Preserving the Original Query Semantics in Routing Processes
Crishane Freire
1
, Nicolle Cysneiros
2
, Damires Souza
1
and Ana Carolina Salgado
2
1
Federal Institute of Education, Science and Technology of Paraiba, João Pessoa, PB, Brazil
2
Federal University of Pernambuco, Recife, PE, Brazil
Keywords: Query Semantics, Query Reformulation, Query Routing, Query Semantics Preservation.
Abstract: In distributed data environments, peers (data sources) are connected with each other through a set of
semantic correspondences in such a way that peers directly connected are called semantic neighbours.
Queries are submitted considering partial information provided by a peer schema and may be answered by
other neighbour peers. From the query submission peer, the original query is successively rewritten into
queries over the peers, according to the correspondences between the original peer and the target ones. In
this process, some of the original query terms may be lost while other ones may be added, leading to a
semantic loss of the original query. In this work, we argue that it is essential to try preserving the original
query semantics if we wish to hold what the users defined as important at query submission time. With this
in mind, we propose an approach to preserve the original query semantics in query routing processes.
Furthermore, we present a metric for assessing the relevance of neighbour peers according to an estimated
query semantic value obtained at each query reformulation. In this paper, we present the developed
approach and some experimental results we have accomplished.
1 INTRODUCTION
Query answering in distributed data environments
faces some challenges related mainly to the large
number of data sources, their autonomous nature,
and the heterogeneity of their data. These
environments have a diversity of perspectives and
are composed by data sources (peers), which are
linked by means of semantic mappings (here called
correspondences). Peers directly connected by
correspondences are called semantic neighbours.
One special problem concerning these
architectures is how to exploit the correspondences
between neighbour peers in order to answer queries.
This problem, named as query routing process,
regards identifying relevant neighbour peers for
answering particular user queries, in such a way that
answers may fit better the user needs. Particularly,
one key issue is how to preserve the semantics of a
submitted query as long as it is routed through the
set of neighbour peers (Delveroudis et al. 2009;
Kantere et al. 2009).
In fact, query routing processes and query
reformulation strategies have a great influence on
each other. We argue that, in query routing
processes, the original query semantics should be
preserved as far as possible. By the “query
semantics”, we mean the set of terms (i.e., concepts
and properties), which are required at query
formulation time. To this end, two aspects should be
considered, namely: (i) query reformulation along
the peers should take into account what users
defined as important in the original submitted query;
and (ii) the query routing process should avoid
forwarding a reformulated query which has been
identified with a high degree of semantic loss
(Delveroudis and Lekeas, 2007).
With this in mind, in this work, we propose an
approach to preserve, as far as possible, the original
query semantics in query routing processes. To this
end, we define a query semantic reference, which is
used to prune the query semantic loss. Furthermore,
we present a metric for assessing the relevance of
neighbour peers according to an estimated query
semantic value obtained at each query reformulation.
Our contributions are summarized as follows:
 We propose an approach to preserve the original
query semantics in query routing processes
taking into account a semantic reference which is
defined on the fly.
 We create a query semantic reference according
to a domain ontology composed by a set of
expanded terms.
 We define a metric to assess the degree of query
70
Freire C., Cysneiros N., Souza D. and Salgado A..
Preserving the Original Query Semantics in Routing Processes.
DOI: 10.5220/0004893100700080
In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 70-80
ISBN: 978-989-758-027-7
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

semantics preservation at query reformulation
time.
 We describe experiments regarding the
effectiveness of our approach.
This paper is organized as follows: Section 2
defines our setting; Section 3 introduces a
motivating example. In Section 4, we propose our
approach to preserve the original query semantics in
query routing processes. Section 5 shows some
experiments we have accomplished. Section 6
discusses some related work. Finally, Section 7
draws our conclusions and points out some future
work.
2 OUR SETTING
We have instantiated our approach in a Peer Data
Management System (PDMS) (Roth and Skritek,
2013), although it can be instantiated in any dynamic
distributed data environment. PDMS consist of a set
of peers, each one with an associated schema that
represents the data to be shared with other peers. In
such systems, schema matching techniques are used
to establish correspondences between peer schemas,
which form the basis for query reformulation.
Our system is called SPEED, a PDMS that
adopts an ontology-based approach to assist relevant
issues in peer data management (Pires, 2009). In
SPEED, the peers are clustered according to the
same knowledge domain (e.g., Education, Health),
and an ontology describing the domain is available
to be used as background knowledge. Ontologies are
also used as a uniform conceptual representation of
peer schemas. Peers are semantically related by
means of correspondences. We consider that
correspondences are defined between pairs of
semantic neighbour peers, i.e., peers that are
semantically related as identified by a clustering
process. In this scenario, in a query routing process,
queries are submitted at one peer and reformulated
to other peers, and the final query result is the union
of the query answers (considering that data
conversions have already been done) returned by
each accessed peer.
In this work, particularly, we consider two
aspects: (i) the ontology matcher that generates the
semantic correspondences between peer schemas;
and (ii) the query reformulation process itself. In the
following subsections, we describe a running
scenario, the semantic matcher (Pires et al. 2009)
and the query reformulation process (Souza et al.
2011) used in our work.
1
2.1 Running Scenario
In this scenario, we use ontologies belonging to the
Education’s knowledge domain. The peers have
complementary data about academic people and
their work (e.g., publications). We assume that peer
ontologies have been normalized into a uniform
representation format according to some background
knowledge (in our work, we use a domain ontology).
Throughout the paper, we will use a domain
ontology concerning Education, named
UnivCSCMO.owl
1
(Figure 1).
Figure 1: An excerpt from the domain ontology.
2.2 Semantic Matcher
The output of a matching process is called an
alignment, which contains a set of correspondences
indicating which terms (concepts or properties) of
the two ontologies logically correspond to each other
(Euzenat and Shvaiko 2007).
The ontology semantic matcher (SemMatcher)
(Pires et al. 2009) considers domain ontologies (DO)
as reliable references, which are usually made
available on the Web. It uses them in order to bridge
1
http://www.cin.ufpe.br/~speed/SemMatch/UnivCsCMO.owl
PreservingtheOriginalQuerySemanticsinRoutingProcesses
71

the conceptual differences or similarities between
two peer schemas. In this process, a linguistic-
structural matcher and a semantic matcher are
executed in parallel. The obtained similarity values
of both matchers are combined through a weighted
average.
The SemMatcher approach identifies seven kinds
of semantic correspondences (Souza et al. 2009),
each one associated with a particular weight which
corresponds to the level of confidence on such
correspondence as follows: isEquivalentTo (1.0),
isSubConceptOf (0.8), isSuperConceptOf (0.8),
isCloseTo (0.7), isPartOf (0.5), isWholeOf (0.5), and
isDisjointWith (0.0). The weights reflect the degree
of similarity between the correspondent elements,
from the strongest relationship (equivalence) to the
weakest one (disjointness).
Example 1: To better illustrate, consider a scenario
composed by two peers P
1
and P
2
. Each peer is
described by an ontology: O
1
and O
2
, respectively. In
2
order to identify the semantic correspondences
between O
1
and O
2
, we used the SemMatcher tool.
As a result, a set of correspondences (i.e., an
alignment) between O
1
and O
2
and their respective
weights were identified. Figure 2 depicts a fragment
of the alignment file. In this illustrative set, we can
see the isEquivalentTo correspondence between the
term FullProfessor in O
1
and O
2
. By using the
semantics underlying the DO, the SemMatcher also
identifies other types of correspondences such as the
isCloseTo correspondence (i.e, terms with a
semantic similarity degree, which is achieved from
the identification of sibling concepts in the domain
ontology) between Lecturer in O
1
and Professor in
O
2
.
Figure 2: Fragment of an alignment.
2.3 Query Reformulation
In a query reformulation process, terms from a
source peer do not always have exact corresponding
2
http://www.w3.org/TR/rdf-sparql-query/
terms in a target one, what may result in an empty
set of reformulations and, possibly, no answer to
users. Query reformulation strategies by terms
expansion (Carpineto and Romano, 2012; Souza et
al., 2009) aim to provide users with not only exact
answers but also approximate, additional (i.e.,
expanded) answers.
The SemRef approach (Souza et al., 2009) uses
the set of semantic correspondences to produce two
kinds of query reformulations: (i) an exact one,
considering only equivalence correspondences and
(ii) an enriched one, resulting from the set of the
other correspondences identified by the
SemMatcher. To define the query enriching mode,
the user may set four variables, which specify what
should be considered when a query Q is to be
enriched. The variables are defined as follows:
 Approximate: includes terms that are close to the
ones of Q;
 Specialize: includes terms that are sub-concepts
of some terms of Q;
 Generalize: includes terms that are super-
concepts of some terms of Q;
 Compose: includes terms that are part-of or
whole-of some terms of Q.
The queries can be formulated by considering
the terms provided by the peer ontology, using
SPARQL
2
or ALC/DL (Baader et al., 2003). For the
sake of simplicity, in this paper, we assume that Q is
a query expressed over a peer ontology, which has
the following form: Q = {T
i
}, i =1…n, where T
i
is a
term (concept or property) that belongs to the peer
ontology.
Considering the scenario described in Example
1, suppose that a user submits a query Q =
{FullProfessor} in P
1
and set all four enriching
variables to TRUE. The SemRef tool produces two
query reformulations: an exact reformulation,
denoted by Qexact = {FullProfessor}, and another
one, enriched, denoted by Qenriched={Professor,
Course}.
In the next section, we present a motivating
example to justify the proposal of our approach.
3 MOTIVATING EXAMPLE
Our motivating example is based on the running
scenario described in Section 2.1. It is composed by
3 peers (Figure 3), which are linked to other peers by
means of correspondences, as defined in Section 2.1.
As depicted in Figure 3, each box represents a
peer ontology. In this scenario, suppose that a user
submits a query Q={GraduateStudent} at peer P1
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72

Figure 3: Motivating Example.
and, in order to acquire more related results, s/he
sets the Specialize and Generalize variables to
TRUE. At P1, query Q is expanded to
Q={GraduateStudent, Student, PhDStudent,
MasterStudent} taking into account the enriching
correspondences. Then Q is executed.
In order to be forwarded to P2, Q
is reformulated
into Q
12
={UndergraduateStudent, Student, Person}
according to the local ontology of P2. To clarify
matters, Figure 4 presents a fragment of the
alignment between P1 (O1) and P2 (O2).
Figure 4: Fragment of the Alignment between P1 and P2.
When reformulating Q
11
to Q
12,
some terms
(GraduateStudent, PhDStudent, MasterStudent) are
lost. On the other hand, Student is preserved and the
terms Person and UndergraduateStudent are
included. Despite the term UndergraduateStudent is
related to the term Student, it doesn’t have a
semantic association with the term GraduateStudent
of the original query. Indeed, they are disjoint terms
as we can verify in the alignment (Figure 4).
After Q
12
has been executed locally, it is
reformulated according to the correspondences
between P2 and P3 (Figure 5). This results in query
Q
13
= {GraduateStudent, UndergraduateStudent,
Monitor, Worker}. Although the original term
GraduateStudent is present in Q
13
, the other included
terms are producing a semantic loss with respect to
the original query that was looking for
GraduateStudent. In this sense, it is likely that the
query results will not be suitable to the user’s
interests.
It is important to emphasize that there is no
semantic loss with respect to the current query (the
one that reaches the peer). The reformulated query
terms UndergraduateStudent, Monitor and Worker
have a semantic correspondence with the current
query terms Student and Person (Figure 5).
Figure 5: Fragment of the Alignment between P2 and P3.
4 OUR APPROACH
Although query reformulation strategies by means of
query expansion have been used in order to improve
queries recall, it sometimes results in a precision loss
(Campos et al., 2013). Close terms which are added
to reformulated queries may produce a gap between
the original query and their expanded forms.
Considering that we are dealing with a large-
scaled dynamic network, composed by thousands of
peers, the successive processes of query
reformulation may lead to a high degree of query
semantics loss. In our setting, this may occur due to
two possible reformulation actions: (i) by losing
(i.e., removing) query original terms, when there is
PreservingtheOriginalQuerySemanticsinRoutingProcesses
73

no equivalence correspondence between these terms
and the target peer ontology ones; and/or (ii) by
including new close terms acquired at the query
enrichment step.
In order to preserve the original query semantics,
we propose the creation of a semantic reference.
This semantic reference is defined with respect to
the original submitted query according to a
background knowledge provided by a domain
ontology. Besides, we define some metrics to assess
the query semantic value.
These metrics are based on preservation and
enrichment measures, which are calculated at query
reformulation time. In the following sections, we
define the semantic reference and the metrics used to
assess the query semantic value.
4.1 Defining a Semantic Reference
The semantic reference is used for pruning (i.e.
removing) terms of the reformulated query that are
producing a semantic loss with respect to the
original query. In order to define the semantic
reference, we state, at first, the direct semantic
relation, as follows.
Definition 1 (Direct Semantic Relation): Let O
1
and O
2
be two neighbour peer ontologies. We state
that a term (concept or property) T
1
in O
1
has a
Direct Semantic Relation (DSR) with T
2
in O
2
, if
there is a defined relationship between them, by
means of a correspondence Co, where Co ∈
{isEquivalentTo, isSubConceptOf,
isSuperConceptOf, isCloseTo, isPartOf, isWholeOf,
isDisjointWith}.
Definition 2 (Semantic Reference): Let Q be a
query submitted at peer P
i
, in accordance with its
local ontology O
i
, and DO a corresponding domain
ontology. A Semantic Reference (SR) is defined as a
triple SR = {<t
i
, t
j
, w>}, where t
i
is a query term ϵ O
i
,
t
j
is a term ϵ DO and t
i
has a DSR with t
j
; w is the
weight associated to each existing correspondence
between t
i
and t
j
.
To create the SR, firstly, an alignment between
the submission peer ontology and the domain
ontology is generated. Then, by considering the
enriching variables, the original terms are expanded
and the weights associated to the semantic
correspondences between the original term and its
expanded terms are identified. Thus, the SR is a set
of triples composed by the original term, the
expanded term and its correspondence weight.
By maintaining the correspondence weights in
the SR allows choosing the semantically closest
correspondences (according to their weights) from
the existing ones. This is especially important when
there are reformulated query terms belonging to the
SR that are not expanded by some original
correspondent term.
Example 2: To clarify matters, consider the query Q
= {FullProfessor}, shown in Example 1. Also,
consider a fragment of an alignment between O
1
and
the DO, as depicted in Figure 6.
Figure 6: Fragment of the alignment between O
1
and the
DO.
Considering that all four enriching variables have
been set to TRUE, a semantic reference SR of Q was
produced by taking into account the existing
semantic correspondences, as follows:
SR={<FullProfessor,FullProfessor,1,0>,<FullProf
essor,Professor,0,8>,<FullProfessor,ResearchProje
ct,0,3>,<FullProfessor,Course,0,3>,<FullProfessor
,VisitingProfessor,0,7>}.
Both terms AssociateProfessor and
AssistantProfessor are disjoint with FullProfessor,
thus they are not included in the query
reformulation. This is due to the fact that the disjoint
correspondence is only used when there is a
negation in the original query. In this way, SR is
called a semantic reference of Q and will be used for
pruning some terms in Q reformulations along a
query routing process.
Thus, enriched terms which do not belong to the
SR will indeed be pruned from the reformulated
queries. The goal is to preserve the original query
semantics and avoid possible semantic loss that may
occur at query reformulation time.
4.2 Assessing the Query Semantic
Value
When a query is reformulated to another peer
schema, some of the original query terms may be
lost due to the differences between peer schemas. On
the other hand, the user may choose whether the
query reformulation Q
r
should consider more
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semantic extra terms than the equivalent ones. In
this way, during an overall query routing process,
whenever Q
r
reaches a target peer, it can be enriched
with more terms and, consequently, may retrieve
more results.
These loss and enrichment processes should be
assessed in order to decide if the reformulated query
is still useful for users (i.e., fits the original query
semantics) and may be forwarded to the other peers.
In this light, suppose Q
origin
is the original query
submitted by the user, Q is a forwarded query from
P
i
to P
j
, Qr is its reformulated query and SR a
semantic reference of Q
origin
. After pruning Qr, we
may assess the semantic value of Qr by means of
two metrics, defined as follows:
Query Preservation (QPreserv): this metric
calculates the number of terms belonging to Q
origin
,
which have been preserved after query
reformulation. The QPreserv measure is stated as
follows:
QPreser
v


#t

#t



(1)
where
t
eq
is the number of terms in Q
r
, which are
equivalent to the original terms of Q
origin
;
t
origin
is the number of terms in Q
origin
.
Query Enrichment (QEnrich): this metric calculates
the query enrichment value considering the terms
that have been expanded according to the original
terms. Thus, the QEnrich metric is stated as follows:
QEnrich


∑

∑


/








(2)
where
nto, number of terms in Q
origin
;
nte
k
, number of enriched terms for each term of
Q
origin
;
w
kl
, weight associated to the enriched term in the
semantic reference.
The query semantic value (QSV), after the query
reformulation, is given by the sum of the measures
obtained from formulas (1) and (2). The QSV is
properly stated in formula (3), as follows.
QSV

 QPreserv

QEnrich

(3)
The QSV is established with values between 0
and 1.8, where 0 indicates no kind of relevancy, and
1.8 states the maximum relevancy for query
reformulation. In this latter value, all the original
terms have been preserved and the maximum
enrichment has been acquired as well.
Since the query is reformulated by considering
the target peer schema, we argue that the QSV may
be used to assess the peer relevance with respect to
that query. In this sense, such measure may be used
as a selection criterion to query forwarding in a
routing process. The next section presents some
experimental results we have obtained.
5 EXPERIMENTS
Our experimental scenario is the one described in
Section 2.1. In this setting, queries are submitted
and reformulated along the network of neighbour
peers.
In order to achieve more meaningful results, the
query Q should be executed in the enriched mode,
i.e., by considering all semantic correspondences
(isEquivalentTo, isSubConceptOf,
isSuperConceptOf, isCloseTo, isPartOf, isWholeOf,
isDisjointWith). Taking into account the query
semantics, Q should be routed only to the most
relevant peers that can contribute with it.
The goals of our experiments are twofold: (i) to
show how the original query semantics may be
preserved along routing processes; and (ii) in which
degree the query semantics is maintained. The
former is measured by using the Jaccard coefficient
(Han and Kamber, 2006). The latter is evaluated by
considering the precision measure (Rijsbergen,
1979). To this end, we consider a set of peers,
which are interconnected in a sequence, as follows:
P2178 to P2278; P2278 to P2378; P2378 to P2478
and P2478 to P2578.
We have defined a QSV threshold to be used as a
selection criterion to query forwarding. As the
threshold value depends on the weights associated to
the semantic correspondences in the matching
process, we have carried out some tests. We have
found out that the reformulated queries with QSV
above 0.4 were closer to the original query
semantics. Also, they have produced better query
results in the majority of the tested settings. As a
result, we have stated our QSV threshold as 0.4.
To evaluate our experiments we have used the
PDMS SPEED. The SPEED interface provides the
user with an ontology view that represents the query
submission peer schema. Queries may be submitted
by using SPARQL or DL languages and the
enriching variables are defined by the user (Souza et
al. 2009).
Given this scenario, we submitted a query Q
asking for GraduateStudent and Workshop at P2178
and all the enriching variables were selected. Figure
7 depicts the query submission interface. After the
query submission, a semantic reference SR of query
PreservingtheOriginalQuerySemanticsinRoutingProcesses
75

Figure 7: Query Submission Interface.
Q was dynamically produced. In this experiment,
SR={<GraduateStudent, GraduateStudent, 1.0>,
<GraduateStudent, Student, 0.8>,
<GraduateStudent, PhdStudent, 0.8>,
<GraduateStudent, MasterStudent, 0.8>,
<GraduateStudent, ResearchProject, 0.3>,
<GraduateStudent, Course, 0.3>,
<Workshop, Workshop, 1.0>,
<Workshop, Event, 0.8>,
<Workshop, Exhibtion, 0.7>,
<Workshop, Conference, 0.7>,
<Workshop, Meeting, 0.7>,
<Workshop, Lecture, 0.7>} was generated as the
semantic reference of Q.
At each routing step, the obtained reformulated
query was compared with SR. Thus, terms that may
produce semantic loss in the query reformulation at
hand were pruned.
At first, the query was expanded locally to
Q={GraduateStudent, MasterStudent, PhdStudent,
Workshop, Event, Lecture, Meeting, Conference} in
order to find out extra similar terms at the
submission peer P2178. Then, it was executed. Now,
considering the peer neighbours, Q was reformulated
in accordance with P2278 ontology.
Figure 8 presents the reformulation log between
the peers P2178 and P2278. The semantic
correspondence weight of each term is described in
parentheses. The query that will be forwarded to the
next peer is presented at the field Reformulated
Query After Pruning. The terms without DSR with
the query original terms are depicted at the field
Pruned Terms. The fields QPreserv, QEnrich and
QSV regard the measures that were assessed by
using the metrics defined in Section 4.2.
Figure 8: Reformulation log from P2178 to P2278.
All the expanded terms which were added to the
reformulated query are in accordance with the
original query semantics, i.e., only terms belonging
to the SR were included. In addition, to assess the
QPreserv, QEnrich and QSV values of the
reformulated query after pruning, it is necessary to
use the right weight of the semantic correspondence
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associated to each term present in the SR.
Thus, using the metrics defined in Section 4.2,
we assess the QPreserv, QEnrich and QSV as
follows:
 Query Preservation:
 
1
2
0,5
 Query Enrichment:
Since the query enrichment measure is assessed
by considering the original query terms, it is
rather important to know which expanded terms
have DSR (i.e., direct semantic relation) with
these original query terms. Taking into account
the SR, it is possible to identify that
MasterStudent, PhDStudent, and Student have
DSR with the original term GraduateStudent,
and; Lecture, Conference and Event have DSR
with the original term Workshop.




0.8  0.8  0.8

/3  

0.7  0.7  0.8

/3
2

  0.77 
 Query Semantic Value:
  0.5  0.77  1.27

The QSV value is used to point out if the
reformulated query is a suitable query that should be
forwarded. After the reformulation step, the query is
only routed to the next neighbour peer if the QSV is
above the semantic threshold of 0.4.
From P2278 to P2378, the original term
GraduateStudent was preserved and other terms
were added by the query enrichment step (Figure 9).
Figure 9: Reformulation log from P2278 to P2378.
In this reformulation example, the term Lecture
present in the field Reformulated Query has a
semantic correspondence weight equal to 1.0,
because it was obtained from an equivalence
correspondence. However this weight is not in
accordance with the weight associated to Lecture in
the Semantic Reference (SR). In SR, Lecture is a
close term to the original term Workshop, and, for
this reason, its value is 0.7.
To ensure the right assessment of the QSV,
QPreserv and QEnrich values, the semantic
correspondences weights are adjusted in accordance
with the SR. In the field Reformulated Query After
Pruning, the term Lecture appears with the new
value 0.7 instead of 1.0. In addition, from P2378 to
P2478 the original term Workshop was restored and
Lecture was lost (Figure 10).
Figure 10: Reformulation log from P2378 to P2478.
After query pruning, some terms were eliminated
and the semantic weights were adjusted. The term
Workshop was restored by means of a closeness
semantic correspondence, but in our experiments it
is an equivalent term to an original one of Q. So, the
weight associated to the term Workshop is 1.0 as we
can see in the field Reformulated Query After
Pruning (Figure 10).
Finally, from P2478 to P2578 the query is
reformulated and forwarded to P2578 (Figure 11).
Figure 12 summarizes the reformulated queries and
their respective obtained measures in this query
routing process illustration.
In this reformulation example, if no query
original term had been found, the reformulated
query would not be forwarded to P2578. QPreserv,
QEnrich and QSV would have the values of 0, 0.3
and 0.3, respectively. In this case, the QSV value of
0.3 would be below the semantic threshold of 0.4.
So, Q would not be forwarded to P2578.
5.1 Results Evaluation
In order to evaluate the experimental results, the
query routing process was carried out in two
different ways. In the former, QR
without_Pruning
, regards
Q reformulations without taking into account if the
expanded terms are semantically related to the terms
of the original query, i.e., without pruning. In the
PreservingtheOriginalQuerySemanticsinRoutingProcesses
77

Figure 12: Query reformulations from P2178 to P2578.
Figure 11: Reformulation log from P2478 to P2578.
latter, QR
pruning
, regards Q reformulations using our
pruning approach, i.e., with pruning.
We define that the SR is our "golden standard
query", which would be capable of producing the
best results for a particular user query. We use the
Jaccard coefficient to evaluate the reformulated
queries similarity with respect to SR in the query
routing process (QR
without_Pruning
and QR
pruning
).
Experimental results show the effectiveness of our
approach when comparing query reformulations
without prunning and with pruning. Figure 13
presents a summary of these similarity evaluation
results. We observe that QR
pruning
remains in a better
level of similarity with the SR. Moreover, in
QR
without_Pruning
, the reformulated query tends to
decrease the level of similarity with respect to the
SR as long as it is forwarded to the next peers.
Besides, we consider the precision measure
(Rijsbergen, 1979) as the ratio between the number
of recovered relevant terms in the reformulated
query and the total number of relevant terms in the
SR. This measure is computed by each peer which
participates in the query routing process.
As already mentioned, in our setting, peers are
semantically related. Thus, when a query is
forwarded to neighbour peers that are semantically
distant from the original one, it is likely that it may
occur a query semantic loss at each reformulation
step. Nevertheless, the experimental results show
that QR
pruning
has achieved a better level of precision.
This is due to the fact that during the query routing
process, the mechanism of pruning has eliminated
the reformulated query terms that could produce a
semantic loss of the original query. Figure 14
presents these results (i.e., the precision gain), by
comparing QR
pruning
with QR
without_pruning
. In other
words, the results indicate a precision increase when
the query routing process take the semantic pruning
into account.
Figure 13: Similarity evaluation.
Figure 14: Precision measure.
6 RELATED WORK
Although there is some research concerning
information loss analysis (Arenas et al., 2010, Roth
2011), few works (Kantere et al., 2009; Delveroudis
et al., 2009) discuss the issue of query semantics
preservation in query routing processes.
With respect to the first issue, Arenas and his
group (Arenas et al., 2010) address the problem of
providing foundations for metadata management by
analysing schema mappings. In this work, a query is
“target rewritable” if the mapping used to transfer
source information is able to answer the query by
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using the target data. The work of Roth (2011)
provides a completeness-driven query planning. It
forwards queries by considering peers and mappings
that promise large result sets and mappings with low
information loss. Histograms are used to estimate
the potential data contribution of mappings.
In terms of query semantics preservation,
Kantere et al. (2009) present GrouPeer, an adaptive,
automated approach to clustering peers based on
their common interests. This work allows peers to
individually decide whether to answer the
successively rewritten query or to automatically
rewrite its original version. The work of Delvedouris
et al. (2009) discusses the query semantic loss in
query reformulation process. It proposes an
algorithm that estimates the semantic loss of the
rewritten queries by means of syntactic differences
between the original query and the reformulated
queries.
Differently from these related works, our
approach enhances query routing processes by
assuring the semantics preservation of the original
query, as closely as possible. To this end, we use a
semantic reference to avoid the query semantic loss
and some metrics to assess the query semantic value.
This value is used to avoid forwarding queries with a
high semantic loss.
7 CONCLUSION AND FUTURE
WORKS
In this work, we address the problem of preserving
the original query semantics in query routing
processes. We argue that the reformulated queries
along the set of peers should be analyzed according
to the original required query semantics. Query
semantics evaluation may contribute not only to
minimize the query routing time, but also to reduce
the search space by considering only peers that can
indeed contribute with relevant answers.
To help matters, we have proposed a semantic
reference, which is built by considering a domain
ontology (available as a background knowledge)
according to a given submitted query. We use the
semantic reference to avoid semantic loss at query
reformulation time. Furthermore, we have specified
three metrics in the light of a query routing process,
namely: query preservation, query enrichment and
query semantic value. These metrics have been used
in some accomplished experiments, which have
shown some benefits. Particularly, we verify that we
can minimize the semantic loss and avoid
forwarding queries with a high semantic loss. As a
result, our approach helps to produce query results
which best meet the users’ needs.
As further work, we are integrating our approach
with an information quality management service.The
idea is joining the three defined metrics with other
ones in order to select the most relevant neighbour
peers to route queries.
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