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 mapping suggestions, using semantic and structural techniques to make this improvement. ALIN has obtained the alignment with the highest quality in the interactive tracking for Conference data set. This paper describes its con guration for the OAEI 2018 competition and discusses its results.
1.1
ALIN has the following steps to perform the interactive ontology matching process: First, ALIN generates an initial set of mappings. This set is called the set of mapping suggestions, that are the mappings to receive expert feedback. After, the interactive phase begins, where, at each interaction, the expert gives his feedback for some mapping suggestions. After each expert feedback, ALIN modi es the set of mapping suggestions according to the expert feedback. The modi cation of the set of mapping suggestions is by the use of the structural analysis of ontologies and the use of alignment anti-patterns. The interactions continue until there are no more mapping suggestions left.
Algorithm 1 ALIN algorithm Input: Two ontologies to be aligned, similarity metrics Output: Alignment between the two ontologies 1: Loading of ontologies 2: Generation of the initial set of mapping suggestions 3: Move of mappings by automatic classi cation from the set of mappings suggestions to the alignment 4: Move of mappings by the low value of semantic similarity from the set of mapping suggestions to a backup set 5: while Set of mapping suggestions is not empty do 6: Choose mapping from the the set of mapping suggestions to submit to the expert 7: Receive expert feedback to chosen mapping and remove it from the set of mapping suggestions 8: if Mapping is accepted then 9: Remove mappings in an alignment anti-pattern with accepted mapping from the set of mapping suggestions 10: Insert some data property and object property mappings related to the accepted mapping into set of mapping suggestions 11: Move some mappings related to the accepted mapping from the backup set to the set of mapping suggestions 12: end if 13: end while 1.2
The steps of ALIN algorithm (Algorithm 1) are the following:
{ Line 1. ALIN loads the ontology classes, object properties, and data
properties through the Alignment API [
ALIN sorts the mappings in the set of mapping suggestions by the sum of similarity metric values, greater sum rst. ALIN submits the mappings to the expert. The set of mapping suggestions has, at rst, only class mappings. After each expert feedback, if the expert accepts the mapping, ALIN moves it from the set of mapping suggestions to the alignment, else ALIN removes it from the set of mapping suggestions. ALIN can remove mappings (besides the mappings that received feedback) from the set of mapping suggestions and can include other mappings into it, depending on the expert feedback.
At each interaction with the expert:
{ ALIN removes from the set of mapping suggestions all the mappings that
are in alignment anti-pattern [
The interaction phase continues until the set of mapping suggestions is empty. 1.3
ALIN is available through Google drive (https://drive.google.com/ le/d/ 1v6cxQvAuWVqIBzWQUEIzDuZogoW35fdq/view?usp=sharing) as a package for running through the SEALS client.
Results Interactive ontology matching is the focus of the ALIN system. The quality of the alignment generated by ALIN is dependent on the correct expert feedback. ALIN has two phases: the non-interactive and the interactive phases. The noninteractive phase goal is to achieve high precision without worrying about the recall. In the interactive phase, ALIN modi es the set of mapping suggestions, including and removing mappings related to the accepted mapping. If the expert makes a mistake, ALIN is more prone to deteriorate the set of mapping suggestions, thereby decreasing the F-measure.
The system performs better when the number of data and object properties documented in the ontologies is proportionately large. In the interactive phase, the system includes into the set of mapping suggestions mappings related with accepted class mappings, thus allowing increase the recall. When the number of properties in the ontologies is small, the system still generates an alignment with good precision, but its recall tends to be not so good. 2.1
As expected the participation of ALIN in non-interactive matching tracks showed the following results: high precision and not so high recall when compared to the other tools, as can be seen in Anatomy track1 (Table 1). The conference track results can be seen on the OAEI 20182 page. 2.2
Interactive Anatomy Track In this track, the program ALIN showed the highest precision among the four evaluated tools when the error rate is zero (Table 2). When the error rate increases, both the precision as the recall falls, so falling the F-measure (Table 3). Dependence on expert feedback to ensure precision and to increase recall explains this decline in quality when the expert makes mistakes.
As ontologies of the Anatomy Track contains almost no properties, ALIN cannot utilize some interactive techniques like the selection of property mappings related to accepted class mappings. Not using these techniques has limited the increase in recall, which in uenced the F-measure. 1 Results for OAEI 2018 - Anatomy track. Available at http://oaei.ontologymatching.org/2018/results/anatomy/ Last accessed on Oct, 02, 2018. 2 Results of Evaluation for the Conference track within OAEI 2018 . Available at http://oaei.ontologymatching.org/2018/results/conference/index.html Last accessed on Oct, 24, 2018. Interactive Conference Track In this track, ALIN stood out, showing the greatest F-measure among the four tools when the error rate is zero (Table 4), as with a loss of F-measure when the error rate increases (Table 5).
Other results, including results with di erent error rates, can be seen on the OAEI 20183 page. 2.3
{ One modi cation made in ALIN was the withdrawal of additional criteria for the automatic classi cation of mappings. At the beginning of its execution, ALIN automatically selects mappings with the entities with the same name to put into the alignment. In the OAEI 2017, ALIN used additional criteria for that, that is, if a mapping had the two entities with the same name, but had met one of those criteria, ALIN didn't put it into the alignment. In the conference data set, the use of these criteria increased the precision of the alignment, and thus its quality, but also the number of interactions. In the Anatomy data set, the use of these criteria increased only the number of interactions. For OAEI 2018, ALIN focused on reducing its number of interactions. So, ALIN doesn't use the additional criteria for the automatic classi cation of mappings anymore. This modi cation reduced the number of interactions (Total Requests) in both the anatomy track (Table 6) and the conference track (Table 7), without decreasing the quality (F-measure) on the anatomy track. { Another modi cation was the selection of new mappings to the set of mapping suggestions. For OAEI, one interactive matching system can place up to three related mappings in an interaction. To take advantage of this rule, 3 Results for OAEI 2018 - Interactive Track http://oaei.ontologymatching.org/2018/results/interactive/ Oct, 2, 2018.
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Last
Available accessed at on in 2018, ALIN selects new mappings, with at least one entity equal to other already selected, to put into the set of mapping suggestions. This selection increases the likelihood of raising the recall. This modi cation increased the recall on the anatomy track (Table 6) but not increased enough on the conference track (Table 7) to compensate for the rst modi cation. { ALIN has stopped using the WS4J API4. ALIN had already stopped using WS4J to calculate similarity in OAEI 2017, starting to use HESML. ALIN was only using WS4J to nd the most common synset to an entity name, but now ALIN is directly accessing the Wordnet les. { handling user error rate; { generating a higher quality (especially w.r.t. recall) initial alignment in its non-interactive phase; { reducing the number of interactions with the expert. 4 'WS4J'. Available at https://github.com/Sciss/ws4j Last accessed on Jan, 16, 2018.
The ALIN system stands out in the interactive ontology matching process when ontologies have some characteristics, such as many documented properties, and when the expert does not make mistakes.
The second author was partially funding by project PQ-UNIRIO N01/2017 (" Aprendendo, adaptando e alinhando ontologias:metodologias e algoritmos.") and CAPES/PROAP.