This paper presents the results of the Wiktionary Matcher in the Ontology Alignment Evaluation Initiative (OAEI) 2020. Wiktionary Matcher is an ontology matching tool that exploits Wiktionary as external background knowledge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wiktionary are merged and used for monolingual ontology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. This is the second OAEI participation of the matching system. Wiktionary Matcher has been improved and is the best performing system on the knowledge graph track this year.3 The Wiktionary Matcher is an element-level, label-based matcher which uses an online lexical resource, namely Wiktionary. The latter is "[a] collaborative project run by the Wikimedia Foundation to produce a free and complete dictionary in every language"4. The dictionary is organized similarly to Wikipedia: Everybody can contribute to the project and the content is reviewed in a community process. Compared to WordNet [2], Wiktionary is signi cantly larger and also available in other languages than English. This matcher uses DBnary [13], an RDF version of Wiktionary that is publicly available5. The DBnary dataset makes use of an extended LEMON model [7] to describe the data. For this 3 Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 4 see https://web.archive.org/web/20190806080601/https://en.wiktionary. org/wiki/Wiktionary 5 see http://kaiko.getalp.org/about-dbnary/download/
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matcher, recent DBnary datasets for 8 Wiktionary languages6 have been downloaded and merged into one RDF graph. Triples not required for the matching algorithm, such as glosses, were removed in order to increase the performance of the matcher and to lower its memory requirements. As Wiktionary contains translations, this matcher can work on monolingual and multilingual matching tasks.
This is the second OAEI participation of this matching system, Wiktionary
Matcher initially participated in the OAEI in 2019 [
This matching system system was initially introduced at the OAEI 2019 [
Monolingual Matching For monolingual ontologies, the matching system rst
applies multiple string matching techniques. Afterwards, the synonym matcher
module links labels to concepts in Wiktionary and checks then whether the
concepts are synonymous in the external dataset. This approach is conceptually
similar to an upper ontology matching approach. Concerning the usage of a
collaboratively built knowledge source, the approach is similar to WikiMatch [
Multilingual Matching For every matching task, the system rst determines the language distributions in the ontologies. If the ontologies appear to be in di erent languages, the system automatically enables the multilingual matching module: Here, Wiktionary translations are exploited: A match is created, if one label can be translated to the other one according to at least one Wiktionary language version { such as the Spanish label ciudad and the French label ville (both meaning city). This process is depicted in Figure 2: The Spanish label is linked to the entry in the Spanish Wiktionary and from the entry the translation is derived. If there is no Wiktionary version for the languages to be matched or the approach described above yields very few results, it is checked whether the two labels appear as a translation for the same word. The Chinese label 决定 (jued ng), for instance, is matched to the Arabic label P@Q¯ (qrar) because both appear as a translation of the English word decision on Wiktionary. This (less precise) approach is particularly important for language pairs for which no Wiktionary dataset is available to the matcher (such as Chinese and Arabic). The process is depicted in Figure 3: The Arabic and Chinese labels cannot be linked to Wiktionary entries but, instead, appear as translation for the same concept.
Instance Matching The matcher presented in this paper can be also used for combined schema and instance matching tasks. If instances are available in the given datasets, the matcher applies a two step strategy: After aligning the schemas, instances are matched using a string index. As there are typically many instances, Wiktionary is not used for the instance matching task in order to increase the matching runtime performance. Moreover, the coverage of schema level concepts in Wiktionary is much higher than for instance level concepts: For example, there is a sophisticated representation of the concept movie8, but hardly any individual movies in Wiktionary. For correspondences where the instances belong to classes that were matched before, a higher con dence is assigned. If one instance matches multiple other instances, the correspondence is preferred where both their classes were matched before.
Explainability Unlike many other ontology matchers, this matcher uses the
extension capabilities of the alignment format [
readable explanation of why a correspondence was added to the nal alignment. Such explanations can help to interpret and to trust a matching system's decision. Similarly, explanations also allow to comprehend why a correspondence was falsely added to the nal alignment: The explanation for the false positive match (http://confOf#Contribution, http://iasted#Tax), for instance, is given as follows: "The rst concept was mapped to dictionary entry [contribution] and the second concept was mapped to dictionary entry [tax]. According to Wiktionary, those two concepts are synonymous." Here, it can be seen that the matcher was successful in linking the labels to but failed due to the missing word sense disambiguation. In order to explain a correspondence, the description property9 of the Dublin Core Metadata Initiative is used. 1.3
For the 2020 campaign, the matching system has been improved. The instance
matching module has been extended to better exploit the string indices. As
a consequence, the matcher is the best performing system in the knowledge
graph track [
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On the anatomy track, recall and F1 could be improved compared to the 2019 version of the matcher. Due to further improvements of the implementation, the matching system's runtime performance could be signi cantly increased and the system is able to align the two ontologies in less than 100 seconds.11 The system performs above the median of all 2020 systems with an F1 score of 0.842 (precision = 0.956, recall = 0.753). 2.2
The matching system achieves almost the same results as in 2019 on the conference track with a slightly improved precision. With an F1 score of 0.65 on rar2-M1, the system performs slightly above the median in terms of F1. 2.3
Wiktionary Matcher is one of the few systems capable of matching multilingual ontologies. This year, Wikitionary Matcher is the system with the highest precision on the aggregated results (precision = 0.8 on di erent ontologies). In terms of f-measure, the system scores at the exact median. Compared to the 2019 campaign, the results improved slightly. This e ect is caused by the updated DBnary dataset used this year { the system improved itself due to a growing knowledge source (the multilingual matching implementation has not been changed compared to 2019). 2.4
Although the system has not been optimized for the LargeBio track, the matcher could complete all matching tasks within the given time. The system performs surprisingly competitive despite not using any other background knowledge source than Wiktionary. With the exception of task \FMA/NCI Whole", the matching system performed signi cantly better than the 2019 version in terms of F1. A small contributor to better results is also the new Wiktionary version which carries more synonyms in 2020 than in 2019. 2.5
Due to an improved instance matching module, the overall instance matching performance in terms of F1 could be increased from 0.79 to 0.87. With an overall 11 In the 2020 campaign, only 4 out of 11 systems were able to align the ontologies in less than 100 seconds. f-measure of 0.87, Wiktionary Matcher is the best matching system on this track.12 3
It is important to note that the matching system currently exploits only a small share of semantic relations available on Wiktionary. The system is restricted by the available relations extracted by the DBnary project. The additional exploitation of the relations alternative forms or derived terms, for instance, would likely improve the system. However, those are not yet extracted and are consequently not used for the matching task as of today.13 4
In this paper, we presented the Wiktionary Matcher, a matcher utilizing a collaboratively built lexical resource, as well as the results of the system in the 2020 OAEI campaign. Overall, the results of the matching system could be signi cantly improved compared to its last OAEI participation. Given Wiktionary's continuous growth, it can be expected that the matching results will improve over time { for example when additional synonyms and translations are added. Small improvements due to new synonyms and translations could already be observed within a one year time frame for example on the Multifarm or the LargeBio track. In addition, improvements to the DBnary dataset, such as the addition of alternative word forms, may also improve the overall matcher performance in the future.