=Paper=
{{Paper
|id=Vol-2288/om2018_Tpaper3
|storemode=property
|title=Interactive ontology matching: using expert feedback to select attribute mappings
|pdfUrl=https://ceur-ws.org/Vol-2288/om2018_LTpaper3.pdf
|volume=Vol-2288
|authors=Jomar Silva,Kate Revoredo,Fernanda Baião,Jérôme Euzenat
|dblpUrl=https://dblp.org/rec/conf/semweb/SilvaRBE18
}}
==Interactive ontology matching: using expert feedback to select attribute mappings==
https://ceur-ws.org/Vol-2288/om2018_LTpaper3.pdf
Interactive Ontology Matching: Using Expert
Feedback to Select Attribute Mappings
Jomar da Silva1 , Kate Revoredo1 , Fernanda Araujo Baião1 , and Jérôme
Euzenat2
1
Department of Applied Informatics
Federal University of the State of Rio de Janeiro (UNIRIO), Brazil
{jomar.silva, katerevoredo, fernanda.baiao}@uniriotec.br
2
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, F-38000 Grenoble France
Jerome.Euzenat@inria.fr
Abstract. Interactive Ontology Matching considers the participation
of domain experts during the matching process of two ontologies. An
important step of this process is the selection of mappings to submit
to the expert. These mappings can be between concepts, attributes or
relationships of the ontologies. Existing approaches define the set of
mapping suggestions only in the beginning of the process before expert
involvement. In previous work, we proposed an approach to refine the set
of mapping suggestions after each expert feedback, benefiting from the
expert feedback to form a set of mapping suggestions of better quality.
In this approach, only concept mappings were considered during the
refinement. In this paper, we show a new approach to evaluate the benefit
of also considering attribute mappings during the interactive phase of
the process. The approach was evaluated using the OAEI conference
data set, which showed an increase in recall without sacrificing precision.
The approach was compared with the state-of-the-art, showing that the
approach has generated alignment with state-of-the-art quality.
Keywords: ontology matching, Wordnet, interactive ontology matching,
ontology alignment, interactive ontology alignment
1 Introduction
Ontology matching aims to discover correspondences (mappings) between entities
of different ontologies [1]. One of its strategies is the interactive one. Interactive
ontology matching approaches consider the knowledge of domain experts during
the matching process. The interaction with the user can be used to improve the
results over fully automatic approaches [2]. An important step of this strategy is
the definition of the set of mappings to be submitted to the expert for feedback.
This set to be submitted to the expert was called, in this paper, set of mapping
suggestions. Existing approaches [3][4][5][6][7][8][9][10][11][12][13][14][15] define
this set before the interaction with the expert begins; thus, the approaches do
not use expert feedback to select mappings to the set of mapping suggestions.
In previous work [16], we combined a structural and a semantic technique
for interactively considering the expert feedback in the revision of the set of
mapping suggestions, but taking into account only concept mappings. However,
considering also the properties of these concepts may bring a better integration
of the ontologies.
In this work, we propose ALINAttr to evaluate the benefit of also considering
attribute mappings during the interactive strategy. The attribute mappings
suggested are associated with the concept mappings evaluated by the expert;
therefore, they are more prone to be correct and potentially increase the recall
compared with existing strategies that automatically include attribute mappings
[14][15].
The evaluation results evidenced the benefit of considering attributes during
the interactive phase, using a heuristic for choosing the attribute mappings in-
spired on the Stable Marriage Problem [17][18]. In addition, the current approach
was compared to the state-of-the-art.
The rest of this paper is organized as follows. Section 2 reviews interactive
ontology matching. Section 3 presents the approach, which is called ALINAttr ,
and its implementation. Section 4 describes our evaluation methodology and
discusses experimental results. Finally, section 5 concludes the paper.
2 Interactive Ontology Matching
An interactive ontology matching process is an ontology matching process con-
sidering the involvement of domain experts. In this paper, we consider this
involvement as the domain experts providing feedback about mappings of ontolo-
gies entities, that is, mapping are presented to the expert who replies which of
them should be accepted or rejected. Therefore, the approach takes advantage of
the knowledge of domain experts towards finding an alignment.
The most relevant steps in this process are the selection of the mappings to
receive expert feedback and the propagation of this feedback. Furthermore, the
propagation may also impact the mappings selected for future expert feedback.
The different existing approaches for interactive ontology matching vary in
techniques for these two steps.
In the selection step, the existing approaches of interactive ontology matching
use similarity metrics to select the set of mapping suggestions. The similarity
metric is a function that returns a numeric value, indicating the similarity between
the two entities of a mapping, according to some criterion. An approach can
associate one or several similarity values, each of a different similarity metric, to
a mapping.
In the selection step, the approaches can use multiple matchers, algorithms
that receive, as input, entities and generate, as output, mappings. Each matcher
can use different similarity metrics, among other features. At the end of the selec-
tion step, the results of these matchers can be combined and filtered generating
the set of mapping suggestions [13].
In the propagation step, user feedback can be used in different ways. Some
approaches automatically classify some mapping suggestions using a threshold, a
value that indicates whether a mapping should be automatically accepted (in
some cases rejected) if its similarity values are greater (or smaller) than it. Expert
feedbacks are used to calculate this threshold [3][4][5][6][7]. Some approaches
automatically classify some mappings of the set of mapping suggestions using a
classifier. These approaches use expert feedbacks to create the training dataset for
learning the classifiers [8][9]. Some approaches use expert feedbacks to modify the
weight of similarity metrics [5][6][10] or to directly change the value of similarity
metrics [11][12]. Expert feedbacks are also used to remove mapping suggestions
from the set of mapping suggestions [13][14][15].
3 The ALINAttr Approach
In this section, we describe our approach, ALINAttr , for interactively matching
two ontologies. ALINAttr , at each interaction, uses expert feedback to remove
mapping suggestions and include new attribute mapping suggestions into the set
of mapping suggestions.
The ALINAttr top-level algorithm (Algorithm 1) starts with a pair of ontolo-
gies (O and O0 ) and a set of similarity metrics (SoM). Then, it splits in two main
steps. The first one defines the initial mapping suggestions (SMS) and the initial
alignment (A) (line 1 to line 17 of Algorithm 1) and the second one interactively
receives expert feedback to a mapping suggestion and propagate it (lines 18 to
29 of Algorithm 1).
The initialization step starts collecting all concepts of ontology O (SCO)
and O0 (SCO0 ) and then for each similarity metric (SimM) a set of mapping
suggestions is found using the simple matching algorithm (line 5 of Algorithm
1). This algorithm treats the matching problem as a stable marriage problem
with size list limited to 1 [17][18], i.e., the algorithm only selects one mapping if
similarity value between the two entities of the mapping is the highest considering
all the mappings with at least one of these entities (Algorithm 2). At this moment
only concept mappings, not property mappings, are chosen. The initial set of
mapping suggestions is defined as the union of the mapping suggestions found for
each similarity metric (lines 6 to 10 of Algorithm 1). The mappings in which their
entity names are the same are placed in the alignment and removed from the
set of mapping suggestions (lines 12 to 17 of Algorithm 1). Moreover, ALINAttr
inserts into the set of mapping suggestions attribute mappings associated with
these concept mappings placed in the alignment (line 15 of Algorithm 1). The
approach uses the structural attribute selection technique, which will be explained
later, to choose the attribute mappings.
After defining the initial set of mapping suggestions and the initial alignment,
ALINAttr moves to the interactive step, in which the mapping suggestions
receive the feedback of the expert (line 20 of Algorithm 1). If the expert accepts
a mapping suggestion, then it is included in the alignment (line 23 of Algorithm
Algorithm 1 ALINAttr Top-level Algorithm
Input: O, O0 , SoM
Output: A
/*Initialization step*/
1: A = ∅; SMS = ∅;
2: SCO ← all concepts of O;
3: SCO 0 ← all concepts of O 0 ;
4: for each SimM ∈ SoM do
5: M ← Simple Matching Algorithm(SCO,SCO0 ,SimM);
6: for each m(e, e0 ) ∈ M do
7: if m(e, e0 ) 6∈ SMS then
8: add m(e, e0 ) to SMS;
9: end if
10: end for
11: end for
12: for each m(e, e0 ) ∈ SMS do
13: if name of e = name of e0 then
14: move m(e, e0 ) from SMS to A;
15: SMS ← Structural Attribute Selection Technique(m(e, e0 ),SoM);
16: end if
17: end for
/*Interactive step*/
18: while SMS 6= ∅ do
19: select m(e, e0 ) ∈ SMS with the biggest sum of similarity metrics;
20: receive expert feedback on m(e, e0 );
21: remove m(e, e0 ) from SMS;
22: if m(e, e0 ) is accepted then
23: add m(e, e0 ) to A;
24: SMS ← Remove Mappings with Equal Entities(SMS,m(e, e0 ));
25: if m(e, e0 ) is a concept mapping then
26: SMS ← Structural Attribute Selection Technique(m(e, e0 ),SoM);
27: end if
28: end if
29: end while
30: return A
1). ALINAttr simulates the expert feedback by accessing a reference alignment.
Session 4 further explains the reference alignment.
Up to this point, as we use several similarity metrics and the set of mapping
suggestions is the union of the formed sets made for each metric there may be
mappings with one of the entities equal. Since we want to generate a one-to-one
alignment, once one of these mappings is accepted, the others will be rejected
and removed from the set of mapping suggestions (line 24 of Algorithm 1) It is
worth noting that ALINAttr uses expert feedback to reject these mappings. If
Algorithm 2 Simple Matching Algorithm
Input: SE, SE 0 , SimM
Output: M
1: for each e ∈ SE do
2: maxe0 ← max
0 0
SimM (e, e0 );
e ∈SE
3: maxe ← max
00
SimM (e00 , maxe0 );
e ∈SE
4: if e = maxe then
5: add m(e, maxe0 ) to M;
6: end if
7: end for
8: return M;
ALINAttr would automatically reject these mappings, it would probably make
mistakes.
At this point, the ALINAttr approach uses the structural attribute selection
technique which will try to select, based on expert feedback, the best attribute
mappings to be included into the set of mapping suggestions. The assumption
behind the structural attribute selection technique is that if the attributes in an
attribute mapping are attributes of concepts of a concept mapping, then this
attribute mapping is more likely to be correct.
Algorithm 3 describes the structural attribute selection technique. It considers
all attributes of the concepts of the input accepted mapping (lines 1 and 2 of
Algorithm 3) and for each similarity metric it uses the simple matching algorithm
to define attribute mapping suggestions. The output of the algorithm is the union
of the set of attribute mappings found for each similarity metric.
Algorithm 3 Structural Attribute Selection Technique
Input: m(c, c0 ),SoM
Output: SMS
1: SA ← all attributes of c;
2: SA0 ← all attributes of c0 ;
3: for each SimM ∈ SoM do
4: M ← Simple Matching Algorithm(SA,SA0 ,SimM);
5: for each m(a, a0 ) ∈ M do
6: if m(a, a0 ) 6∈ SMS then
7: add m(a, a0 ) to SMS;
8: end if
9: end for
10: end for
11: return SMS
Instead of selecting mappings between concepts of the two ontologies, like
in the ALINAttr top-level algorithm, the structural attribute selection technique
(Algorithm 3) uses the simple matching algorithm (Algorithm 2) to select map-
pings between attributes of the concepts in an accepted mapping. The use of
the simple matching algorithm proved to be efficient in choosing the attribute
mappings to be inserted in the set of mapping suggestions, as will be shown later
in this paper.
ALINAttr was implemented in Java using the following Java APIs: Stanford
coreNLP API [19] with a routine to put a word in canonical form; Simmetrics API
[20], with string-based similarity metrics; HESML API [21], with Wordnet [22]
based linguistic metrics; And the Alignment API [23], which contains routines
for handling ontologies written in OWL. The most frequent synsets of words
are used to calculate semantic similarities. To find this synset is used the WS4J
API3 .
4 Experimental Evaluation
In this section, we evaluate our approach for interactive ontology matching
considering attribute mappings.
4.1 Configuration of the experiment
The evaluation is designed towards answering three research questions:
RQ1: Does the consideration of attribute mappings improve the quality of
the final alignment?
RQ2: Does the use of expert feedback for the inclusion of attribute mappings
in the set of mapping suggestions improve the quality of the final alignment?
RQ3: Does the simple matching algorithm between the attributes of the
concepts improve the quality of the final alignment?
The quality of an alignment is generally measured by F-measure, which is the
harmonic mean between recall and precision. In an interactive approach another
quality metric should be taken into account, the number of interactions with the
expert that was necessary to achieve the alignment. The lower the number of
interactions, the better. Thus, the two quality metrics were used to answer the
research questions in this work.
3
’WS4J’. Available at https://github.com/Sciss/ws4j Last accessed on Jan, 16, 2018.
Algorithm 4 Attribute Inclusion Technique for ALINAttrAuto
Input: O, O0 , SoM
Output: SMS
1: SA ← all attributes of O;
2: SA0 ← all attributes of O 0 ;
3: for SimM ∈ SoM do
4: M ← Simple Matching Algorithm(SA,SA0 ,SimM);
5: for each m(e, e0 ) ∈ M do
6: if m(e, e0 ) 6∈ SMS then
7: add m(e, e0 ) to SMS;
8: end if
9: end for
10: end for
11: return SMS
Algorithm 5 Structural Attribute Selection Technique for ALINAttrF Back
Input: m(c, c0 ),SoM
Output: SMS
1: SA ← all attributes of c;
2: SA0 ← all attributes of c’;
3: for each a ∈ SA do
4: for each a0 in ∈ SA0 do
5: add m(a, a0 ) to SMS;
6: end for
7: end for
8: return SMS
Towards answering these questions, some variations of ALINAttr were con-
sidered:
• ALINW Attr : This variation didn’t take into account attribute mappings, i.e.,
only concept mappings compose the set of mapping suggestions. For that, the
ALINW Attr variation removes the calls for the structural attribute selection
technique (Algorithm 3) in line 15 and from line 25 to line 27 of the ALINAttr
top-level algorithm (Algorithm 1).
• ALINAttrAuto : This variation includes the attribute mappings only in the ini-
tialization step, i.e., not considering expert feedback. For that, the ALINAttrAuto
variation removes the calls for the structural attribute selection technique
(Algorithm 3) in line 15 and from line 25 to line 27 in the ALINAttr top-level
algorithm (Algorithm 1) and includes a call for attribute inclusion tech-
nique for ALINAttrAuto (Algorithm 4) in the ALINAttr top-level algorithm
(Algorithm 1) after line 17.
• ALINAttrF Back : This variation includes all attribute mappings related to
the accepted concept mapping into the set of mapping suggestions, i.e., this
variation doesn’t use the simple matching algorithm (Algorithm 2) to reduce
the number of included attribute mappings. For that, the ALINAttrAuto
variation makes a call to the structural attribute selection technique for
ALINAttrF Back (Algorithm 5) instead of a call to the structural attribute
selection technique (Algorithm 3) in lines 15 and 26 of the ALINAttr top-level
algorithm (Algorithm 1).
OAEI provides several data sets, which are sets of ontologies, to be used in
the evaluation of ontology matching tools. From the data sets provided by OAEI,
the only one that contained documentation of attributes and that had size that
allowed the execution of ALINAttr is the conference data set. Therefore, the
conference data set was used to evaluate the approach. OAEI provides reference
alignments, which are alignments that contains the mappings that are believed
to be correct, between the pairs of the ontologies of the conference data set. In
the ALINAttr approach, a reference alignment query simulates the consult to
the expert. The selection of the similarity metrics was based on two criteria:
available implementations and the result of these metrics in assessments, such as
those carried out in [24] and [25]. Based on [24] and [25], ALINAttr uses Jaccard,
Jaro-Wrinkler and n-gram string-based metrics and the Resnick, Jiang-Conrath
and Lin linguistic metrics. Resnick, Jiang-Conrath and Lin are metrics that
require a taxonomy to be computed [24], this taxonomy being provided, in this
algorithm, by Wordnet [22].
4.2 Results
The results in terms of number of interactions (NI), precision, recall and F-measure
can be seen in Table 1.
Table 1. Comparison between different ALINAttr variations executions with Conference
Data Set
Total of questions NI Precision F-measure Recall
ALINW Attr 1183 582 0.921 0.783 0.692
ALINAttrAuto 1574 739 0.905 0.809 0.741
ALINAttrF Back 1321 631 0.924 0.817 0.741
ALINAttr 1242 614 0.924 0.815 0.738
In each interaction with the expert, up to three mapping suggestions can be
presented, since each mapping suggestion has one entity in common with another
mapping suggestion of the interaction [26].
Comparing ALINW Attr with the other three approaches, that considered
attributes mappings, we can see the improvement in the recall, which was expected
since other mappings were evaluated. It is also possible to notice an increase in
the number of interactions with the expert. Therefore, the inclusion of attribute
mappings without taking into account the expert feedback generates an increase
in the F-measure, but also an increase in the number of interactions with the
expert leading to an inconclusive answer to the RQ1 question.
Comparing ALINAttrAuto , which did not take into account the feedback of the
expert, with ALINAttrF Back and ALINAttr , which considered it, we can observe
an improvement in the F-measure and a decrease in the number of interactions
with the expert. This demonstrates that using expert feedback is a good practice,
answering positively RQ2. It is important to note that it was assumed that the
expert did not make mistakes. Therefore, these results are valid when the expert
makes no mistakes.
Addressing RQ3, i.e., comparing ALINAttr with ALINAttrF Back towards
evaluating the benefit of reducing the number attribute mappings by using the
simple matching algorithm, we observed a decrease in the number of interactions
with almost no loss of quality of the alignment, what answer positively to the
RQ3 question.
4.3 Comparison between tools that participated in the OAEI
interactive conference track
Table 2. Comparison between some the tools of OAEI 2017 Conference Data Set
Interactive Tracking and ALINAttr and ALINAttr+Syn with 100% hit rate
Number of questions NI Precision F-measure Recall
ALINAttr 1242 614 0.924 0.815 0.738
AML [14][27] 270 271 0.912 0.799 0.711
LogMap [15][28] 142 82 0.886 0.723 0.610
XMap [29][30] 4 4 0.837 0.678 0.57
ALINAttr+Syn 443 205 0.918 0.782 0.692
OAEI annually provides a comparison between ontology matching tool perfor-
mances, and one ontology group used is the conference dataset, used in this paper
[31]. Table 2 depicts a comparison between some the tools that participated in
the OAEI 2017 interactive conference track and ALINAttr and ALINAttr+Syn .
The tools AML, LogMap, and XMAP (Table 2) are interactive ontology
matching tools. This tools, like ALINAttr , include attribute mappings in the
generated alignment but this inclusion is done in a non-interactive way, not
taking into account the expert feedback.
The Table 2 depicts results with the expert hitting 100% of the answers. The
results showed that ALINAttr generated a high level result when running the
conference data set when the expert hit 100% of the answers, but with a very
large number of interactions when compared to the other tools.
To verify the quality of ALINAttr if it uses a number of interactions more
compatible with the other tools, two techniques, described in [16], were added to
ALINAttr . In [16], these techniques proved to be very efficient in reducing the
number of interactions without significantly reducing quality. The inclusion of
the two techniques generates the results shown on line ’ALINAttr+Syn ’ of Table
2 and shows that, as the quality as the number of interactions, ALINAttr+Syn is
good when compared to other tools.
5 Conclusion
Ontology matching is a necessary step for establishing interoperation among
semantic web applications. Its purpose is to discover mappings between the
entities of at least two ontologies. The quality of an alignment generated by a
matching approach is generally measured by F-measure, which is the harmonic
means between recall and precision. Another quality metric, when the ontology
matching process is interactive, is the number of interactions with the expert.
An important step in the process of interactive ontology matching is the
definition of the set of mapping suggestions, that is, the set of mappings that
will be shown to the expert. The problem seen in this paper is how to efficiently
include attribute mappings into the set of mapping suggestions. The ALINAttr
approach includes attribute mappings taking advantage of the expert feedback,
of the structures of the involved ontologies, as well as the use of the simple
matching algorithm. Experimental results showed the benefit of the approach
when assuming that the expert does not make mistakes.
In addition, the quality of the alignment provided by ALINAttr was compared
to state of the art tools that have participated in the track of interactive ontology
matching in OAEI 2017. The results obtained show that ALINAttr 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, but
with a number of interactions far superior to other tools. When performed with
techniques to decrease the number of interactions, the number of interactions
was compatible with that of the other tools, preserving a good quality.
As future work, one interesting direction is to explore how to reduce the
negative effects of expert mistakes. The ALINAttr generates good results when
the expert does not make mistakes, but because the approach uses the expert
feedback as the input of the structural attribute selection technique, probably
incorrect attribute mappings will be generated when the expert makes a mistake.
Acknowledgement
The second author was partially funding by project PQ-UNIRIO No 01/2017
(”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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