=Paper=
{{Paper
|id=Vol-3324/oaei22_paper1
|storemode=property
|title=ALIN results for OAEI 2022
|pdfUrl=https://ceur-ws.org/Vol-3324/oaei22_paper1.pdf
|volume=Vol-3324
|authors=Jomar da Silva,Kate Revoredo,Fernanda Baião,Cabral Lima
|dblpUrl=https://dblp.org/rec/conf/semweb/SilvaRBL22
}}
==ALIN results for OAEI 2022==
https://ceur-ws.org/Vol-3324/oaei22_paper1.pdf
ALIN Results for OAEI 2022
Jomar da Silva1,∗ , Kate Revoredo2 , Fernanda Araujo Baião3 and Cabral Lima1
1
Graduate Program in Informatics
Federal University of Rio de Janeiro (UFRJ), Brazil
2
Humboldt University of Berlin, Berlin, Germany
3
Department of Industrial Engineering
Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
Abstract
Alin is a system for interactive ontology matching. Since 2020, Alin applies natural language processing
(NLP) techniques to standardize the concept names of the ontologies that participate in the matching
process. In the current version, we modified the grammars used during the process in order to improve
the quality of the alignments. This paper describes the participation of Alin at OAEI 2022 and discusses
its results.
Keywords
ontology matching, Wordnet, interactive ontology matching, ontology alignment, interactive ontology
alignment, natural language processing
1. Presentation of the system
Due to the advances in Information and Communication Technologies (ICT) in general, a large
amount of data repositories became available as valuable assets for enabling integrated data
exchange platforms across organizations. However, those repositories 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. The Ontology Matching process
seeks to discover correspondences (mappings) between entities of different ontologies, and
this may be performed manually, semi-automatically or automatically [1]. The interactive
approach, which considers the knowledge of domain experts through their participation during
the matching process, has stood out among semi-automatic ones [2]. A domain expert is
an expensive, scarce, and time-consuming resource; when available, however, this resource
has improved the achieved results. Nevertheless, there is still room for improvements [2], as
evidenced by the most recent results from the evaluation of interactive tools in the OAEI1
(Ontology Alignment Evaluation Initiative). Alin [3] is a system for interactive ontology
The 17th International Workshop on Ontology Matching, The 21th International Semantic Web Conference ISWC-2022,
October 23rd, 2022, Hangzhou, China
∗
Corresponding author.
Envelope-Open jomar.silva@ufrj.br (J. d. Silva); kate.revoredo@hu-berlin.de (K. Revoredo); fbaiao@puc-rio.br (F. A. Baião);
cabrallima@ufrj.br (C. Lima)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
CEUR Workshop Proceedings (CEUR-WS.org)
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
Available at http://oaei.ontologymatching.org/2022/results/interactive/index.htm, last accessed on Oct, 4, 2022.
matching which has been participating in all OAEI editions since 2016, with increasingly
improved results in the Anatomy interactive track.
The main contribution of this new version of Alin was the introduction of modifications to
the used regular grammars. These modifications aimed to standardize more terms used in the
ontologies. This standardization improves the result of the lexical and semantic metrics used in
Alin, which leads to a better mapping selection to be chosen automatically or presented to the
domain expert.
1.1. State, Purpose and General statement
Interactive ontology matching systems select mappings for domain expert evaluation. Alin
selects many of these mappings through semantic and lexical metrics. Concept name standard-
ization could lead to a more correct value in the metrics used. No standardized names may
cause Alin not to select correct mappings for evaluation by the domain expert.
Since its 2020 version, Alin uses Natural Language Processing (NLP) resources such as regular
grammars (in fact, their equivalent regular expressions) and context-free grammars along with
their respective lexical analyzers (scanners) and syntax analyzers (parsers).
These NLP resources make it possible to translate different patterns used in the two ontologies
into unique one. This standardization allows Alin to select better mappings for the domain
expert to evaluate.
To perform the standardization step, since 2020 Alin has a new phase before the program
runs. In this phase, an NLP expert provides grammars, and their respective scanners and parsers,
to the ontologies. Alin uses these scanners and parsers during the execution of the program.
This standarization step is possible in an interactive ontology matching system because:
1. We know before the program runs which ontologies Alin will match, as we need to look
for experts in the domain of ontologies to interact with the program;
2. The process of searching, meeting, and scheduling a day available for the expert to
participate in the process can take a long time, probably a few days.
We can use this time of a few days until the execution of the program to develop the necessary
grammars, scanners, and parsers for the ontologies. To test the new techniques, the authors of
this paper played the role of the NLP experts.
1.2. Specific techniques used
During its matching process, 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 selected mappings.
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.
Detailed in the ’Description of the used techniques’ paragraph below;
2. Filter mappings: suspend some selected mappings, using lexical and semantic criteria for
that;
3. Ask domain expert: accepts or rejects selected mappings, according to domain expert
feedback;
4. Propagate: select new mappings, reject some selected mappings or unsuspend some
suspended mappings (depending on newly accepted mappings);
5. Go to step 3 as long as there are undecided selected mappings.
All versions of Alin (since its first OAEI participation) follow this general procedure. Since
its 2020 version, Alin included a new step where an NLP expert develops grammars and
their respective scanners and parsers to the concept names of the ontologies. Alin uses these
scanners and parsers to standardize the concept names of the ontologies and thus improve the
generated alignment. The new step can lead to, for example, correcting spelling errors and
unifying different spellings for the same concept name. More detailed examples of possible
standardization of concept names are presented in [4]. Alin uses the developed scanners and
parsers in step 1 of the procedure.
1.2.1. Description of the used techniques
• Step 1. Alin runs the scanners and the parsers for each concept name of the ontologies,
modifying it and standardizing it. Alin uses a blocking strategy where it discards all
data properties and object properties of the ontologies. So, in this step, Alin selects
only concept mappings, using linguistic similarities between the standardized concept
names. Alin automatically accepts concept mappings whose standardized names are
synonyms. Alin uses the Wordnet and domain-specific ontologies (the FMA Ontology in
the Anatomy track) to find synonyms between entities.
• Step 2. Alin suspends the selected mappings whose entities have low lexical and semantic
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 [5]. These
suspended mappings can be further unsuspended later, returning to the status of selected
mappings, as proposed in [6].
• Step 3. At this point, the domain 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 domain expert. Alin can present up to
three mappings together to the domain expert if a full entity name in a candidate mapping
is the same as another entity name in another candidate mapping.
• Step 4. Initially, the set of selected mappings contains only concept mappings. At each
interaction with the domain expert, if he accepts the mapping, Alin (i) removes from the
set of selected mappings all the mappings that compose an instantiation of a mapping
anti-pattern [7][8] (we explain mapping anti-patterns below in the ’Mapping anti-patterns’
paragraph) with the accepted mappings; (ii) selects data property (as proposed in [9])
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 (as proposed in [6]).
• Step 5. The interaction phase continues until there are no selected mappings.
1.2.2. Mapping anti-patterns
An anti-pattern mapping can be a logical inconsistency, a construction constraint on the
ontology, or an alignment constraint. An ontology may have construction constraints,
such as a concept cannot be equivalent to its superconcept. The alignment between
two ontologies can have constraints. For example, an entity of ontology 𝑂 cannot be
equivalent to two entities of the ontology 𝑂 ′ . Anti-pattern mapping is a combination of map-
pings that generates a problematic alignment, i.e., a logical inconsistency or a violated constraint.
In its 2022 version, we have modified the formal grammars used by Alin to improve the
quality of the generated alignments.
1.3. Link to the system and parameters file
Alin is available 2 as a SEALS package ( It can be run with MELT ).
2. Results
Interactive ontology matching is the focus of the Alin system.
The comparison between the participation of Alin in 2021 and 2022 (Tables 5 and 6) shows
an improvement in the quality of the generated alignment, attesting the effectiveness of the
changes in the grammars.
2.1. Comments on the participation of Alin in non-interactive tracks
The modified grammars generated an increase in the F-Measure, recall and precision of non-
interactively generated alignments in the Anatomy track (Table 1).
Table 1
Participation of Alin in Anatomy Non-Interactive Track in OAEI 2021[10] and OAEI 2022[11]
Year Precision Recall F-measure
2021 0.983 0.726 0.835
2022 0.984 0.752 0.852
In the Conference track, the modified grammars have increased the F-Measure and recall in
relation to the 2020 results (ALIN’s last participation on this track) (Table 2).
2.2. Comments on the participation of Alin in interactive tracks
In the Anatomy interactive track, Alin 2022 was better than LogMap in quality (F-Measure)
but worse in total requests (Table 3). In the Conference track, Alin 2022 was, again, better than
LogMap in quality (F-Measure) but worse in total requests (Table 4).
2
𝑜𝑠𝑓 .𝑖𝑜/𝑑𝑜𝑤𝑛𝑙𝑜𝑎𝑑/636𝑎𝑑4𝑐𝑎2𝑒𝑎85404𝑏3𝑎2𝑑5𝑏𝑒/?𝑑𝑖𝑟𝑒𝑐𝑡%26𝑚𝑜𝑑𝑒 = 𝑟𝑒𝑛𝑑𝑒𝑟
Table 2
Participation of Alin in Conference Non-Interactive Track ( rar2-M3, F1-Measure ) in OAEI 2020[12]
and OAEI 2022[13]
Year Precision Recall F-measure
2020 0.82 0.43 0.56
2022 0.82 0.44 0.57
Table 3
Participation of Alin in Anatomy Interactive Track - OAEI 2022[14] - Error Rate 0.0
Tool Precision Recall F-measure Total Requests
ALIN 0.987 0.92 0.952 579
LogMap 0.988 0.846 0.912 388
Table 4
Participation of Alin in Conference Interactive Track - OAEI 2022[14] - Error Rate 0.0
Tool Precision Recall F-measure Total Requests
ALIN 0.919 0.744 0.822 309
LogMap 0.886 0.61 0.723 82
2.3. Alin participations in OAEI 2022 and OAEI 2021
Alin improved the quality of the alignment generated for both the Anatomy and the Conference
tracks. The number of interactions has also increased on both tracks (Tables 5 and 6).
The quality of the alignment generated by Alin depends on the correct feedback from the
domain expert, as Alin uses this feedback to select new mappings. When Alin selects wrong
mappings, the quality of the generated alignment tends to decrease. The comparison between
the two runs of Table 7 shows a quality decline sharper in the current version.
Table 5
Participation of Alin in Anatomy Interactive Track - OAEI
2016[15]/2017[16]/2018[17]/2019[18]/2020[12]/2021[10]/2022[14] - 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
2020 0.988 0.856 0.917 360
2021 0.986 0.887 0.934 404
2022 0.987 0.92 0.952 579
Table 6
Participation of Alin in Conference Interactive Track - OAEI
2016[15]/2017[16]/2018[17]/2019[18]/2020[12]/2021[10]/2022[14] - 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
2020 0.915 0.705 0.796 233
2021 0.916 0.718 0.799 281
2022 0.919 0.744 0.822 309
Table 7
F-Measure of Alin in Anatomy Interactive Track - OAEI 2021[10]/OAEI 2022[14] and in Conference
Interactive Track - OAEI /2021[10]/2022[14] - with Different Error Rates
Year Error rate 0.0 Error rate 0.1
Anatomy 2021 0.934 0.899
2022 0.952 0.905
Year Error rate 0.0 Error rate 0.1
Conference 2021 0.799 0.698
2022 0.822 0.705
3. General comments
The analysis of OAEI 2022 results showed that Alin has improved the quality of the generated
alignment in the interactive track. But an increase in the expert error rate caused a sharper fall
in the alignment quality. The number of interactions with the expert was increased (from 281
to 309 requests in the Conference track and from 404 to 579 requests in the Anatomy track).
3.1. Conclusions
In this new version, Alin (Alin 2022) addresses the problem of improving the standardization
of terms to achieve better results with lexical and semantic metrics. That aims to improve the
selection of mappings, both those that Alin will automatically place in the alignment and those
that Alin will present to the domain expert. To address this problem, we put new patterns in
the regular grammars that Alin uses to standardize ontology terms.
This grammar modification proved to be efficient in increasing the F-measure but increasing
the number of interactions with the domain expert.
Since OAEI 2020, Alin has been improving grammars to improve the standardization of
terms used in ontologies, and always with an improvement in F-measure, which shows how
greater standardization of ontologies terms gives positive results when used.
In future versions, we intend to implement new grammar changes to further improve the
quality of the generated alignment.
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