the
query expansion method which is applied to help
and guide users during the querying process.
It is important to note that users directly query
the cooperation through its reference ontology and
that the answers returned to the users are enriched
with related terms of the ontology. The query
expansion methodology we propose is a mean to
choose pertinently these terms regarding the usual
behaviors of the users.
1.2 Expansion Method Overview
The aim of the query expansion method is to enrich
the query of a user by providing him the results of
his initial query but also a list of terms associated to
the terms of his initial query. These terms may be
used by the user to refine his query or to build a new
query. In particular, in our solution, the related terms
are chosen by analyzing the usual behaviour of the
users.
Figure 2 gives an overview of query expansion
process applied in the system OWSCIS.
Figure 2: OWSCIS Query Expansion Process.
This query expansion process consists in four
steps: 1) the user submits his query (in SPARQL) on
the reference ontology, 2) the query elements
(concepts and properties) are extracted to build a list
of terms to be enriched, 3) the query expansion
algorithm is applied to enrich the initial list of the
concepts extracted from the query with a list of
proposed concepts also belonging to the reference
ontology and 4) the user can visualize the results of
his initial query but also the enriched query.
The paper is organized as follows. Section 2
proposes a discussion on some existing expansion
methods based on different approaches. Section 3 is
dedicated to the details of the proposed query
expansion methodology. Section 4 illustrates the
methodology and section 5 concludes this paper.
Sparql
query
Query concept
Proposed concept
Visualization Web Service
Visualization (text, 2D, 3D)
Enrichment
algorithm
Query Web Service
Initial query result
+
Enriched query
A QUERY EXPANSION METHODOLOGY IN A COOPERATION OF INFORMATION SYSTEMS BASED ON
ONTOLOGIES
257

2 QUERY EXPANSION
METHODS: RELATED WORKS
Query expansion in information retrieval systems
attempts to make answers more clear and precise.
This allows users to modify their queries and
improves retrieval performance (Salton and McGill,
1986).
Several works have tackled this problem. The
approaches described below are based on (i)
documents retrieval, (ii) statistical methods and (iii)
ontology-based methods by calculating a semantic
similarity, etc.
2.1 Query Expansion Methods using
Documents
Raymond et al., (2002) present a query
reformulation system based on an unsupervised
classification method. The information retrieval
system called SIAC is used to obtain a set of initial
documents from a query and then each document is
associated to a set of terms. The system classifies
the sentences of the retrieved document according to
some words extracted from the document.
Other query expansion methods are based on the
use of the profile of the user.
Bottraud and G. Bisson, (2004) propose an
approach which is based on the building of the
user’s profile in order to enrich his queries. The
system identifies a context using the user’s profile
and his query. The query expansion is done in two
phases: (i) vocabulary learning and (ii) queries
learning. The profile of the user is updated
according to his search.
Another approach (Peninou et al., 2006) is also
based on the use of user’s profile and consists in
comparing the similarity between the query and the
user’s profile elements.
2.2 Statistical Query Expansion
Methods
Some statistical methods are generally based on the
notion of co-occurrence of terms. Terms are grouped
into classes and the terms of a same class are used to
enrich the queries. It consists in creating relations
between the terms of a same document.
Cui et al., (2002) assume that the terms occurring
in a query are correlated to the terms of the
document on which the user has clicked. The
expansion method is based on a probabilistic
analysis. It extracts correlations among the terms of
the query and the documents by analyzing the query
logs. The best terms from documents (high quality)
are used to expand the query.
Instead of calculating similarity with each term
of the query, (Qiu and Frei, 1993) take into account
the whole query and consider terms similar to the
“query concept”.
2.3 Semantic Query Expansion
Methods
According to Voorhees, (1994), statistical methods
are less successful than methods using semantic and
linguistic relations. He proposes an enrichment
query method based on the use of the semantic
relations between the terms (synsets) as defined in
Wordnet. In order to enrich a query, several methods
are also based on the use of external resources like
ontologies. The relations between terms are used to
expand the terms of the query.
Tomassen et al., (2006) use an ontology to
expand the queries and improve the quality of the
information retrieval. They use text mining
techniques to adapt the ontology concepts to the
domain terminology (terms used in documents but
not in query).
Schweighofer and Geist, (2007) combine two
techniques: the use of ontologies and the relevance
feedback. The terms of the query are looked up in a
knowledge base (lexical ontology) and weighted.
2.4 Discussion
Most of the approaches described in the previous
section, propose methods which enable the
expansion of user queries either using the users
profile or terms semantically “close” to the terms of
queries. In the latter case, the way to obtain the
“similar” terms can differ. They can be found using
some statistical methods by analyzing the co-
occurrence of terms in some documents and in the
query or using external resources such as thesaurus
or more complex ontologies. Few approaches use
the relevance feedback which allows the
consideration of the actions “really” done by the
users.
We propose a query expansion method which is
based on the analysis of the usual behaviors of the
users. The initial and the enriched queries are
expressed using terms of a reference ontology.
The proposed method can be viewed as a variant
of the PageRank algorithm used to find and rank
web pages. However, our algorithm is applied in a
WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies
258

different context. The notion of link between pages
used in the page rank algorithm is substituted by the
notion of usual behavior of the users in their queries
and the value of the page rank for a page is replaced
by the notion of ConceptRank value which
expresses the popularity of a concept and by the
notion of importance of concept relatively to another
one.
We can note that some works such as those
proposed by (Richardson and Domingos, 2002)
focus on the improvement of the PageRank
algorithm with a probabilistic model to find the
relevant pages. Our methodology differs from these
approaches because it is based on the analysis of the
concepts of the queries and the behavior of the
users.
3 THE QUERY EXPANSION
METHOD
As we discuss below, our method is based on the
page rank algorithm but the value of the page rank
for a page is replaced by the notion of ConceptRank
value which expresses the popularity and the
importance of a concept relatively to another one.
The basic version of PageRank algorithm is
based on the idea that the more a document is
important, the more other documents are linked to it.
We apply this heuristic to concepts with the idea
that the more a concept is important, the more this
concept is searched after another concept in the
users’ queries.
The PageRank algorithm is based on the analysis
of the hypertext graph of the links between the
pages. In our method, we build a concept graph
which is based on the terms successively used by the
users in their queries.
3.1 Concept Graph Definition
A concept graph is built on the basis on the users’
queries. Intuitively, the nodes of the graph are the
concepts and an arc from a node N1 to a node N2
expresses that the concept of the node N2 has been
searched after the concept of the node N1 by a user.
This graph is directed and weighted.
First, we introduce the definitions of a query
sequence and sub-sequence, then we define the
notions of concept graph for a sub-sequence and a
sequence of queries.
Definition. Let C be a set of concepts of a domain,
Q a query which is a subset of concepts of C, a
query sequence is a n-uplet S (Q
1
,Q
2
,…,Q
n
) where
Q
i
is a
query.
A query sub-sequence of a sequence
S(Q
1
,Q
2
,…,Q
n
) is a 2-uplet S
i
(Q
i
,Q
i+1
) where
i=1…n-1.
The cardinality │Q│ of a query Q, is the number of
concepts belonging to this query.
Figure 3 illustrates a query sequence
S(Q
1
,Q
2
,Q
3
). Each sub-sequence Si of a sequence S
is represented by a concept graph G
i
.
Location
Event
Conference
Paper
Event
Person
Conference
Person
Presentation
Event
S 1 (Q 1 , Q 2 )
Q2
Q3
S 2 (Q 2 , Q 3 )
Q1
Figure 3: A Query Sequence S(Q
1
,Q
2
,Q
3
).
Definition. A concept graph G
i
(O
i
,E
i
) is a directed
weighted graph, for a sub-sequence S
i
(Q
i
,Q
i+1
) of a
sequence S(Q
1
,…,Qn) where:
O
i
is the set of the concepts belonging to Q
i
⋃ Q
i+1
,
V
i
is the set of edges belonging to
(Q
i
∩ Q
i+1
) X (Q
i+1
- Q
i
).
The weight w
i
(v
i
) associated to an edge v
i
∈ V
i
is
defined by:
1
1
)(
+
∩
=
ii
ii
QQ
vw
(1)
The value w
i
(v
i
) represents the weight associated to
an arc. It is based on the number of concepts that a
user has kept between two successive queries of a
sub-sequence S
i
(Q
i
, Q
i+1
).
Definition. A graph G for a sequence S(Q
1
,…,Qn)
is the union of the G
i
graphs of the sub-sequences
S
i
(Q
i
,Q
i+1
).
U
1
1
−
=
=
n
i
i
GG
(2)
A QUERY EXPANSION METHODOLOGY IN A COOPERATION OF INFORMATION SYSTEMS BASED ON
ONTOLOGIES
259

Figure 4 illustrates the concept graphs G
1
and G
2
for the sub-sequences S
1
(Q
1
,Q
2
) and S
2
(Q
2
,Q
3
) of the
sequence S(Q
1,
Q
2
,Q
3
) shown in figure 3.
Event
Person
Presentation
Conference
1/2
1/2
1/2
1/2
Event
Person
Paper
1
1
G 1
G 2
Figure 4: Concept graphs of the sub-sequences.
The concept graph of a query sequence is
translated into a matrix. Intuitively, this matrix
contains for each couple of concepts (C1, C2), the
sum of the weights of the arcs existing between the
node C1 and C2 in the concept graph.
We first define below the notion of matrix for a sub-
sequence and then the notion of matrix for a whole
sequence.
Definition. A graph G
i
for a sub-sequence
S
i
(Q
i
,Q
i+1
), can be represented by a matrix MC
i
, such
as:
MC
i
: C →C, where C is the set of concepts of the
domain.
w
i
(c
i
,c
j
) if there is an arc
v
i
:c
i
→c
j
MC
i
(c
i
,c
j
)=
0 else.
(3)
Definition. The matrix MC of a graph G of a
sequence S(Q
1
,…,Qn) is the sum of the matrix MC
i
of the sub-sequences S
i
(Q
i
,Q
i+1
).
∑
−
=
=
1
1
n
i
i
MCMC
(4)
Definition. We note N(c) the number of arcs of G,
going from c
to another concept.
v
j
=1, if MC(c
,
c
j
)≠0
∑
=
=
n
j
j
vcN
0
)(
v
j
=0, else
(5)
For each query sequence, a concept graph is
computed and the matrix is incremented.
3.2 ConceptRank Measure
Based on the matrix defined in the previous section,
we can now introduce a new measure of popularity
called the ConceptRank measure.
As in the PageRank algorithm, the computation
of ConceptRank measure needs a number of
iterations to fix the value. The ConceptRank
measure expresses the popularity of a concept
according to the usual behavior of the users during
their querying process. The ConceptRank measure is
defined as follows.
Definition. Let c
i
be a concept of C, B(c
i
) the set of
concepts, such that MC(c
j ,
c
i
)≠0, d a normalization
factor:
the ConceptRank measure of a concept c
i
,CR (c
i
) is
defined by:
∑
∈
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
+−=
)(
)(
)(
)1()(
ij
cBc
j
i
cjN
cCR
ddcCR
(6)
We complete this notion of popularity of a
concept with the notion of the importance of a
concept relatively to another concept or to a query.
3.3 Concept Importance Measure
The notion of importance of a concept relatively to
another concept and more precisely the importance
of a concept relatively to a whole query is the
measure that we use to choose the concepts to add to
enrich the user’s query. These measures are based
on the popularity of the concepts as defined
previously with the ConceptRank measure.
Definition. Let MC the matrix representing some
graph G and Q a query.
CI(c
i
,c
j
) is the Concept Importance of the concept c
i
relatively to the concept c
j
which is defined by:
CI (c
i
,c
j
) = MC(c
i
,c
j
) * CR(c
i
)
I(c
i
,Q
i
) is the Importance of a concept c
i
relatively to a query Q
i
which is defined by:
(7)
∑
∈
=
ij
Qc
jiii
ccCIQcI ),(),(
(8)
WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies
260

3.4 The Minimum Importance Factor
To choose the concepts to add to expand a query
considering their importance relatively to this query,
we define a threshold value called the minimum
factor importance. A query Q
i
is expanded with a
concept c
i
if its concept importance I(c
i
,Q
i
) is greater
than the minimum importance factor.
Definition. Let I
Qi
be the set of concepts importance
values for a query Q
i
, I
Qi
= {I(c
1
,Q
i
),…,I(c
n
,Q
i
).
The minimum importance factor Min (FI/Q
i
)for a
query Q
i
is defined by:
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
+
=
2
)()(
)/(
ii
QQ
i
IMinIMax
QFIMin
(9)
4 EXPERIMENTATION
The presented approach has been implemented. We
introduce in this section some preliminary results.
To simplify the description of the experimentation,
we directly consider the concepts extracted from the
SPARQL queries done on the reference ontology of
the OWSCIS architecture. The used ontology is a
sample “Conference” ontology freely available on
the Web.
The experimentation is composed of two phases:
•
Learning phase: this initial phase consists in
storing in the knowledge base a set of query
sequences in order to be able to compute the
importance of the concepts relatively to a new
query. The enrichment process is based on
this phase. Then the knowledge base is
updated for each new query sequence.
•
Query Expansion: the user submits a new
query and the system returns the results of this
initial query and also a list of new concepts
related to the initial query. The user can
visualize all the concepts in the reference
ontology.
The experimentation has been done on 55 users’
queries for the learning phase. Then the matrix of
concepts (MC) has been calculated. This matrix is
incremented for each new query sequence done by a
user. We also calculated the ConceptRank values of
the concepts and all the necessary values of
importance of a concept relatively to a new query to
be enriched.
Table 1 gives a short extract of the results with
the description of the query to be expanded (first
column), the value of the minimum importance
factor for this query (second column) and the
expansion of the query (third column). The added
concepts are in bold. The new concepts occurring in
the enriched query are those for which the
importance value relatively to the query is greater
than the minimum importance factor.
Table 1: Query expansion.
Query Min(FI) Query Expansion
Conference,
Event
1.1205 Conference, Event,
Location,
Presentation
Commitee,
Organization
1.304 Commitee,
Organization,
Person,
SocialEvent,
Conference
Location,
SocialEvent
1.57 Location,
SocialEvent,
Presentation,
Organization
KeynoteTalk,
Presenter,
Presentation
1.5515 KeynoteTalk,
Presenter,
Presentation,
Event, Paper
Person,
Group,
Event
2.4965 Person,Group,
Event, Committee,
Organization
Presentation,
Conference,
Paper
1.685 Presentation,
Conference, Paper,
Event
Organization,
Workshop,
Presentation
1.467 Organization,
Workshop,
Presentation,
Paper, Presenter
Commitee,
Location
1.417 Commitee,
Location,
Conference,
Presentation,
Organization
5 CONCLUSIONS
In this paper, we have presented a new query
expansion method based on the use of ontology and
a new measure of popularity and importance taking
into account the usual behavior of the users. The
query expansion process is included in the query and
visualization processes of the system OWSCIS
(Ontology and Web Service based Cooperation of
Information Sources). Some preliminary results are
given in section 5. An experimentation on a huge
ontology (1500 concepts) is currently performed to
confirm the method. The quality and the pertinence
A QUERY EXPANSION METHODOLOGY IN A COOPERATION OF INFORMATION SYSTEMS BASED ON
ONTOLOGIES
261

of the results are greatly linked to the “intelligent”
behavior of the users. A more detailed analysis of
this knowledge to construct the graph is also
currently studied to optimize this work.
REFERENCES
Bottraud, J-C., Bisson, G., Bruandet, M-F., 2004.
Expansion de requêtes par apprentissage automatique
dans un assistant pour la recherche d'information. In
CORIA’04, Conférence en Recherche Information et
Applications., pages 89-108.
Beneventano D., Bergamaschi, S., 2004. Methodology for
integrating heterogeneous data sources. In IFIP’04,
World Computer Congress., pages 22-27.
Cui, H., Rong Wen, J., Yun Nie, J., Ying Ma, W., 2002.
Probabilistic query expansion using query logs. In
WWW’02, 11
th
international World Wide Web
conference., pages 325-332. ACM Press.
Nodine, M., Fowler, B., Ksiezyk, T., Perry, B., Taylor,
M., Unruh, A., 2000. Active Information Gathering in
InfoSleuth, In International Journal of Cooperative
Information Systems., pages 3-28.
Peninou, C.Z.A., Canut, M., Sedes, F., 2006. An
adaptation approach: query enrichment by user
profile. In SITIS’06, The international conference on
Signal-Image Technology & Internet-Based Systems.,
pages 24-35.
Preece, A., Hui, K., Gray, A., Marti, P., Bench-Capon, T.,
Jones,
D., Cui, Z., 2000. The KRAFT architecture for
knowledge fusion and transformation. In Knowledge-
Based Systems, Volume 13, Issues 2-3, pages 113-120.
Quiu, Y., Frei, H.-P., 1993. Concept based query
expansion. In SIGIR’93,16
th
Annual International
Conference on Research and Development in
Information Retrieval., pages 160-169, New York,
NY, USA. ACM Press.
Raymond, C., Bellot, P., El-Bèze, M., 2002.
Enrichissement de requêtes pour la recherche
documentaire selon une classification non-supervisée.
In RFIA’02, 13ème Congrès Francophone AFRIF-
AFIA de Reconnaissance des Formes et d'Intelligence
Artificielle., pages 625-632.
Richardson, M., Domingos, P., 2002. The intelligent
surfer : Probabilistic combination of link and content
information in PageRank. In Advances in Neural
Information Processing Systems Vol. 14., pages 1441-
1448, Cambridge, MA: MIT Press.
Salton, G., McGuill, M., 1986. Introduction to Modern
Information Retrieval. McGraw-Hill, Inc., New York,
NY, USA.
Schweighofer, E., Geist, A., 2007. Legal query expansion
using ontologies and relevance feedback. In
LOAIT’07, Legal Ontologies and Artificial
Intelligence Techniques., pages 149-160.
Stuckenschmidt, H.,Wache, H., 2000. Context modeling
and transformation for semantic interoperability. In
KRDB’00, Knowledge Representation Meets
Databases.
Tomassen, S., Gulla, J., Stransunskas, D., 2006.
Document space adapted ontology: Application in
query enrichment. In NLDB’06, 11
th
International
Conference on Applications of Natural Language to
Information Systems., pages 46-57.
Voorhees, .E. M., 1994. Query expansion using lexical-
semantic relations. In SIGIR '94, 17th annual
international ACM-SIGIR conference on Research
and Development in Information Retrieval, pages 61-
69, New York, NY, USA. ACM Press.
WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies
262