=Paper=
{{Paper
|id=Vol-2536/oaei19_paper2
|storemode=property
|title=ALIN Results for OAEI 2019
|pdfUrl=https://ceur-ws.org/Vol-2536/oaei19_paper2.pdf
|volume=Vol-2536
|authors=Jomar da Silva,Carla Delgado,Kate Revoredo,Fernanda Baião
|dblpUrl=https://dblp.org/rec/conf/semweb/SilvaDRB19
}}
==ALIN Results for OAEI 2019==
https://ceur-ws.org/Vol-2536/oaei19_paper2.pdf
ALIN Results for OAEI 2019
Jomar da Silva1 , Carla Delgado1 , Kate Revoredo1 , and Fernanda Araujo
Baião2
1
Graduate Program in Informatics, Federal University of Rio de Janeiro (UFRJ),
Brazil
2
Department of Industrial Engineering, Pontifical Catholic University of Rio de
Janeiro (PUC-Rio), Brazil
jomar.silva@uniriotec.br,carla@ppgi.ufrj.br,katerevoredo@ppgi.ufrj.br,
fbaiao@puc-rio.br
Abstract. 1 ALIN is an ontology matching system specialized in the
interactive ontology matching, and its main characteristic is the use of
expert feedback to improve the set of selected mappings, using semantic
and structural techniques to make this improvement. This paper de-
scribes its configuration for the OAEI 2019 competition and discusses its
results.
Keywords: ontology matching, Wordnet, interactive ontology match-
ing, ontology alignment, interactive ontology alignment
1 Presentation of the System
Due to the advances in information and communication technologies, a large
amount of data repositories became available. Those repositories, however, are
highly semantically heterogeneous, which hinders their integration. Ontology
matching has been successfully applied to solve this problem, by discovering
mappings between two distinct ontologies which, in turn, conceptually define
the data stored in each repository. Among the various ontology matching ap-
proaches that exist in the literature, interactive ontology matching includes the
participation of domain experts to improve the quality of the final alignment [1].
ALIN is an interactive ontology matching system and has been participating in
all OAEI editions since 2016, with improving results.
1.1 State, Purpose and General Statement
ALIN is a system for interactive ontology matching that consists of two steps:
one non-interactive and one interactive. In the first step, ALIN chooses the first
mappings, among which some are directly placed in the alignment and others
are presented to the expert. In the 2019 version, ALIN uses new techniques to
improve the first step, thus placing more mappings directly in the alignment
without having to present them to the expert.
1
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
1.2 Specific Techniques Used
Alin handles three sets of mappings: (i) Accepted, which is a set of mappings
definitely to be retained in the alignment; (ii) Selected, which is a set of mappings
where each is yet to be decided if it will be included in the alignment; and (iii)
Suspended, which is a set of mappings that have been previously selected, but
(temporarily or permanently) filtered out of the alignment.
Given the previous definitions, Alin procedure follows 5 Steps, described as
follows:
1. Select mappings: select the first mappings and automatically accepts some
of them. We explain the selection and acceptance process below;
2. Filter mappings: suspend some selected mappings, using lexical criteria for
that;
3. Ask expert: accepts or rejects selected mappings, according to expert feed-
back
4. Propagate: select new mappings, reject some selected mappings or unsuspend
some suspended mappings (depending on newly accepted mappings)
5. Go back to 3 as long as there are undecided selected mappings
All versions of ALIN (since its very first OAEI participation) follow this
general procedure. In this 2019 version, however, we introduced modifications in
Step 1. In previous versions, ALIN automatically accepted only the entities with
the same name. In this version, ALIN also automatically accepts the entities
whose names are synonyms or with variations in name words. ALIN searches
synonyms in the Wordnet. In the Anatomy track, ALIN uses the FMA ontology
too.
ALIN applies the following techniques:
– Line 1. ALIN selects mappings using linguistic similarities between entity
names. ALIN uses synonyms and variations in entity name words to auto-
matically accept mappings. At this time, ALIN automatically selects and
accepts only concept mappings. To do that, ALIN uses linguistic metrics.
ALIN uses the Wordnet and domain-specific ontologies (the FMA Ontology
in the Anatomy track) to find synonyms between entities.
– Line 2. ALIN suspends the selected mappings whose entities have low lexical
similarity. We use the Jaccard, Jaro-Wrinkler, and n-gram lexical metrics to
calculate the lexical similarity of the selected mappings. We based the process
of choosing the similarity metrics used by ALIN on the result of these metrics
in assessments [2]. It is important to know that these suspended mappings
can be unsuspended later, by structural analysis, as proposed in [3].
– Line 3. At this point, the expert interaction begins. ALIN sorts the selected
mappings in a descending order according to the sum of similarity metric
values. The sorted selected mappings are submitted to the expert.
– Line 4. Initially, the set of selected mappings contains only concept map-
pings. At each interaction with the expert, if the expert accepts the mapping,
ALIN (i) removes from the set of selected mappings all the mappings that
compose the mapping anti-pattern [4][5] (we explain mapping anti-pattern
below) with the accepted mappings; (ii) selects data property (like [6]) and
object property mappings related to the accepted concept mappings; (iii)
unsuspends all concept mappings whose both entities are subconcepts of the
concept of an accepted mapping, following a similar technique proposed in
our previous work [3].
– Line 5. The interaction phase continues until there are no selected mappings.
An ontology may have construction constraints, such as a concept cannot be
equivalent to its superconcept. An alignment may have other constraints like,
for example, an entity of ontology O cannot be equivalent to two entities of the
ontology O0 . A mapping anti-pattern is a combination of mappings that generates
a problematic alignment, i.e., a logical inconsistency or a violated constraint.
1.3 Link to the System and Parameters File
ALIN is available 2 as a package to be run through the SEALS client.
2 Results
Interactive ontology matching is the focus of the ALIN system. Comparing its
results in the 2019 campaign to its previous participations (Table 5), ALIN
improvements include an expressive reduction on the number of interactions
with the expert and the increase of the quality of the generated alignment.
2.1 Comments on the Participation of the ALIN in Non-Interactive
Tracks
ALIN used new techniques to automatically accept mappings. These techniques
led to an increase in the F-Measure of non-interactively generated alignment,
which shows the effectiveness of the techniques. (Table 1 and Table 2). Confer-
ence track, unlike the Anatomy track, has relationship mappings and attribute
mappings that ALIN does not automatically accept, thus making the F-Measure
on the Conference track, although higher than last year, still low.
Table 1. Participation of ALIN in Anatomy Non-Interactive Track - OAEI
2018[7]/2019[8]
Year Precision Recall F-measure
2018 0.998 0.611 0.758
2019 0.974 0.698 0.813
2
https : //drive.google.com/f ile/d/1SxJL6f LRV qI84epm8DbAM lcscEoGbgZ/view?usp =
sharing
Table 2. Participation of ALIN in Conference Non-Interactive Track - OAEI
2018/2019[9]
Year Precision Recall F-measure
2018 0.81 0.42 0.55
2019 0.82 0.43 0.56
2.2 Comments on the Participation of the ALIN in Interactive
Tracks
In the Anatomy track, ALIN was tied for second in quality (F-Measure) with
slightly lower total requests (Table 3). In the Conference track, ALIN was tied
for first in quality with a slightly higher total request (Table 4).
Table 3. Participation of ALIN in Anatomy Interactive Track - Error Rate 0.0[10]
Tool Precision Recall F-measure Total Requests
ALIN 0.979 0.85 0.91 365
AML 0.968 0.948 0.958 236
LogMap 0.982 0.846 0.909 388
Table 4. Participation of ALIN in Conference Interactive Track - Error Rate 0.0[10]
Tool Precision Recall F-measure Total Requests
ALIN 0.914 0.695 0.79 228
AML 0.91 0.698 0.79 221
LogMap 0.886 0.61 0.723 82
Interactive Anatomy Track In this track, ALIN has had a decrease in the
number of expert interactions and an increase in the quality of the generated
alignment, showing that the new techniques used to automatically accept correct
mappings are effective (Table 5).
ALIN used the FMA ontology to help find synonyms between the two on-
tologies of the Anatomy track. The Foundational Model of Anatomy Ontology
(FMA) is a reference ontology for the domain of Human anatomy 3 .
3
“Foundational Model of Anatomy Ontology”. Available at
http://sig.biostr.washington.edu/projects/fm/AboutFM.html Last accessed on
Oct, 11, 2019.
Interactive Conference Track In this track, ALIN has had a decrease in the
number of expert interactions keeping a good quality of the generated alignment
(Table 7).
2.3 Comparison of the Participation of ALIN in OAEI 2019 with
his Participation in OAEI 2018
In this version, ALIN uses new techniques to automatically accept mappings.
These techniques use synonyms and word variations to find equal entities be-
tween the two ontologies. ALIN also started to use FMA ontology as an external
resource.
The use of the new techniques proved to be effective as it reduced the number
of interactions while keeping a good level of quality. The new techniques also
increased the quality of the alignment generated in Anatomy interactive tracking,
where ALIN used the FMA ontology.
It is not always possible to use an external resource to find synonyms between
entities of two ontologies, but when this is possible, the results showed that it is
worth it.
The quality of the alignment generated by ALIN is dependent on the correct
expert feedback, as expert responses are used to select new mappings. When
ALIN selects wrong mappings, the quality of the generated alignment tends to
decrease. But if we compare this year’s quality decline with last year’s, we see
that this fall is less sharp (Table 6 and Table 8). The less sharp decline in quality
is because we need less user interaction as we are automatically accepting more
mappings.
The organization of FMA ontology in memory and the search for synonyms
and word variations led to longer run time (Table 9 and Table 10)
Table 5. Participation of ALIN in Anatomy Interactive Track - OAEI
2016[11]/2017[12]/2018[7]/2019[10] - Error Rate 0.0
Year Precision Recall F-measure Total Requests
2016 0.993 0.749 0.854 803
2017 0.993 0.794 0.882 939
2018 0.994 0.826 0.902 602
2019 0.979 0.85 0.91 365
3 General Comments
Evaluating the results, we can see that the system has improved, although it can
improve even further, towards:
– handling user error rate;
Table 6. F-Measure of ALIN in Anatomy Interactive Track - OAEI /2018[7]/2019[10]
- with Different Error Rates
Year Error rate 0.0 Error rate 0.1
2018 0.902 0.854
2019 0.91 0.889
Table 7. Participation of ALIN in Conference Interactive Track - OAEI
2016[11]/2017[12]/2018[7]/2019[10] - Error Rate 0.0
Year Precision Recall F-measure Total Requests
2016 0.957 0.735 0.831 326
2017 0.957 0.731 0.829 329
2018 0.921 0.721 0.809 276
2019 0.914 0.695 0.79 228
Table 8. F-Measure of ALIN in Conference Interactive Track - OAEI /2018[7]/2019[10]
- with Different Error Rates
Year Error rate 0.0 Error rate 0.1
2018 0.809 0.705
2019 0.79 0.725
Table 9. Run Time (sec) in Anatomy Interactive Track - OAEI /2018[13]//2019[10]
Tool 2018 2019
ALIN 317 2132
AML 48 82
LogMap 23 29
Table 10. Run Time (sec) in Conference interactive track - OAEI /2018[13]/2019[10]
Tool 2018 2019
ALIN 106 397
AML 35 34
LogMap 37 37
– generating a higher quality initial alignment in its non-interactive phase;
– reducing the number of interactions with the expert;
And there was a worsening run time, where we could improve too.
3.1 Conclusions
ALIN used new techniques to automatically accept new mappings. They have
been effective in reducing the number of interactions, while also keeping good
quality in the generated alignment. In the case of the Anatomy track, these new
techniques both decreased the number of interactions and increased the quality
of the generated alignment. We can explain this quality improvement in this
track by the use of the FMA ontology as a new external resource. With the use
of the new techniques in both Anatomy and Conference tracks, there has been
a less sharp drop in quality as the expert makes mistakes. Nevertheless, ALIN
had an increase in run time due to the use of the new techniques, which may be
addressed in future work.
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