its results. Alin is a system for interactive ontology matching. Since 2020A,lin 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 oAf lin at OAEI 2022 and discusses 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 diferent ontologies, and this may be performed manually, semi-automatically or automaticall1y].[ 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 improvements2][, as evidenced by the most recent results from the evaluation of interactive tools in the OA1EI (Ontology Alignment Evaluation Initiative)A.lin [3] is a system for interactive ontology The 17th International Workshop on Ontology Matching, The 21th International Semantic Web Conference ISWC-2022, ∗Corresponding author.
CEUR Workshop Proceedings 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 ofAlin 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.
Interactive ontology matching systems select mappings for domain expert evaluationA.lin selects many of these mappings through semantic and lexical metrics. Concept name standardization 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 diferent patterns used in the two ontologies into unique one. This standardization allowAs lin to select better mappings for the domain expert to evaluate.
To perform the standardization step, since 2020Alin 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 ontologiesAlin 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.
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) ifltered 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 ofAlin (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 ontologiesA.lin 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 diferent 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 stepA,lin 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 byAlin 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 [
• 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 cannot be equivalent to two entities of the ontolog y ′. Anti-pattern mapping is a combination of mappings 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.
Alin is available2 as a SEALS package ( It can be run with MELT ).
Interactive ontology matching is the focus of theAlin system.
The comparison between the participation ofAlin in 2021 and 2022 (Tables5 and 6) shows an improvement in the quality of the generated alignment, attesting the efectiveness of the changes in the grammars.
The modified grammars generated an increase in the F-Measure, recall and precision of noninteractively generated alignments in the Anatomy track (Tabl1e).
In the Anatomy interactive track,Alin 2022 was better than LogMap in quality (F-Measure) but worse in total requests (Table3). In the Conference track,Alin 2022 was, again, better than LogMap in quality (F-Measure) but worse in total requests (Tabl4e). 2 .//6364285404325/?%26 = 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 (Table5s and 6).
The quality of the alignment generated byAlin depends on the correct feedback from the domain expert, as Alin uses this feedback to select new mappings. WhenAlin selects wrong mappings, the quality of the generated alignment tends to decrease. The comparison between the two runs of Table7 shows a quality decline sharper in the current version.
LogMap Year
The analysis of OAEI 2022 results showed thatAlin 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).
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 thatAlin 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 thatAlin uses to standardize ontology terms.
This grammar modification proved to be eficient 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. the 14th International Workshop on Ontology Matching, OM’19, 2019, pp. 94–100.