=Paper=
{{Paper
|id=Vol-2032/om2017_Tpaper2
|storemode=property
|title=Semantic interactive ontology matching: synergistic combination of techniques to improve the set of candidate correspondences
|pdfUrl=https://ceur-ws.org/Vol-2032/om2017_Tpaper2.pdf
|volume=Vol-2032
|authors=Jomar da Silva,Fernanda Baião,Kate Revoredo,Jérôme Euzenat
|dblpUrl=https://dblp.org/rec/conf/semweb/SilvaBRE17
}}
==Semantic interactive ontology matching: synergistic combination of techniques to improve the set of candidate correspondences==
https://ceur-ws.org/Vol-2032/om2017_Tpaper2.pdf
Semantic Interactive Ontology Matching:
Synergistic Combination of Techniques to
Improve the Set of Candidate Correspondences
Jomar da Silva1 , Fernanda Araujo Baião1 , Kate Revoredo1 , and Jérôme
Euzenat2
1
Graduated Program in Informatics, Department of Applied Informatics
Federal University of the State of Rio de Janeiro (UNIRIO), Brazil
{jomar.silva, fernanda.baiao,katerevoredo}@uniriotec.br
2
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, F-38000 Grenoble France
Jerome.Euzenat@inria.fr
Abstract. Ontology Matching is the task of finding a set of entity cor-
respondences between a pair of ontologies, i.e. an alignment. It has been
receiving a lot of attention due to its broad applications. Many techniques
have been proposed, among which the ones applying interactive strate-
gies. An interactive ontology matching strategy uses expert knowledge
towards improving the quality of the final alignment. When these strate-
gies are based on the expert feedback to validate correspondences, it is
important to establish criteria for selecting the set of correspondences
to be shown to the expert. A bad definition of this set can prevent the
algorithm from finding the right alignment or it can delay convergence.
In this work we present techniques which, when used simultaneously, im-
prove the set of candidate correspondences. These techniques are incor-
porated in an interactive ontology matching approach, called ALINSyn.
Experiments successfully show the potential of our proposal.
Keywords: ontology matching, Wordnet, interactive ontology match-
ing, ontology alignment, interactive ontology alignment
1 Introduction
Ontology matching seeks to discover correspondences between entities of differ-
ent ontologies [1]. Ontology matching can be processed manually, semi-automatically
or automatically [1]. Among the semi-automatic approaches, the ones that follow
an interactive strategy stand out, considering the knowledge of domain experts
through their participation [2]. The involvement of a domain expert is not al-
ways possible, as it is an expensive, scarce and time-consuming resource. How-
ever, when possible, better results have been achieved compared with automatic
approaches.
An expert can be involved by giving his feedback to a correspondence, in-
dicating whether or not it belongs to the alignment. Therefore, defining the set
of correspondences to show to the expert is one of the problems of these in-
teractive techniques. If this set is not well defined, the final alignment may be
imprecise or incomplete, or convergence to a good alignment can be delayed.
Therefore, the scientific problem addressed in this paper is how to improve the
set of correspondences to receive expert feedback.
This paper proposes ALINSyn, an approach that uses two techniques – a
semantic and a structural – for the improvement of a given set of candidate
correspondences. The semantic technique works by temporarily removing corre-
spondences from the set of candidate correspondences. The structural technique
interactively places part of the correspondences taken by the semantic technique
back in the set of candidate correspondences. ALINSyn uses techniques used in
the ALIN [13] system, that participated in OAEI 2016.
To evaluate ALINSyn, we defined ALINBasic, a basic ontology matching
algorithm that generates and use a set of candidate correspondences to do the
matching. Each of the two ALINSyn techniques was added to ALINBasic in order
to modify the set of candidate correspondences generated by it, and the obtained
alignments were compared. ALINSyn was also compared to state-of-the-art in-
teractive ontology matching systems, showing the potential of our proposal.
This paper is structured as follows: Section 2 describes interactive ontol-
ogy matching, Section 3 describes the ALINBasic algorithm, section 4 describes
ALINSyn approach, by explaining its two steps, in section 5 the evaluation of
the approach is made and the section 6 is the conclusion.
2 Interactive Ontology Matching
An ontology O is represented as a labeled graph G = (V, E, vlabel, elabel).
The set of vertices V contains ontology entities such as concepts and properties.
Edges in E (E ⊆ V × V ) represent structural relationships between entities.
The edge labeling function elabel, which maps an edge (v, v) ∈ E to a subset of
the set SL of structural labels, which in turn specify the nature of the structural
relationships between entities (e.g., subclassOf). Let LL denote the set of lexical
labels associated with entities (e.g., name, documentation). Finally, the vertex
labeling function, vlabel : V × LL → String, maps a pair (e, l) ∈ V × LL to
a string corresponding to the value of the lexical label l (e.g., name) associated
with the entity e [3].
Given two ontologies O and O’, an ontology matching is the process that
aims to finding a set of correspondences (e, e’), where e and e’ are entities in
O and O’, respectively. Interactive ontology matching takes advantage of user
feedback to perform ontology matching.
Within the set of all possible correspondences between the entities of two
ontologies, in the context of the interactive ontology matching, we distinguish
two types of correspondences:
– Candidate correspondences are those possible correspondences that have
been selected to be presented to the expert but have not yet received deci-
sion,
– Classified correspondences are those possible correspondences that have been
selected to be presented to the expert and have received decision.
There are similarity measures, denoted sim, which map the possible corre-
spondence (e, e’) ∈ O×O’ to a real number in [0, 1].
According to Meilicke and Stuckenschmidt [4], ontology matching algorithms
that are based on the analysis of entity names usually have two phases:
– In the first phase, there is the creation of a set of candidate correspondences.
To reduce the need to classify all possible correspondences (all pairs of enti-
ties) between two ontologies as belonging or not to alignment, the algorithm
selects a subset called set of candidate correspondences;
– In the second phase, each correspondence in the set of candidate correspon-
dences is classified by the ontology matching algorithm. In an interactive
strategy, at least part of these correspondences is classified by the expert,
and the other part can be classified by some automatic technique.
3 ALINBasic Algorithm
When the ontology matching is done interactively, we have two quality measures
that are conflicting: the number of interactions with the expert and the qual-
ity of the generated alignment. It is interesting that a technique to be used in
an algorithm of ontology matching can improve one of these qualities without
worsening the other in an accentuated way. That is, to decrease the number of
interactions without decreasing proportionally the quality of the generated align-
ment, or to increase the quality of the generated alignment without increasing
proportionally the number of interactions with the expert.
In this paper two techniques will be presented, which used alone, cannot in-
crease one of the qualities without considerably worsening the other. The first
one, the semantic technique, decreases the number of interactions with the ex-
pert, but greatly decrease the quality of the generated alignment. The other,
structural technique, enhances the quality of the generated alignment, but in-
creasing a lot the number of interactions with the expert. But when used to-
gether, they can mitigate the disadvantages of each other, reducing the num-
ber of interactions without dramatically decreasing the quality of the generated
alignment.
To evaluate the results of the two proposed techniques, three algorithms will
be compared. An algorithm without the inclusion of any of the two techniques,
called ALINBasic, a second algorithm, with the inclusion of the semantic tech-
nique, called ALINSem, and a third one with the inclusion of both the semantic
and structural techniques, called ALINSyn. The two techniques are included in
the algorithms as steps of these algorithms, so ALINSem is equivalent to the
ALINBasic algorithm plus a semantic step that implements the semantic tech-
nique, and the ALINSyn algorithm is equivalent to the ALINSem algorithm plus
a structural step that implements the structural technique.
The ALINBasic algorithm has two phases, as described by Meilicke and
Stuckenschmidt [4]. The first phase selects candidate correspondences to be pre-
sented to the user. The second phase presents the selected candidate correspon-
dence to the user and assigns them to the classified correspondences. Hence, in
the end there are no candidate correspondences left.
In the phase of generating the candidate correspondences, only class corre-
spondences, not property correspondences, are chosen, therefore, the ALINBasic
algorithm finds only class correspondences.
The first phase of ALINBasic (Algorithm 1) will use the stable marriage
algorithm with size list limited to 1 [5][6], where the pair will be formed by
classes of the two ontologies to be aligned. Correspondences will be ordered by
decreased similarity.
The stable marriage algorithm will be executed six times, each time with a
different similarity metric (Jaccard, Jaro-Winkler, n-Gram, Wu-Palmer, Jiang-
Conrath and Lin) and the result of the six executions will form a set of corre-
spondences by the union of the six formed sets (Steps 1 to 4 of Algorithm 1). The
process of selecting the similarity metrics was based on two criteria: available
implementations and the result of these metrics in assessments, such as those
carried out in [7] and [8]. Wu-Palmer, Jiang-Conrath and Lin are metrics that
require a taxonomy to be computed [7], this taxonomy being provided, in this
algorithm, by Wordnet.
From the set of correspondences formed by the union of the six sets all
correspondence whose classes have exactly the same name will be classified as
true (Step 5 of Algorithm 1). The correspondences selected by the running of
stable marriage algorithm and not automatically classified will be the candidate
correspondences (Step 6 of Algorithm 1).
Algorithm 1 Candidate correspondence generation
Input: Two ontologies to be aligned
Output: Candidate correspondences
1: for Each one of the similarity metrics: Jaccard, Jaro-Winkler, n-Gram, Wu-
Palmer, Jiang-Conrath and Lin do
2: Run stable Marriage Algorithm forming the set Asim (being sim the cor-
responding similarity metric)
3: end for
4: Let A = AJaccard ∪ AJaro-Winkler ∪ An-Gram ∪ AWu-Palmer ∪ AJiang-Conrath ∪
ALin
5: Let B = Correspondences, from A, automatically classified as true by the
fact that their entities have the same name
6: Set of candidate correspondences = A - B
Then begins the classification phase of the candidate correspondences of the
ALINBasic. At this phase all the candidate correspondences will be presented
to the expert to receive his feedback.
For this, the concept of interaction with the expert will be used. An inter-
action with the expert corresponds to a question asked about at most three
correspondences, as long as they pair-wisely have at least one of the entities
in common. This is compliant with the OAEI definition [10]. For example, if
the following correspondences are shown to the expert at the same time (Con-
ferenceChair,Chair), (Chairman,Chair) and (Chairman,AssociatedChair), they
will be counted as only one interaction since each correspondence has at least
one entity of another correspondence. The number of interactions will be used
as a comparison criterion between the various executions shown in this paper.
The ALINBasic algorithm can be seen in Algorithm 2.
Algorithm 2 ALINBasic
Input: Two ontologies to be aligned
Output: Alignment between the two ontologies
1: Run candidate correspondence generation (Algorithm 1)
2: for Each candidate correspondence do
3: Receive feedback (the candidate correspondence is transformed to clas-
sified correspondence)
4: end for
4 ALINSyn Algorithm
4.1 Improving the Set of Candidate Correspondences
The objective of the ALINSyn algorithm is to decrease the number of interactions
with the expert without decreasing in the same proportion the quality of the
generated alignment. To achieve this objective, two steps, one semantic step and
one structural step, are added to the ALINBasic algorithm to improve the set
of candidate correspondences.
We first introduce another type of correspondence:
– Temporarily suspended correspondences are correspondences that are no
longer candidate correspondences because of the semantic step. These corre-
spondences can once again be candidate correspondences after the structural
step.
The semantic step transforms some candidate correspondences to temporarily
suspended correspondences. The structural step can transform some temporarily
suspended correspondences to candidate correspondences again.
At the end of the non-interactive phase, by the use of the semantic step, all
candidate correspondences that are not semantically equivalent will be trans-
formed to temporarily suspended correspondences. In the interactive phase, by
the use of the structural step, after each interaction with the expert, the expert’s
feedback can transform temporarily suspended correspondences in candidate cor-
respondences if they have a particular structural relationship with a candidate
correspondence that received positive feedback.
4.2 Semantic Step
The action of this step is to transform all candidate correspondences with se-
mantically different entity names to temporarily suspended correspondences.
The step will be added to the ALINBasic algorithm at the end of the generation
phase.
The semantic step uses Wordnet. Wordnet consists of synonym sets called
synsets [9]. A synset denotes a group of terms with the same meaning. The same
term may appear in various synsets, as long as it has several meanings.
Comparison of entity names A head noun of a phrase is a noun to which
all other terms are dependent [11]. Only correspondences relating entities whose
name head nouns are in the same Wordnet synset will remain in the set of
candidate correspondences after the semantic step. Before comparing the two
entity names, a pre-processing step is necessary in order to extract the correct
terms to be compared. An entity name can be atomic or composed. In the latter
case, our approach searches for the head noun, and only this head noun will be
used to compare the two entities. The rule we used for detection of head noun
can be summarized as follows:
1. If the name contains a preposition (e.g. HeadOfDepartment) then the head
noun is the token before the preposition.
2. Otherwise the head noun is the last token in the name.
Algorithm 3 Semantic step
Input: Candidate correspondences
Output: Temporarily suspended correspondences (ex-candidate correspon-
dences)
1: for Each candidate correspondence do
2: Choose the head noun of each entity of the name of the correspondence
3: Put the head noun of each name in the canonical form
4: if The two head nouns are not in the same wordnet synset then
5: Transform the candidate correspondence to temporarily suspended
correspondence
6: end if
7: end for
Example of the semantic step The semantic step can be seen in the Algo-
rithm 3. To illustrate the semantic step we assume that we have the candidate
correspondences selected by Algorithm 1 shown in Table 1. The first correspon-
dence to be analyzed will be (Author, Regular Author) (step 1 of Algorithm 3).
The head noun of Author is Author, since it has only one word. The head noun
for Regular Author is Author, because it does not have a preposition and the
last word is Author (step 2). The two head nouns are already in canonical form
(step 3) and as they are the same word they are in the same synset, so they are
not transformed to temporarily suspended correspondences.
Table 1. Correspondences before and after the semantic step, and after the first run
of structural step
before after after the first run of
e e’
semantic step semantic step structural step
Candidate Candidate Candidate
Author Regular author
correspondence correspondence correspondence
Temporarily Temporarily
Candidate
Chairman Chair suspended suspended
correspondence
correspondence correspondence
Temporarily
Candidate Candidate
Co-author Regular author suspended
correspondence correspondence
correspondence
Candidate Candidate Candidate
Paper Paper
correspondence correspondence correspondence
Candidate Candidate Candidate
Paper Abstract Abstract
correspondence correspondence correspondence
Candidate Candidate Classified
Person Person
correspondence correspondence as true
Temporarily Temporarily
Candidate
Subject Area Abstract suspended suspended
correspondence
correspondence correspondence
Temporarily Temporarily
Candidate
Subject Area Program Committee suspended suspended
correspondence
correspondence correspondence
The second correspondence in the table is the correspondence (Chairman,Chair)
(step 1). Chairman is considered a word because a term is only divided into
words if it has hyphen, white space or is in camelcase (step 2). Since the two
are in the canonical form (step 3) of the word their synsets are compared in
Wordnet, and they are different. It is important to note that the most common
meanings of words are searched for in wordnet, so Chair is the object of sitting
and not Boss. Therefore this correspondence will be transformed to temporarily
suspended correspondences (step 5).
The result after following these steps for all correspondences is shown in
Table 1, in the column ’after the semantic step’.
Algorithm 4 ALINSyn
Input: Two ontologies to be aligned
Output: Alignment between the two ontologies
1: Run candidate correspondence generation (Algorithm 1)
2: Run semantic step (Algorithm 3)
3: for Each candidate correspondence do
4: Receive feedback (the candidate correspondence is transformed to clas-
sified correspondence)
5: Run structural Step (Algorithm 5 )
6: end for
With the inclusion of the semantic step, the algorithm will be called ALIN-
Sem. As an illustration, this algorithm is the same as the algorithm ALINSyn
(Algorithm 4) without the inclusion of step 5 (Run structural step). The results
of ALINSem will be compared to the results of ALINSyn with the objective
of verifying if the combined use of the semantic step and the structural step
improves the result achieved by the use of the semantic step alone.
Algorithm 5 Structural Step
Input: Temporarily suspended Correspondences, Classified correspondences
Output: Candidate Correspondences (ex-temporarily suspended correspon-
dences)
1: for Each temporarily suspended correspondence do
2: if The two classes of the temporarily suspended correspondence are sub-
classes of classes of a correspondence classified as true then
3: Transform the temporarily suspended correspondence to candidate
correspondence
4: end if
5: end for
4.3 Structural Step
When only the semantic step is applied, experiments showed that the number of
interactions with the expert were reduced, i.e. convergence was reached faster,
however the final alignment lost in quality. This is because some true correspon-
dences have been taken from the set of candidate correspondences because of
semantic step. The main goal of the structural step is to recover part of the
quality lost through the use of the semantic step by transforming some tem-
porarily suspended correspondences again to candidate correspondences.
At each iteration, all temporarily suspended correspondences that are formed
by subclasses of the classes of the correspondences that received positive feedback
from the expert are transformed again to candidate correspondences. Tests were
performed again using the two techniques, which showed that the use of both
techniques makes the number of interactions decrease considerably, but with a
much lower quality loss, in relation to the results obtained with the ALINBasic
algorithm. The structural step can be seen in Algorithm 5.
Fig. 1. Correspondences with classes that are subclasses of other correspondence classes
To illustrate the technique let us assume the situation described in Figure 1,
where Co author is a subclass of Person in the cmt ontology and Regular author
is a subclass of Person in the Conference ontology. Let us assume that the corre-
spondence A (Person, Person) is a candidate correspondence and correspondence
B (Co author, Regular author) is a temporarily suspended correspondence. If the
correspondence A receive positive feedback, the correspondence B by having its
classes that are subclasses of the classes of A is transformed to candidate corre-
spondence. The result of the structural step can be seen in Table 1 in the column
’after the first run of the structural step’. With the inclusion of the structural
step in the interactive phase, the algorithm is called ALINSyn and can be seen
in the Algorithm 4.
5 Evaluation Overview and Designed Analysis
The goal of the ALINSyn approach is to reduce the number of interactions with
the expert without greatly diminishing the quality of the generated alignment.
Thus a first research question is:
RQ1: Does the semantic step allow the ontology matching strategy to de-
crease the number of interactions with the expert? This question is answered
with the use of the semantic step in the ALINBasic algorithm, as we see in the
section ”Analysis of the Results”, which shows that the number of interactions
with the expert has been reduced, but with a great drop in quality. That is why
it is important to address other research questions.
RQ2: Can the expert feedback reduce the quality loss by the use of the
semantic step?
RQ3: Does the use of both, semantic step and structural step together, gen-
erate an alignment with quality and number of interactions compatible with the
state of the art proposals?
5.1 Conference dataset
Results obtained in the interactive matching of OAEI 2016 using the conference
dataset were used to compare with the state of the art.
The OAEI interactive track is performed with percentages of expert correct-
ness, from 70% to 100%. This paper has taken into consideration, for the eval-
uation of the execution of the ALINSyn and of other tools, 100% of correctness
by the expert.
5.2 Analysis of the Results
After using the semantic step the results presented in Table 2 (ALINSem row)
were reached, which shows that the use of the semantic step decreases the number
of expert interactions, which responds to ’RQ1: Does the semantic step allow the
ontology matching strategy to decrease the number of iterations with the expert
?’, but there has been a sharp drop in quality, which shows the need to answer
the question ’RQ2: Can the expert feedback reduce the quality loss by using the
semantic step?’.
The recovery in the quality of the generated alignment was attempted by the
use of structural step. After the inclusion of this new step the results shown in
Table 2 (ALINSyn row) were reached. That shows that the goal of the ALIN-
Syn was achieved using the two techniques. The number of interactions with the
expert decreased greatly, from 619 to 219, with the quality decreasing propor-
tionally much less, the f-measure was from 0.79 to 0.75, what responds to RQ2:
Can the expert feedback reduce the quality loss by the use of the semantic step
?. The result achieved is due to the combined effect of the joint use of the two
techniques.
If we use only the semantic step we have a good decrease in the number of
interactions with the expert, but with a sharp drop in quality. The subsequent
use of the structural step, interactively, causes some of the lost quality to be
recovered.
If we use only the structural step, without using the semantic step before,
with all possible correspondences, not only the temporarily suspended corre-
spondences, we would have an increase in quality, but a large number of cor-
respondences would be added to the set of candidate correspondences, which
would make the number of interactions with the expert too large (Table 2,
ALINStr row). The transformation of candidate correspondences into temporar-
ily suspended correspondences, through the semantic step, and the search, by
the structural step, only among the temporarily suspended correspondence re-
duces the search space, which means that the number of interactions with the
expert do not go up explosively.
The combined use of the two techniques results in a more balanced result,
with a reduction in the number of interactions without a big loss of quality (
Table 2, ALINSyn row ).
Table 2. Comparison between different matching executions
NI Precision F-measure Recall
ALINBasic 619 0.92 0.79 0.70
ALINSem 152 0.90 0.69 0.57
ALINStr 3539 0.93 0.84 0.78
ALINSyn 219 0.91 0.75 0.65
5.3 Comparison among Tools that Participated in the OAEI
Interactive Conference Track
OAEI provides a comparison among tool performance in the ontology matching
process each year, and one of the ontology groups used is the conference dataset
used in this paper [12].
Table 3 shows a comparison of some tools that participated in the OAEI
2016 interactive conference track. NI means number of interactions. In each
interaction there can be up to three questions. ”%” is the ratio of the number of
interactions to the number of possible correspondences among all the alignments
of the conference dataset.
Table 3 compares the performance of ALINSyn with some interactive tools
that participated in OAEI 2016, with the expert hitting 100% of the answers
in relation to the conference dataset. The use of the techniques shown in this
work generates a high quality alignment, in cases where the expert does not make
errors, what responds to ’RQ3: Does the use of the two techniques, semantic step
and structural step together, generate an alignment with quality and number of
interactions compatible with the state of the art ?’. The use of the two techniques
combined puts ALINSyn among the best tools in the evaluation of OAEI 2016,
when the expert hits 100% of the interactions.
Table 3. Comparison between some OAEI 2016 conference dataset interactive tracking
tools and ALINSyn
Number of questions NI % Precision F-measure Recall
AML 270 271 0.215 0.912 0.799 0.711
ALINSyn 483 219 0.174 0.915 0.754 0.652
LogMap 142 142 0.113 0.886 0.723 0.610
XMap 4 4 0.003 0.837 0.681 0.574
6 Conclusion
Progress in information and communication technologies has made a large num-
ber of data repositories available, but with a great deal of semantic heterogeneity,
which makes it difficult to integrate. A process that has been used to solve this
problem is the ontology matching, which tries to discover the existing correspon-
dences between the entities of two distinct ontologies, which in turn structures
the concepts that define the data stored in each repository.
This work presented an interactive approach for ontology matching, based on
manipulation of the set of candidate correspondences with techniques to decrease
the number of interactions with the expert, without greatly reducing the quality
of the alignment.
Two techniques were combined, one semantic and the other structural. The
goal of the semantic technique was to decrease the number of interactions with
the expert. The structural technique came in support of the semantic technique,
and its objective was to decrease the quality loss resulting from the decrease in
the number of interactions with the expert.
In order to evaluate if the techniques generated a decrease in the number of
interactions without significantly lowering the quality, the executions of a basic
algorithm with and without the techniques were compared, which showed that
the techniques, when combined, reach their goal.
In addition, the quality of the alignment provided by the ALINSyn approach
was compared to state of the art tools that have participated in the track of
interactive ontology matching in OAEI 2016. The results obtained show that
ALINSyn generates an alignment with a good quality in comparison to other
tools, with regard to precision, recall and f-measure, when the expert never
makes mistakes, keeping the number of interactions within the range achieved
by the other tools.
The third author was partially funding by project PQ-UNIRIO N01/2017 (”
Aprendendo, adaptando e alinhando ontologias:metodologias e algoritmos.”) and
CAPES/PROAP.
The fourth author was partially funding by ’CNPq Special visiting researcher
grant (314782/2014-1)’.
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