Abstract. The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.
The Ontology Alignment Evaluation Initiative1 (OAEI) is a coordinated international initiative, which organizes the evaluation of an increasing number of ontology matching systems [26, 28], and which has been run for seventeen years by now. The main goal of the OAEI is to compare systems and algorithms openly and on the same basis, in order to allow anyone to draw conclusions about the best matching strategies. Furthermore, the ambition is that, from such evaluations, developers can improve their systems and offer better tools that answer the evolving application needs.
Two first events were organized in 2004: (i) the Information Interpretation and Integration Conference (I3CON) held at the NIST Performance Metrics for Intelligent Systems (PerMIS) workshop and (ii) the Ontology Alignment Contest held at the Evaluation of Ontology-based Tools (EON) workshop of the annual International Semantic Web Conference (ISWC) [66]. Then, a unique OAEI campaign occurred in 2005 at the workshop on Integrating Ontologies held in conjunction with the International Conference on Knowledge Capture (K-Cap) [7]. From 2006 until the present, the OAEI campaigns were held at the Ontology Matching workshop, collocated with ISWC [5, 4, 1, 2, 11, 18, 15, 3, 24, 23, 22, 10, 25, 27], which this year took place virtually (originally planned in Athens, Greece)2.
Since 2011, we have been using an environment for automatically processing evaluations (Section 2.1) which was developed within the SEALS (Semantic Evaluation At Large Scale) project3. SEALS provided a software infrastructure for automatically executing evaluations and evaluation campaigns for typical semantic web tools, including ontology matching. Since OAEI 2017, a novel evaluation environment, called HOBBIT (Section 2.1), was adopted for the HOBBIT Link Discovery track, and later extended to enable the evaluation of other tracks. Some tracks are run exclusively through SEALS and others through HOBBIT, but several allow participants to choose the platform they prefer. This year, the MELT framework [36] was adopted in order to facilitate the SEALS and HOBBIT wrapping and evaluation.
This paper synthesizes the 2020 evaluation campaign and introduces the results provided in the papers of the participants. The remainder of the paper is organized as follows: in Section 2, we present the overall evaluation methodology; in Section 3 we present the tracks and datasets; in Section 4 we present and discuss the results; and finally, Section 5 discusses the lessons learned. ? Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 1 http://oaei.ontologymatching.org 2 http://om2020.ontologymatching.org 3 http://www.seals-project.eu
The OAEI evaluation was carried out in one of two alternative platforms: the SEALS client or the HOBBIT platform. Both have the goal of ensuring reproducibility and comparability of the results across matching systems.
The SEALS client was developed in 2011. It is a Java-based command line interface for ontology matching evaluation, which requires system developers to implement a simple interface and to wrap their tools in a predefined way including all required libraries and resources. A tutorial for tool wrapping is provided to the participants, describing how to wrap a tool and how to run a full evaluation locally.
The HOBBIT platform4 was introduced in 2017. It is a web interface for linked data and ontology matching evaluation, which requires systems to be wrapped inside docker containers and includes a SystemAdapter class, then being uploaded into the HOBBIT platform [44].
Both platforms compute the standard evaluation metrics against the reference alignments: precision, recall and F-measure. In test cases where different evaluation modalities are required, evaluation was carried out a posteriori, using the alignments produced by the matching systems.
The MELT framework5 [36] was introduced in 2019 and is under active development. It allows to develop, evaluate, and package matching systems for arbitrary evaluation interfaces like SEALS or HOBBIT. It further enables developers to use Python in their matching systems. In terms of evaluation, MELT offers a correspondence level analysis for multiple matching systems which can even implement different interfaces. It is, therefore, suitable for track organisers as well as system developers. 2.2
As in previous years, the OAEI 2020 campaign was divided into three phases: preparatory, execution, and evaluation.
In the preparatory phase, the test cases were provided to participants in an initial assessment period between June 15th and July 15th, 2020. The goal of this phase is to ensure that the test cases make sense to participants, and give them the opportunity to provide feedback to organizers on the test case as well as potentially report errors. At the end of this phase, the final test base was frozen and released.
During the ensuing execution phase, participants test and potentially develop their matching systems to automatically match the test cases. Participants can self-evaluate their results either by comparing their output with the reference alignments or by using either of the evaluation platforms. They can tune their systems with respect to the nonblind evaluation as long as they respect the rules of the OAEI. Participants were required to register their systems and make a preliminary evaluation by July 31st. The execution phase was terminated on October 15th, 2020, at which date participants had to submit the (near) final versions of their systems (SEALS-wrapped and/or HOBBIT-wrapped). 4 https://project-hobbit.eu/outcomes/hobbit-platform/ 5 https://github.com/dwslab/melt
During the evaluation phase, systems were evaluated by all track organizers. In case minor problems were found during the initial stages of this phase, they were reported to the developers, who were given the opportunity to fix and resubmit their systems. Initial results were provided directly to the participants, whereas final results for most tracks were published on the respective OAEI web pages by October 24th, 2020. 3
This year’s OAEI campaign consisted of 12 tracks gathering 36 test cases, all of which included OWL ontologies to align.6 They can be grouped into: – Schema matching tracks, which have as objective matching ontology classes and/or properties. – Instance Matching tracks, which have as objective matching ontology instances. – Instance and Schema Matching tracks, which involve both of the above. – Complex Matching tracks, which have as objective finding complex correspondences between ontology entities. – Interactive tracks, which simulate user interaction to enable the benchmarking of interactive matching algorithms.
3.1
The anatomy track comprises a single test case consisting of matching two fragments of biomedical ontologies which describe the human anatomy7 (3304 classes) and the anatomy of the mouse8 (2744 classes). The evaluation is based on a manually curated reference alignment. This dataset has been used since 2007 with some improvements over the years [20].
Systems are evaluated with the standard parameters of precision, recall, F-measure. Additionally, recall+ is computed by excluding trivial correspondences (i.e., correspondences that have the same normalized label). Alignments are also checked for coherence using the Pellet reasoner. The evaluation was carried out on a server with a 6 core CPU @ 3.46 GHz with 8GB allocated RAM, using the SEALS client. For some system requires more RAM, the evaluation was carried out on a Windows 10 (64-bit) desktop with an Intel Core i7-6700 CPU @ 3.40GHz x 8 with 16GB RAM allocated. However, the evaluation parameters were computed a posteriori, after removing from the alignments produced by the systems, correspondences expressing relations other than equivalence, as well as trivial correspondences in the oboInOwl namespace (e.g., oboInOwl#Synonym = oboInOwl#Synonym). The results obtained with the SEALS client vary in some cases by 0.5% compared to the results presented below.
7 www.cancer.gov/cancertopics/cancerlibrary/terminologyresources 8 http://www.informatics.jax.org/searches/AMA_form.shtml open+blind DFER,, EITN, ,NELS,, SEALS
AR, CZ, CN,
RU, PT Schema Matching = [0 1] =, <= =, <= = = = Open evaluation is made with already published reference alignments and blind evaluation is made by organizers, either from reference alignments unknown to the participants or manually. The biodiversity and ecology (biodiv) track has been originally motivated by two projects, namely GFBio9 (The German Federation for Biological Data) and AquaDiva10, which aim at providing semantically enriched data management solutions for data capture, annotation, indexing and search [46, 48]. This year, the third edition of the biodiv track features the two matching tasks present in former editions, namely: matching the Environment Ontology (ENVO) [9] to the Semantic Web for Earth and Environment Technology Ontology (SWEET) [58], and matching the Flora Phenotype Ontology (FLOPO) [38] to Plant Trait Ontology (PTO) [14]. In this edition, we partnered with the D2KAB project11 (Data to Knowledge in Agronomy and Biodiversity) which develops the AgroPortal12 vocabulary and ontology repository, to include 9 www.gfbio.org 10 www.aquadiva.uni-jena.de 11 www.d2kab.org 12 agroportal.lirmm.fr EN EN EN EN EN EN EN EN EN EN
SEALS two new matching tasks involving important thesauri (originally developed in SKOS) in agronomy and environmental sciences: finding alignments between the AGROVOC thesaurus [59] and the US National Agricultural Library Thesaurus (NALT)13 and between the General Multilingual Environmental Thesaurus (GEMET)14 and the Analysis and Experimentation on Ecosystems thesaurus (ANAEETHES)[13]. These ontologies and thesauri are particularly useful for biodiversity and ecology research and are being used in various projects. They have been developed in parallel and are significantly overlapping. They are semantically rich and contain tens of thousands of concepts. By providing semantic resources developed in SKOS, our objective is also to encourage the ontology alignment community to develop tools that can natively handle SKOS which is an important standard to encode terminologies (particularly thesauri and taxonomies) and for which alignment is also very important.
Table 2 presents detailed information about the ontologies and thesauri used in the evaluation, such as the ontology format, version, number of classes as well as the number of instances15.
For the ontologies ENVO, SWEET, FLOPO and PTO, we created the reference
alignments for the tasks following the same procedure as in former editions. Reference
files were produced using a hybrid approach consisting of (
For the thesauri AGROVOC, NALT, GEMET and ANEETHES, we created the reference alignments using the Ontology Mapping Harvesting Tool (OMHT).16 OMHT was developed as a standalone Java program that works with one semantic resource file pulled out from AgroPortal or BioPortal17. OMHT automatically extracts all declared mappings by developers inside an ontology or a thesauri source files. We used for the reference alignments only the mappings with a skos:exactMatch property.
The evaluation was carried out on a Windows 10 (64-bit) desktop with an Intel Core i7-4770 CPU @ 3.40GHz x 4 with 16 GB RAM allocated, using the SEALS client. Systems were evaluated using the standard metrics. 3.3
The conference track features a single test case that is a suite of 21 matching tasks corresponding to the pairwise combination of 7 moderately expressive ontologies describing the domain of organizing conferences. The dataset and its usage are described in [70].
The track uses several reference alignments for evaluation: the old (and not fully complete) manually curated open reference alignment, ra1; an extended, also manually curated version of this alignment, ra2; a version of the latter corrected to resolve violations of conservativity, rar2; and an uncertain version of ra1 produced through crowd-sourcing, where the score of each correspondence is the fraction of people in the evaluation group that agree with the correspondence. The latter reference was used in two evaluation modalities: discrete and continuous evaluation. In the former, correspondences in the uncertain reference alignment with a score of at least 0.5 are treated as correct whereas those with lower score are treated as incorrect, and standard evaluation parameters are used to evaluated systems. In the latter, weighted precision, recall and F-measure values are computed by taking into consideration the actual scores of the uncertain reference, as well as the scores generated by the matching system. For the sharp reference alignments (ra1, ra2 and rar2), the evaluation is based on the standard parameters, as well the F0:5-measure and F2-measure and on conservativity and consistency violations. Whereas F1 is the harmonic mean of precision and recall where both receive equal weight, F2 gives higher weight to recall than precision and F0:5 gives higher weight to precision higher than recall. The track also includes an analysis of False Positives.
Two baseline matchers are used to benchmark the systems: edna string edit distance matcher; and StringEquiv string equivalence matcher as in the anatomy test case.
The evaluation was carried out on a Windows 10 (64-bit) desktop with an Intel Core i7–8550U (1,8 GHz, TB 4 GHz) x 4 with 16 GB RAM allocated using the SEALS client. Systems were evaluated using the standard metrics. 3.4
The Disease and Phenotype is organized by the Pistoia Alliance Ontologies Mapping project team18. It comprises 2 test cases that involve 4 biomedical ontologies cov16 https://github.com/agroportal/ontology_mapping_harvester 17 https://bioportal.bioontology.org 18 http://www.pistoiaalliance.org/projects/ontologies-mapping/ ering the disease and phenotype domains: Human Phenotype Ontology (HP) versus Mammalian Phenotype Ontology (MP) and Human Disease Ontology (DOID) versus Orphanet and Rare Diseases Ontology (ORDO). Currently, correspondences between these ontologies are mostly curated by bioinformatics and disease experts who would benefit from automation of their workflows supported by implementation of ontology matching algorithms. More details about the Pistoia Alliance Ontologies Mapping project and the OAEI evaluation are available in [31]. Table 3 summarizes the versions of the ontologies used in OAEI 2020.
The reference alignments used in this track are silver standard consensus alignments automatically built by merging/voting the outputs of the participating systems in the OAEI campaigns 2016-2020 (with vote=3). Note that systems participating with different variants and in different years only contributed once in the voting, that is, the voting was done by family of systems/variants rather than by individual systems. The HP-MP silver standard thus contains 2,504 correspondences, whereas the DOID-ORDO one contains 3,909 correspondences.
Systems were evaluated using the standard parameters as well as the (approximate) number of unsatisfiable classes computed using the OWL 2 EL reasoner ELK [47]. The evaluation was carried out in a Ubuntu 18 Laptop with an Intel Core i5-6300HQ CPU @ 2.30GHz x 4 and allocating 15 Gb of RAM. 3.5
The large biomedical ontologies (largebio) track aims at finding alignments between the large and semantically rich biomedical ontologies FMA, SNOMED-CT, and NCI, which contain 78,989, 306,591 and 66,724 classes, respectively. The track consists of six test cases corresponding to three matching problems (FMA-NCI, FMA-SNOMED and SNOMED-NCI) in two modalities: small overlapping fragments and whole ontologies (FMA and NCI) or large fragments (SNOMED-CT).
The reference alignments used in this track are derived directly from the UMLS Metathesaurus [8] as detailed in [42], then automatically repaired to ensure logical coherence. However, rather than use a standard repair procedure of removing problem causing correspondences, we set the relation of such correspondences to “?” (unknown). These “?” correspondences are neither considered positive nor negative when evaluating matching systems, but are simply ignored. This way, systems that do not perform alignment repair are not penalized for finding correspondences that (despite causing incoherences) may or may not be correct, and systems that do perform alignment repair are not penalized for removing such correspondences. To avoid any bias, correspondences were considered problem causing if they were selected for removal by any of the three established repair algorithms: Alcomo [52], LogMap [41], or AML [60]. The reference alignments are summarized in Table 4.
The evaluation was carried out in a Ubuntu 18 Laptop with an Intel Core i5-6300HQ CPU @ 2.30GHz x 4 and allocating 15 Gb of RAM. Evaluation was based on the standard parameters (modified to account for the “?” relations) as well as the number of unsatisfiable classes and the ratio of unsatisfiable classes with respect to the size of the union of the input ontologies. Unsatisfiable classes were computed using the OWL 2 reasoner HermiT [54], or, in the cases in which HermiT could not cope with the input ontologies and the alignments (in less than 2 hours) a lower bound on the number of unsatisfiable classes (indicated by ) was computed using the OWL2 EL reasoner ELK [47]. 3.6
The multifarm track [53] aims at evaluating the ability of matching systems to deal with ontologies in different natural languages. This dataset results from the translation of 7 ontologies from the conference track (cmt, conference, confOf, iasted, sigkdd, ekaw and edas) into 10 languages: Arabic (ar), Chinese (cn), Czech (cz), Dutch (nl), French (fr), German (de), Italian (it), Portuguese (pt), Russian (ru), and Spanish (es). The dataset is composed of 55 pairs of languages, with 49 matching tasks for each of them, taking into account the alignment direction (e.g. cmten !edasde and cmtde !edasen are distinct matching tasks). While part of the dataset is openly available, all matching tasks involving the edas and ekaw ontologies (resulting in 55 24 matching tasks) are used for blind evaluation.
We consider two test cases: i) those tasks where two different ontologies (cmt!edas, for instance) have been translated into two different languages; and ii) those tasks where the same ontology (cmt!cmt) has been translated into two different languages. For the tasks of type ii), good results are not only related to the use of specific techniques for dealing with cross-lingual ontologies, but also on the ability to exploit the identical structure of the ontologies.
The reference alignments used in this track derive directly from the manually curated Conference ra1 reference alignments. The systems have been executed on a Ubuntu Linux machine configured with 8GB of RAM running under a Intel Core CPU 2.00GHz x4 processors, using the SEALS client. 3.7 The Link Discovery track features two test cases, Linking and Spatial, that deal with link discovery for spatial data represented as trajectories i.e., sequences of longitude, latitude pairs. The track is based on two datasets generated from TomTom19 and Spaten [17].
The Linking test case aims at testing the performance of instance matching tools that implement mostly string-based approaches for identifying matching entities. It can be used not only by instance matching tools, but also by SPARQL engines that deal with query answering over geospatial data. The test case was based on SPIMBENCH [62], but since the ontologies used to represent trajectories are fairly simple and do not consider complex RDF or OWL schema constructs already supported by SPIMBENCH, only a subset of the transformations implemented by SPIMBENCH was used. The transformations implemented in the test case were (i) string-based with different (a) levels, (b) types of spatial object representations and (c) types of date representations, and (ii) schema-based, i.e., addition and deletion of ontology (schema) properties. These transformations were implemented in the TomTom dataset. In a nutshell, instance matching systems are expected to determine whether two traces with their points annotated with place names designate the same trajectory. In order to evaluate the systems a ground truth was built that contains the set of expected links where an instance s1 in the source dataset is associated with an instance t1 in the target dataset that has been generated as a modified description of s1.
The Spatial test case aims at testing the performance of systems that deal with topological relations proposed in the state of the art DE-9IM (Dimensionally Extended nine-Intersection Model) model [65]. The benchmark generator behind this test case implements all topological relations of DE-9IM between trajectories in the two dimensional space. To the best of our knowledge such a generic benchmark, that takes as input trajectories and checks the performance of linking systems for spatial data does not exist. The focus for the design was (a) on the correct implementation of all the topological relations of the DE-9IM topological model and (b) on producing datasets large enough to stress the systems under test. The supported relations are: Equals, Disjoint, Touches, Contains/Within, Covers/CoveredBy, Intersects, Crosses, Overlaps. The test case comprises tasks for all the DE-9IM relations and for LineString/LineString and LineString/Polygon cases, for both TomTom and Spaten datasets, ranging from 200 to 2K instances. We did not exceed 64 KB per instance due to a limitation of the Silk system20, in order to enable a fair comparison of the systems participating in this track.
The evaluation for both test cases was carried out using the HOBBIT platform. 3.8
The SPIMBENCH track consists of matching instances that are found to refer to the same real-world entity corresponding to a creative work (that can be a news item, 19 https://www.tomtom.com/en_gr/ 20 https://github.com/silk-framework/silk/issues/57 blog post or programme). The datasets were generated and transformed using SPIMBENCH [62] by altering a set of original linked data through value-based, structurebased, and semantics-aware transformations (simple combination of transformations). They share almost the same ontology (with some differences in property level, due to the structure-based transformations), which describes instances using 22 classes, 31 data properties, and 85 object properties. Participants are requested to produce a set of correspondences between the pairs of matching instances from the source and target datasets that are found to refer to the same real-world entity. An instance in the source dataset can have none or one matching counterpart in the target dataset. The SPIMBENCH task uses two sets of datasets21 with different scales (i.e., number of instances to match): – Sandbox (380 INSTANCES, 10000 TRIPLES). It contains two datasets called source (Tbox1) and target (Tbox2) as well as the set of expected correspondences (i.e., reference alignment). – Mainbox (1800 CWs, 50000 TRIPLES). It contains two datasets called source (Tbox1) and target (Tbox2). This test case is blind, meaning that the reference alignment is not given to the participants.
In both cases, the goal is to discover the correspondences among the instances in the source dataset (Tbox1) and the instances in the target dataset (Tbox2).
The evaluation was carried out using the HOBBIT platform. 3.9
The Geolink Cruise track consists of matching instances from different ontologies describing the same cruise in the real-world. The datasets are collected from the Geolink project,22 which was funded under the U.S. National Science Foundation’s EarthCube initiative. The datasets and alignments are guaranteed to contain real-world use cases to solve the instance matching problem in practice. In the GeoLink Cruise dataset, there are two ontologies which are GeoLink Base Ontology (gbo) and GeoLink Modular Ontology (gmo). The data providers from different organizations populate their own data into these two ontologies. In this track, we utilize instances from two different data providers, Biological and Chemical Oceanography Data Management Office (bco21 Although the files are called Tbox1 and Tbox2, they actually contain a Tbox and an Abox. 22 https://www.geolink.org/ dmo)23 and Rolling Deck to Repository (r2r)24 and populate all the triples related to Cruise into two ontologies. There are 491 Cruise pairs between these two datasets that are labelled by domain experts as equivalent. Some statistic information of the ontologies are listed in the Table 5. More details of this benchmark can be found in the paper [6]. The Knowledge Graph track was run for the third year. The task of the track is to match pairs of knowledge graphs, whose schema and instances have to be matched simultaneously. The individual knowledge graphs are created by running the DBpedia extraction framework on eight different Wikis from the Fandom Wiki hosting platform25 in the course of the DBkWik project [34, 33]. They cover different topics (movies, games, comics and books) and three Knowledge Graph clusters sharing the same domain e.g. star trek, as shown in Table 6.
The evaluation is based on reference correspondences at both schema and instance levels. While the schema level correspondences were created by experts, the instance correspondences were extracted from the wiki page itself. Due to the fact that not all inter wiki links on a page represent the same concept a few restrictions were made: 1) only links in sections with a header containing “link” are used, 2) all links are removed where the source page links to more than one concept in another wiki (ensures the alignments are functional), 3) multiple links which point to the same concept are also removed (ensures injectivity), 4) links to disambiguation pages were manually checked and corrected. Since we do not have a correspondence for each instance, class, and property in the graphs, this gold standard is only a partial gold standard.
The evaluation was executed on a virtual machine (VM) with 32GB of RAM and 16 vCPUs (2.4 GHz), with Debian 9 operating system and Openjdk version 1.8.0 265, 23 https://www.bco-dmo.org/ 24 https://www.rvdata.us/ 25 https://www.wikia.com/ using the SEALS client (version 7.0.5). The -o option in SEALS is used to provide the two knowledge graphs which should be matched. This decreases runtime because the matching system can load the input from local files rather than downloading it from HTTP URLs. We could not use the ”-x” option of SEALS because the evaluation routine needed to be changed for two reasons: first, to differentiate between results for class, property, and instance correspondences, and second, to deal with the partial nature of the gold standard.
The alignments were evaluated based on precision, recall, and f-measure for classes, properties, and instances (each in isolation). The partial gold standard contained 1:1 correspondences and we further assume that in each knowledge graph, only one representation of the concept exists. This means that if we have a correspondence in our gold standard, we count a correspondence to a different concept as a false positive. The count of false negatives is only increased if we have a 1:1 correspondence and it is not found by a matcher. The whole source code for generating the evaluation results is also available.26
Additionally we run the matchers on three hidden test cases where the source wikis are: Marvel Cinematic Universe, Memory Alpha, and Star Wars Wiki. The target wiki is for all test cases the same. It is the lyrics wiki with 1,062,920 instances, 270 properties and 67 classes. The goal is to explore how the matchers behave on matching mostly unrelated knowledge graphs.
As a baseline, we employed two simple string matching approaches. The source code for these matchers is publicly available.27
The interactive matching track aims to assess the performance of semi-automated matching systems by simulating user interaction [56, 19, 50]. The evaluation thus focuses on how interaction with the user improves the matching results. Currently, this track does not evaluate the user experience or the user interfaces of the systems [39, 19].
The interactive matching track is based on the datasets from the Anatomy and Conference tracks, which have been previously described. It relies on the SEALS client’s Oracle class to simulate user interactions. An interactive matching system can present a collection of correspondences simultaneously to the oracle, which will tell the system whether that correspondence is correct or not. If a system presents up to three correspondences together and each correspondence presented has a mapped entity (i.e., class or property) in common with at least one other correspondence presented, the oracle counts this as a single interaction, under the rationale that this corresponds to a scenario where a user is asked to choose between conflicting candidate correspondences. To simulate the possibility of user errors, the oracle can be set to reply with a given error probability (randomly, from a uniform distribution). We evaluated systems with four different error rates: 0.0 (perfect user), 0.1, 0.2, and 0.3. 26 http://oaei.ontologymatching.org/2020/results/knowledgegraph/ matching-eval-trackspecific.zip 27 http://oaei.ontologymatching.org/2019/results/knowledgegraph/ kgBaselineMatchers.zip
In addition to the standard evaluation parameters, we also compute the number of requests made by the system, the total number of distinct correspondences asked, the number of positive and negative answers from the oracle, the performance of the system according to the oracle (to assess the impact of the oracle errors on the system) and finally, the performance of the oracle itself (to assess how erroneous it was).
The evaluation was carried out on a server with 3.46 GHz (6 cores) and 8GB RAM allocated to the matching systems. For systems requiring more RAM, the evaluation was carried out on a Windows 10 (64-bit) desktop with an Intel Core i7-6700 CPU @ 3.40GHz x 8 with 16GB RAM allocated. Each system was run ten times and the final result of a system for each error rate represents the average of these runs. For the Conference dataset with the ra1 alignment, precision and recall correspond to the micro-average over all ontology pairs, whereas the number of interactions is the total number of interactions for all the pairs. 3.12
The complex matching track is meant to evaluate the matchers based on their ability to generate complex alignments. A complex alignment is composed of complex correspondences typically involving more than two ontology entities, such as o1:AcceptedPaper o2:Paper u o2:hasDecision.o2:Acceptance. In addition to last year’s datasets [69], two new datasets have been added: Populated Geolink and Populated Enslaved.
The complex conference dataset is composed of three ontologies: cmt, conference and ekaw from the conference dataset. The reference alignment was created as a consensus between experts. In the evaluation process, the matchers can take the simple reference alignment ra1 as input. The precision and recall measures are manually calculated over the complex equivalence correspondences only.
The populated complex conference is a populated version of the Conference dataset. 5 ontologies have been populated with more or less common instances resulting in 6 datasets (6 versions on the seals repository: v0, v20, v40, v60, v80 and v100). The alignments were evaluated based on Competency Questions for Alignment, i.e., basic queries that the alignment should be able to cover [67]. The queries are automatically rewritten using 2 systems: that from [68] which covers (1:n) correspondences with EDOAL expressions; and a system which compares the answers (sets of instances or sets of pairs of instances) of the source query and the source member of the correspondences and which outputs the target member if both sets are identical. The best rewritten query scores are kept. A precision score is given by comparing the instances described by the source and target members of the correspondences.
The Hydrography dataset consists of matching four different source ontologies (hydro3, hydrOntology-translated, hydrOntology-native, and cree) to a single target ontology (SWO) [12]. The evaluation process is based on three subtasks: given an entity from the source ontology, identify all related entities in the source and target ontology; given an entity in the source ontology and the set of related entities, identify the logical relation that holds between them; identify the full complex correspondences. The three subtasks were evaluated based on relaxed precision and recall [21].
The GeoLink dataset derives from the homonymous project, funded under the U.S. National Science Foundation’s EarthCube initiative. It is composed of two ontologies: the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO). The GeoLink project is a real-world use case of ontologies. The alignment between the two ontologies was developed in consultation with domain experts from several geoscience research institutions. More detailed information on this benchmark can be found in [72]. Evaluation was done in the same way as with the Hydrography dataset. The evaluation platform was a MacBook Pro with a 2.5 GHz Intel Core i7 processor and 16 GB of 1600 MHz DDR3 RAM running mac OS Catalina version 10.15.6.
The Populated GeoLink dataset is designed to allow alignment systems that rely on the instance data to participate over the Geolink benchmark. The instance data are from real-worlds and collected from seven data repositories in the Geolink project. More detailed information on this benchmark can be found in [73]. Evaluation was done in the same way as with the Hydrography dataset. The evaluation platform was a MacBook Pro with a 2.5 GHz Intel Core i7 processor and 16 GB of 1600 MHz DDR3 RAM running mac OS Catalina version 10.15.6.
The Populated Enslaved dataset was derived from the ongoing project entitled “Enslaved: People of the Historical Slave Trade28 and funded by The Andrew W. Mellon Foundation where the focus is on tracking the movements and details of peoples in the historical slave trade. It is composed of the Enslaved ontology and the Enslaved Wikibase repository along with the populated instance data. To the best of our knowledge, it is the first attempt to align a modular ontology to the Wikibase repository. More detailed information on this benchmark can be found in [71]. Evaluation was done in the same way as with the Hydrography dataset. The evaluation platform was a MacBook Pro with a 2.5 GHz Intel Core i7 processor and 16 GB of 1600 MHz DDR3 RAM running mac OS Catalina version 10.15.6.
The Taxon dataset is composed of four knowledge bases containing knowledge about plant taxonomy: AgronomicTaxon, AGROVOC, TAXREF-LD and DBpedia. The evaluation is two-fold: first, the precision of the output alignment is manually assessed; then, a set of source queries are rewritten using the output alignment. The rewritten target query is then manually classified as correct or incorrect. A source query is considered successfully rewritten if at least one of the target queries is semantically equivalent to it. The proportion of source queries successfully rewritten is then calculated (QWR in the results table). The evaluation over this dataset is open to all matching systems (simple or complex) but some queries can not be rewritten without complex correspondences. The evaluation was performed with an Ubuntu 16.04 machine configured with 16GB of RAM running under a i7-4790K CPU 4.00GHz x 8 processors. 4 4.1
Following an initial period of growth, the number of OAEI participants has remained approximately constant since 2012, which is slightly over 20. This year we count with 28 https://enslaved.org/ 19 participating systems. Table 7 lists the participants and the tracks in which they competed. Some matching systems participated with different variants (AML, LogMap) whereas others were evaluated with different configurations, as requested by developers (see test case sections for details). tseySm ILNA ec2LODAV LAM LCAM ROAA oxTBA SEKDM RCNAA IFLTRM ilyL agopLM i-agopoLBM tagopLLM ttcenoonnCO RADON i-renERm ilkS lieegnVA ttrckhMW tlao19=T Confidence X X X X X X X X X X X X X X X X 16 anatomy # # # # # # # # 11 conference # # # # # # # # # 10 multifarm # # # # # # # # #G # # # # # 6
complex # # # # # # # # # # # # # # # # 3 interactive # # # # # # # # # # # # # # # # 3
largebio # G# # # #G # # # # # # # # 8 phenotype # # # # # # # # # # # # 7
biodiv # G# # # # # # # # # # # # #G 7 spimbench # # # # # # # # # # # # # # 5 link discovery # # # # # # # # # # # # # # # # 3 geolink cruise # # # # # # # # # # # # # # # # # # # 0 knowledge graph # # # # # # #G # # # # # 8 total 3 6 10 1 1 6 4 1 1 4 9 5 7 1 1 1 1 2 7 71 Confidence pertains to the confidence scores returned by the system, with X indicating that they are non-boolean; # indicates that the system did not participate in the track; indicates that it participated fully in the track; and G# indicates that it participated in or completed only part of the tasks of the track.
A number of participating systems use external sources of background knowledge, which are especially critical in matching ontologies in the biomedical domain. LogMapBio uses BioPortal as mediating ontology provider, that is, it retrieves from BioPortal the most suitable top-10 ontologies for each matching task. LogMap uses normalizations and spelling variants from the general (biomedical) purpose SPECIALIST Lexicon. AML has three sources of background knowledge which can be used as mediators between the input ontologies: the Uber Anatomy Ontology (Uberon), the Human Disease Ontology (DOID) and the Medical Subject Headings (MeSH). XMAP and Lily use a dictionary of synonyms (pre)extracted from the UMLS Metathesaurus. In addition Lily also uses a dictionary of synonyms (pre)extracted from BioPortal. 4.2 The results for the Anatomy track are shown in Table 8. Of the 11 systems participating in the Anatomy track, 10 achieved an F-measure higher than the StringEquiv baseline. Three systems were first time participants (ATBox, OntoConnect, and DESKMatcher). Long-term participating systems showed few changes in comparison with previous years with respect to alignment quality (precision, recall, F-measure, and recall+), size and run time. The exceptions were ALIN which increased in precision (from 0.974 to 0.986), recall (from 0.698 to 0.72), recall+ (from 0.365 to 0.382), F-measure (from 0.813 to 0.832), and size (from 1086 to 1107), and Lily that increased in precision (from 0.873 to 0.901), recall (from 0.796 to 0.902), recall+ (from 0.52 to 0.747), F-measure (from 0.833 to 0.901), and size (from 1381 to 1517). In terms of run time, 4 out of 11 systems computed an alignment in less than 100 seconds, a ratio which is similar to 2019 (5 out of 12). LogMapLite remains the system with the shortest runtime. Regarding quality, AML remains the system with the highest F-measure (0.941) and recall+ (0.81), but 3 other systems obtained an F-measure above 0.88 (Lily, LogMapBio, and LogMap) which is at least as good as the best systems in OAEI 2007-2010. Like in previous years, there is no significant correlation between the quality of the generated alignment and the run time. Four systems produced coherent alignments. Four systems participating this year did participate to this track last year as well: AML and the LogMap family systems (LogMap, LogMapBio and LogMapLT). Three are new participants: ATBox, ALOD2Vec and Wiktionary. The newcomer ATBox did not register explicitly to the track but could cope with at least one task so we did include its results. As in the previous edition, we used precision, recall and F-measure to evaluate the performance of the participating systems. The results for the Biodiversity and Ecology track are shown in Table 9.
In comparison to previous years, we observed a decrease in the number of systems that succeeded to generate alignments for the ENVO-SWEET and FLOPO-PTO tasks. Basically, except of AML and the LogMap variants, only ATBox could cope with the LogMap LogMapBio AML LogMapLt ATBox Wiktionary ALOD2Vec AML LogMapLt ATBox LogMap LogMapBio
AML tasks with fair results. ALOD2Vec and Wiktionary generated a similar, huge set of non meaningful mappings with a very low F-measure as shown in Table 9.
The results of the participating systems have slightly increased in terms of Fmeasure for both first two tasks compared to last year. In terms of run time, Wiktionary, ALOD2Vec and LogMapBio took the longer time, for the latter due to the loading of mediating ontologies from BioPortal.
For the FLOPO-PTO task, LogMap and LogMapBio achieved the highest Fmeasure. AML generated a large number of mappings (significantly bigger than the size of the reference alignment), those alignments were mostly subsumption ones. In order to evaluate the precision in a more significant manner, we had to calculate an approximation by manually assessing a subset of around 100 mappings, that were not present in the reference alignment. LogMapLt and ATBox achieved a high precision but the lowest recall.
Regarding the ENVO-SWEET task, AML ranked first in terms of F-measure, followed by LogMapLt and ATBox. The systems with the highest precision (LogMap and LogMapBio) achieve the lowest recall. Again here, AML generated a bigger set with a high number of subsumption mappings, it still achieved the best F-Measure for the task. It is worth nothing that due the specific structure of the SWEET ontology, a lot of the false positives come from homonyms [45].
The ANAEETHES-GEMET and AGROVOC-NALT matching tasks have been introduced to the track this year, with the particularity of being resources developed in SKOS. Only AML could handle the files in their original format. LogMap and its variants could generate mappings for ANAEETHES-GEMET, based on ontology files after being transformed automatically into OWL. For the transformation, we made use of a source code29 that was directly derived from AML ontology parsing module, kindly provided to us by its developers. LogMap and LogMapBio achieve the best results with LogMap processing the task in a shorter time. LogMapBio took a much longer time due to downloading 10 mediating ontologies from BioPortal, still the gain is not significant in terms of performance. The AGROVOC-NALT task has been managed only by AML. All other systems failed in generating mappings on both the SKOS and OWL versions of the thesauri. AML achieves good results and a very high precicion. It generated a higher number of mappings (around 1000 more) than the curated reference alignment. We performed a manual assessment of a subset of those mappings to reevaluate the precision and F-measure.
Overall, in this third evaluation, the results obtained from participating systems for the two tasks ENVO-SWEET and FLOPO-PTO remained similar with a slight increase in terms of F-measure compared to last year. The results of the two new tracks demonstrate systems (beside AML) are not ready to handle SKOS. Sometimes automatically transforming to OWL helps to avoid the issue, sometimes not. The number of mappings in the AGROVOC-NALT track is really a challenge and AML does not loose in performance which demonstrates that besides being the more tolerant tool in terms of format, it also scales up to large size thesauri. The conference evaluation results using the sharp reference alignment rar2 are shown in Table 10. For the sake of brevity, only results with this reference alignment and considering both classes and properties are shown. For more detailed evaluation results, please check conference track’s web page.
With regard to two baselines we can group tools according to system’s position: eight matching systems outperformed both baselines (ALIN, AML, ALOD2Vec, ATBox, LogMap, LogMapLt, VeeAlign and Wiktionary); two performed worse than both baselines (DESKMatcher and Lily). Three matchers (ALIN and Lily) do not match properties at all. Naturally, this has a negative effect on their overall performance.
The performance of all matching systems regarding their precision, recall and F1measure is plotted in Figure 1. Systems are represented as squares or triangles, whereas the baselines are represented as circles.
With respect to logical coherence [63, 64], as the last year, only three tools (ALIN, AML and LogMap) have no consistency principle violation.
As the last year we performed analysis of the False Positives, i.e. correspondences discovered by the tools which were evaluated as incorrect. The list of the False Positives 29 http://oaei.ontologymatching.org/2020/biodiv/code/SKOS2OWL.zip is available on the conference track’s web page as well as further details about this evaluation. Comparing to the previous year we added the comparison of ”why was an alignment discovered” assigned by us with the explanation for the alignment provided by the system itself. This year three systems generated explanations with the mappings ALOD2Vec, DESKMatcher and Wiktionary.
The Conference evaluation results using the uncertain reference alignments are presented in Table 11. Out of the 10 alignment systems, three (ALIN, DESKMatcher, LogMapLt) use 1.0 as the confidence value for all matches they identify. The remaining 7 systems (ALOD2Vec, AML, ATBOX, Lily, LogMap, VeeAlign, Wiktionary) have a wide variation of confidence values.
The Multifarm evaluation results based on the blind dataset are presented in Table 15. They have been computed using the Alignment API 4.9 and can slightly differ from those computed with the SEALS client. We haven’t applied any threshold on the results. We do not report the results of non-specific systems here, as we could observe in the last campaigns that they can have intermediate results in the “same ontologies” task (ii) and poor performance in the “different ontologies” task (i). The detailed results can be investigated on the page of multifarm track results32.
Time #pairs SizTeypePr(ei)c.– 22 Fte-smts. per pRaeicr.
AML outperforms all other systems in terms of F-measure for task i) (same behaviour in the last campaigns). In terms of precision, Wiktionary is the system that generates the most precise alignments, followed by LogMap, VeeAlign and AML. With respect to the task ii) LogMap has the overall best performance. Comparing the results from last year, in terms F-measure (cases of type i), AML maintains its overall performance (.45 in 2019, .46 in 2018, .46 in 2017, .45 in 2016 and .47 in 2015). The same could be observed for LogMap (.37 in 2019, .37 in 2018, .36 in 2017, and .37 in 2016). The performance in terms of F-measure of Wiktionary also remains stable. In terms of runtime, the results are not really comparable with the ones in the last campaign considering the fact the SEALS repositories have been moved to another server with a different configuration.
Overall, the F-measure for blind tests remains relatively stable across campaigns. As observed in previous campaigns, systems still privilege precision over recall. Furthermore, the overall results in MultiFarm are lower than the ones obtained for the original English version of the Conference dataset. 32 http://oaei.ontologymatching.org/2020/results/multifarm/index.
html This year the Link Discovery track counted three participants in the Spatial test case: AML, Silk and RADON. Those were the exact same systems (and versions) that participated on OAEI 2019.
We divided the Spatial test cases into four suites. In the first two suites (SLL and LLL), the systems were asked to match LineStrings to LineStrings considering a given relation for 200 and 2K instances for the TomTom and Spaten datasets. In the last two tasks (SLP, LLP), the systems were asked to match LineStrings to Polygons (or Polygons to LineStrings depending on the relation) again for both datasets. Since the precision, recall and F-measure results from all systems were equal to 1.0, we are only presenting results regarding the time performance. The time performance of the matching systems in the SLL, LLL, SLP and LLP suites are shown in Figures 2-3. The results can also be found in HOBBIT git (https://hobbit-project.github.io/ OAEI_2020.html).
In the SLL suite, RADON has the best performance in most cases except for the Touches and Intersects relations, followed by AML. Silk seems to need the most time, particularly for Touches and Intersects relations in the TomTom dataset and Overlaps in both datasets.
In the LLL suite we have a more clear view of the capabilities of the systems with the increase in the number of instances. In this case, RADON and Silk have similar behavior as in the small dataset, but it is more clear that the systems need much more time to match instances from the TomTom dataset. RADON has still the best performance in most cases. AML has the next best performance and is able to handle some cases better than other systems (e.g. Touches and Intersects), however, it also hits the platform time limit in the case of Disjoint.
In the SLP suite, in contrast to the first two suites, RADON has the best performance for all relations. AML and Silk have minor time differences and, depending on the case, one is slightly better than the other. All the systems need more time for the TomTom dataset but due to the small size of the instances the time difference is minor.
In the LLP suite, RADON again has the best performance in all cases. AML hits the platform time limit in Disjoint relations on both datasets and is better than Silk in most cases except Contains and Within on the TomTom dataset where it needs an excessive amount of time.
Taking into account the executed test cases we can identify the capabilities of the tested systems as well as suggest some improvements. All the systems participated in most of the test cases, with the exception of Silk which did not participate in the Covers and Covered By test cases.
RADON was the only system that successfully addressed all the tasks, and had the best performance for the SLP and LLP suites, but it can be improved for the Touches and Intersects relations for the SLL and LLL suites. AML performs extremely well in most cases, but can be improved in the cases of Covers/Covered By and Contains/Within when it comes to LineStrings/Polygons Tasks and especially in Disjoint relations where it hits the platform time limit. Silk can be improved for the Touches, Intersects and Overlaps relations and for the SLL and LLL tasks and for the Disjoint relation in SLP and LLP Tasks.
In general, all systems needed more time to match the TomTom dataset than the Spaten one, due to the smaller number of points per instance in the latter. Comparing the LineString/LineString to the LineString/Polygon Tasks we can say that all the systems needed less time for the first for the Contains, Within, Covers and Covered by relations, more time for the Touches, Instersects and Crosses relations, and approximately the same time for the Disjoint relation. This year, the SPIMBENCH track counted five participants: AML, Lily, LogMap, FTRLIM and REMiner. REMiner participated for the first time this year while AML, Lily, LogMap and FTRLIM also participated last year. The evaluation results of the track are shown in Table 16. The results can also be found in HOBBIT git (https: //hobbit-project.github.io/OAEI_2020.html).
Lily and FTRLIM had the best performance overall both in terms of F-measure and run time. Notably, their run time scaled very well with the increase in the number of instances. REMiner produces the best results (almost full) for all metrics. Lily, FTRLIM and AML had a higher recall than precision, while Lily and FTRLIM had a full recall. By contrast, REMiner and LogMap had a higher precision and lower recall, while REMiner had a full precision. AML, LogMap and REMiner had a similar run time performance. We evaluated all participants in the OAEI 2020. Unfortunately, none of the current alignment systems can generate the coreferences between the cruise instances in the Geolink Cruise benchmark. The state of the art alignment systems work well on finding the links with a higher string similarity or string synonyms between two objects. However, in terms of the instances with lower string similarities, or the external information is not available or very limited to help the aligning task. Another kind of algorithm is needed, like finding the relation of the instances based on the underlying structure of the graphs. We hope that system will manage this track in future years. We evaluated all SEALS participants in the OAEI (even those not registered for the track) on a very small matching task33. This revealed that not all systems were able to handle the task, and in the end, only the following systems were evaluated: ALOD2Vec, AML, ATBox, DESKMatcher, LogMapKG, LogMapLt, Wiktionary. We also evaluated LogMapBio but compared to LogMapKG it does not change the results (meaning that the external knowledge does not help in these cases which is reasonable). LogMapKG is the LogMap systems which returns TBox as well as ABox correspondences. In this year, two systems registered especially for this track but were unable to finally submit their system in time. This shows that there is a demand for this track and we plan to provide this track also next year. We hope that the system developers are able to submit the system next year. In comparison to the previous years, we have new matchers like ALOD2Vec (which produced an error in 2018), ATBox (new), and DESKMatcher (new).
What did not change over the years is that some matchers do not return a valid alignment file. The reason is the xml format of this file together with URIs in the knowledge graph containing special characters e.g. ampersand. These characters should be encoded, in order that xml parsers can process this file. Thus a post processing step is executed which tries to create a valid xml file. The resulting alignments are available for download. 34
Table 17 shows the aggregated results for all systems, including the number of tasks in which they were able to generate a non-empty alignment (#tasks) and the average number of generated correspondences in those tasks (size). We report the macro averaged precision, F-measure, and recall results where we do not distinguishing empty and erroneous (or not generated) alignments. The values between parentheses show the results when considering only non empty alignments.
All systems were able to generate class correspondences. In terms of F-measure, AML is still the best one and only DESKMatcher could not beat the baselines. The recall values are higher than last year (maximum of 0.77) which shows that some matchers improved and can find more class correspondences. Nevertheless there is still room for improvement and some of these class matches looks like they are not easy to find.
In the third year of this track all systems except the LogMap family are able to return property correspondences. This is a huge improvement (which happens over the years) because it makes the systems more usable in real case scenarios where a property might not be classified as owl:ObjectProperty or owl:DatatypeProperty. The systems ALOD2Vec, ATBox, and Wiktionary could achieve a F-measure of 0.95 or more which shows that property matching is easier in this track than class or instance matching.
With respect to instance correspondences, two systems (ALOD2Vec and Wikitionary) exceed the best performance of last year with an F-measure of 0.87. The margin between the baseline and the best systems is now a bit greater but still only 0.03 away. Again LogMapKG returns a much higher number of instance correspondences (29,190 33 http://oaei.ontologymatching.org/2019/results/knowledgegraph/ small_test.zip 34 http://oaei.ontologymatching.org/2020/results/knowledgegraph/ oaei2020-knowledgegraph-alignments.zip
ALOD2Vec 0:13:24 AML 0:50:55 ATBox 0:16:22 baselineAltLabel 0:10:57 baselineLabel 0:10:44 DESKMatcher 0:13:54 LogMapKG 2:47:51 LogMapLt 0:07:19 Wiktionary 0:30:12 ALOD2Vec 0:13:24 AML 0:50:55 ATBox 0:16:22 baselineAltLabel 0:10:57 baselineLabel 0:10:44 DESKMatcher 0:13:54 LogMapKG 2:47:51 LogMapLt 0:07:19 Wiktionary 0:30:12 ALOD2Vec 0:13:24 AML 0:50:55 ATBox 0:16:22 baselineAltLabel 0:10:57 baselineLabel 0:10:44 DESKMatcher 0:13:54 LogMapKG 2:47:51 LogMapLt 0:07:19 Wiktionary 0:30:12 ALOD2Vec 0:13:24 AML 0:50:55 ATBox 0:16:22 baselineAltLabel 0:10:57 baselineLabel 0:10:44 DESKMatcher 0:13:54 LogMapKG 2:47:51 LogMapLt 0:07:19 Wiktionary 0:30:12
Class performance 5 20.0 1.00 0.80 0.67 5 23.6 0.98 0.89 0.81 5 25.6 0.97 0.87 0.79 5 16.4 1.00 0.74 0.59 5 16.4 1.00 0.74 0.59 5 91.4 0.76 0.71 0.66 5 24.0 0.95 0.84 0.76 4 23.0 0.80 (1.00) 0.56 (0.70) 0.43 (0.54) 5 22.4 1.00 0.80 0.67 in average) than all other participants but the recall is only slighly higher (0.03 to the next best recall of 0.83).
When analyzing the confidence values of the alignments, it turns out that most matchers makes use of the range between zero and one. Only DESKMatcher, LogMapLt, and the baselines return only 1.0. Further analysis can be made by browsing to the dashboard 35 which is generated with the MELT framework [37].
Regarding runtime, LogMapKG was was the slowest system (2:47:51 for all test cases), followed by AML (0:50:55). Besides the baseline, four matchers were able to compute the alignment in under 20 minutes which is a reasonable time for this track.
In this year we also run the matchers in the hidden test cases to see how many instance correspondences they return. The systems DESKMatcher, LogMapKG, and AML (in test case starwars-lyrics) run into memory issues. Due to the fact that there is no partial nor full gold standard available for these test cases, only the number of returned instances correspondences is analyzed. In [35] we run the matchers from OAEI 2019 on these hidden test cases and manually evaluated 1,050 returned correspondences. This results in the number of matches and a approximation of the precision for each matcher and test case. Based on these values, the estimated number of true positives for each test case can be calculated. The average and maximum number of expected instance correspondences is shown in table 18 together with the number of instance correspondences returned from OAEI 2020 matchers One can see that they return 1-2 orders of magnitude more correspondences than the number of expected true positives. Especially LogMapLt returns the highest number of correspondences in the first two test cases and Wiktionary in the last test case. ATBox and AML return less correspondences and a higher precision is expected in these test cases. marvelcinematicuniverse 292.7 584.8 memoryalpha 73.6 285.5 starwars 48.5 109.1
This year, three systems participated in the Interactive matching track. They are ALIN, AML, and LogMap. Their results are shown in Table 19 and Figure 4 for both Anatomy and Conference datasets.
The table includes the following information (column names within parentheses): – The performance of the system: Precision (Prec.), Recall (Rec.) and F-measure (Fm.) with respect to the fixed reference alignment, as well as Recall+ (Rec.+) for the 35 http://oaei.ontologymatching.org/2020/results/knowledgegraph/ knowledge_graph_dashboard.html 0.874 0.456 0.599 0.915 0.705 0.796 0.75 0.679 0.713 0.612 0.648 0.629 0.516 0.617 0.562 0.841 0.659 0.739 0.91 0.698 0.79 0.843 0.682 0.754 0.777 0.677 0.723 0.721 0.65 0.684 – – – – – – – – – – – – – – – NI stands for non-interactive, and refers to the results obtained by the matching system in the original track.
Anatomy task. To facilitate the assessment of the impact of user interactions, we also provide the performance results from the original tracks, without interaction (line with Error NI). – To ascertain the impact of the oracle errors, we provide the performance of the system with respect to the oracle (i.e., the reference alignment as modified by the errors introduced by the oracle: Precision oracle (Prec. oracle), Recall oracle (Rec. oracle) and F-measure oracle (F-m. oracle). For a perfect oracle these values match the actual performance of the system. – Total requests (Tot Reqs.) represents the number of distinct user interactions with the tool, where each interaction can contain one to three conflicting correspondences, that could be analysed simultaneously by a user. – Distinct correspondences (Dist. Mapps) counts the total number of correspondences for which the oracle gave feedback to the user (regardless of whether they were submitted simultaneously, or separately). – Finally, the performance of the oracle itself with respect to the errors it introduced can be gauged through the positive precision (Pos. Prec.) and negative precision (Neg. Prec.), which measure respectively the fraction of positive and negative answers given by the oracle that are correct. For a perfect oracle these values are equal to 1 (or 0, if no questions were asked).
The figure shows the time intervals between the questions to the user/oracle for the different systems and error rates. Different runs are depicted with different colors.
The matching systems that participated in this track employ different userinteraction strategies. While LogMap, and AML make use of user interactions exclusively in the post-matching steps to filter their candidate correspondences, ALIN can also add new candidate correspondences to its initial set. LogMap and AML both request feedback on only selected correspondences candidates (based on their similarity patterns or their involvement in unsatisfiabilities) and AML presents one correspondence at a time to the user. ALIN and LogMap can both ask the oracle to analyze several conflicting correspondences simultaneously.
The performance of the systems usually improves when interacting with a perfect oracle in comparison with no interaction. ALIN is the system that improves the most, because its high number of oracle requests and its non-interactive performance was the lowest of the interactive systems, and thus the easiest to improve.
Although system performance deteriorates when the error rate increases, there are still benefits from the user interaction—some of the systems’ measures stay above their non-interactive values even for the larger error rates. Naturally, the more a system relies on the oracle, the more its performance tends to be affected by the oracle’s errors.
The impact of the oracle’s errors is linear for ALIN, and AML in most tasks, as the F-measure according to the oracle remains approximately constant across all error rates. It is supra-linear for LogMap in all datasets.
Another aspect that was assessed, was the response time of systems, i.e., the time between requests. Two models for system response times are frequently used in the literature [16]: Shneiderman and Seow take different approaches to categorize the response times taking a task-centered view and a user-centered view respectively. According to task complexity, Shneiderman defines response time in four categories: typing, mouse movement (50-150 ms), simple frequent tasks (1 s), common tasks (2-4 s) and complex tasks (8-12 s). While Seow’s definition of response time is based on the user expectations towards the execution of a task: instantaneous (100-200 ms), immediate (0.5-1 s), continuous (2-5 s), captive (7-10 s). Ontology alignment is a cognitively demanding task and can fall into the third or fourth categories in both models. In this regard the response times (request intervals as we call them above) observed in all datasets fall into the tolerable and acceptable response times, and even into the first categories, in both models. The request intervals for AML, LogMap and ALIN stay at a few milliseconds for most datasets. It could be the case, however, that a user would not be able to take advantage of these low response times because the task complexity may result in higher user response time (i.e., the time the user needs to respond to the system after the system is ready).
Three systems were able to generate complex correspondences: AMLC, AROA, and CANARD. The results for the other systems are reported in terms of simple alignments. The results of the systems on the five test cases are summarized in Table 20.
With respect to the Hydrography test cases, only AMLC can generate two correct complex correspondences which are stating that a class in the source ontology is equivalent to the union of two classes in the target ontology. Most of the systems achieved fair results in terms of precision, but the low recall reflects that the current ontology alignment systems still need to be improved to find more complex relations.
In terms of Geolink and populated GeoLink test cases, the real-world instance data from GeoLink Project is also populated into the ontology in order to enable the systems that depend on instance-based matching algorithms to evaluate their performance. There are three alignment systems that generate complex alignments in GeoLink Benchmark, which are AMLC, AROA, and CANARD. AMLC didn’t find any correct complex alignment, while AROA and CANARD achieved relatively good performance. One of the reasons may be that these two systems are instance-based systems, which rely on the shared instances between ontologies. In other words, the shared instance data between two ontologies would be helpful to the matching process.
In the populated Enslaved test case, only AMLC, AROA, and CANARD can produce complex alignments. The relaxed precision of AMLC and AROA look relatively fair, while CANARD reports a lower relaxed precision. AROA found the largest number of the complex correspondences among three systems, while the AMLC outputs the largest number of the simple correspondences.
With respect to the Conference test cases the track has the same participant, AMLC, as the last year. Based on the evaluation the alignments from AMLC now conforms to the EDOAL syntax but otherwise the content of the alignment is the same.
In the Populated Conference test case, AMLC’s results precision and coverage scores are lower than last year, probably because it did not take a simple reference alignment as input. CANARD’s results are close to last year’s. ALIN obtains the best precision score.
In the Taxon dataset, CANARD obtains the best coverage score but its precision has decreased significantly. This year, AMLC could be evaluated on this dataset ; however, the output correspondences did not cover the evaluation queries. The simple matcher obtains approximatively the same coverage score.
A more detailed discussion of the results of each task can be found in the OAEI page for this track. For a third edition of complex matching in an OAEI campaign, and given the inherent difficulty of the task, the results and participation are promising albeit still modest. 5
In 2020, we witnessed a slight decrease in the number of participants in comparison with previous years, but with a healthy mix of new and returning systems. However, like last year, the distribution of participants by tracks was uneven. In future editions we should facilitate the participation of non-Java systems (the use of the MELT framework [36] was a step forward this year) and Machine Learning based system by providing partial alignment sets for supervised learning. Furthermore, new systems might use deep learning technology which requires specific hardware like GPUs and the like. An option would be a simple HTTP interface to allow the deployment and evaluation on different machines. The MELT framework can be easily extended with such an interface while at the same time compatibility with SEALS and HOBBIT can be retained.
The schema matching tracks saw abundant participation, but, as has been the trend of the recent years, little substantial progress in terms of quality of the results or run time of top matching systems, judging from the long-standing tracks. On the one hand, this may be a sign of a performance plateau being reached by existing strategies and algorithms, which would suggest that new technology is needed to obtain significant improvements. On the other hand, it is also true that established matching systems tend to focus more on new tracks and datasets than on improving their performance in longstanding tracks, whereas new systems typically struggle to compete with established ones.
The number of matching systems capable of handling very large ontologies has increased slightly over the last years, but is still relatively modest, judging from the Large Biomedical Ontologies track. We will aim at facilitating participation in future editions of this track by providing techniques to divide the matching tasks in manageable subtasks (e.g., [40]).
According to the Conference track there is still need for an improvement with regard to the ability of matching systems to match properties. To assist system developers in tackling this aspect we provided a more detailed evaluation in terms of the analysis of the false positives per matching system (available on the Conference track web page). This year this has been extended by the inspection of the explanation of the correspondences provided by the systems. As already pointed out last year, less encouraging is the low number of systems concerned with the logical coherence of the alignments they produce, an aspect which is critical for several semantic web applications. Perhaps a more direct approach is needed to promote this topic, such as providing a more indepth analysis of the causes of incoherence in the evaluation or even organizing a future track focusing on logical coherence alone. It is, however, clear that this is not an easy task. When naively computing coherent alignments correct correspondences may be removed and incorrect ones are kept, and therefore a domain expert should be involved in the validation of different logical solutions [57, 49]. Finally, this year it was shown that matching domain ontology to cross-domain ontology is difficult task for general matching systems. While this has been done as an experiment without announcing beforehand, we suppose to announce this as new test cases within the track for next year.
With respect to the cross-lingual version of Conference, the MultiFarm track still attracts a few number of participants implementing specific strategies to deal with ontologies having a terminological layer in different natural languages. Despite this fact, this year new participants came with alternative strategies (i.e, deep learning) with respect to the last campaigns.
The consensus-based evaluation in the Disease and Phenotype track offers limited insights into performance, as several matching systems produce a number of unique correspondences which may or may not be correct. In the absence of a true reference alignment, future evaluation should seek to determine whether the unique correspondences contain indicators of correctness, such as semantic similarity, or appear to be noise. Comparison of the task results with embedded mappings of equivalence in the MONDO disease ontology can also be investigated in future evaluation [55].
Despite the quite promising results obtained by matching systems for the Biodiversity and Ecology track, the most important observation is that none of the systems has been able to detect mappings established by domain experts. Detecting such correspondences requires the use of domain-specific core knowledge that captures biodiversity concepts. In addition this year, we put the light on the quasi total incapacity of systems to handle SKOS as input format for semantic resources to align.
The interactive matching track also witnessed a small number of participants. Three systems participated this year. This is puzzling considering that this track is based on the Anatomy and Conference test cases, and those tracks had 13 participants. The process of programmatically querying the Oracle class used to simulate user interactions is simple enough that it should not be a deterrent for participation, but perhaps we should look at facilitating the process further in future OAEI editions by providing implementation examples.
The complex matching track opens new perspectives in the field of ontology matching. Tackling complex matching automatically is extremely challenging, likely requiring profound adaptations from matching systems, so the fact that there were three participants that were able to generate complex correspondences in this track should be seen as a positive sign of progress to the state of the art in ontology matching. This year automatic evaluation has been introduced following an instance-based comparison approach.
In the instance matching tracks participation increased this year for SPIMBENCH as systems became more familiar with the HOBBIT platform and had more time to do the migration. Regarding Spatial benchmark, the systems didn’t have newer versions and the number of participants remained the same. Thus, the benchmark and the systems were the exact same as last year. Participation might increase next year as the systems are still updating their versions and new systems are under development. Automatic instance-matching benchmark generation algorithms have been gaining popularity, as evidenced by the fact that they are used in all three instance matching tracks of this OAEI edition. One aspect that has not been addressed in such algorithms is that, if the transformation is too extreme, the correspondence may be unrealistic and impossible to detect even by humans. As such, we argue that human-in-the-loop techniques can be exploited to do a preventive quality-checking of generated correspondences, and refine the set of correspondences included in the final reference alignment.
In the knowledge graph track, more matchers are able to match rdf:Properties and are thus better suited for real matching cases. In the third year of this track we saw a small improvement in instance alignments but the margin to the baselines is still small. In this year two new systems focused on the KG track but could not submit their systems in time. We thus expect more systems in the upcoming year.
Like in previous OAEI editions, most participants provided a description of their systems and their experience in the evaluation, in the form of OAEI system papers. These papers, like the present one, have not been peer reviewed. However, they are full contributions to this evaluation exercise, reflecting the effort and insight of matching systems developers, and providing details about those systems and the algorithms they implement.
As each year, fruitful discussions at the Ontology Matching point out different directions for future improvements in OAEI. In particular, in terms of new use cases, one potential new track involves matching ontologies of units of measure (OM and QUDT) [51], in order to improve the ability of a digital twin platform to harmonise, integrate and process quantity values. Another track to be included in the next campaign is about the chemical/biological laboratory domain with strong interest from pharmaceutical companies [30, 32].
The Ontology Alignment Evaluation Initiative will strive to remain a reference to the ontology matching community by improving both the test cases and the testing methodology to better reflect actual needs, as well as to promote progress in this field. More information can be found at: http://oaei.ontologymatching.org.
We warmly thank the participants of this campaign. We know that they have worked hard to have their matching tools executable in time and they provided useful reports on their experience. The best way to learn about the results remains to read the papers that follow.
We are also grateful to Martin Ringwald and Terry Hayamizu for providing the reference alignment for the anatomy ontologies and thank Elena Beisswanger for her thorough support on improving the quality of the dataset.
We thank Andrea Turbati and the AGROVOC team for their very appreciated help with the preparation of the AGROVOC subset ontology. We are also grateful to Catherine Roussey and Nathalie Hernandez for their help on the Taxon alignment.
We also thank for their support the past members of the Ontology Alignment Evaluation Initiative steering committee: Je´roˆme Euzenat (INRIA, FR), Yannis Kalfoglou (Ricoh laboratories, UK), Miklos Nagy (The Open University,UK), Natasha Noy (Google Inc., USA), Yuzhong Qu (Southeast University, CN), York Sure (Leibniz Gemeinschaft, DE), Jie Tang (Tsinghua University, CN), Heiner Stuckenschmidt (Mannheim Universita¨t, DE), George Vouros (University of the Aegean, GR).
Daniel Faria was supported by the EC H2020 grant 676559 ELIXIREXCELERATE and the Portuguese FCT Grant 22231 BioData.pt, co-financed by FEDER.
Ernesto Jimenez-Ruiz has been partially supported by the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project no.: 237889) and the AIDA project (Alan Turing Institute).
Catia Pesquita was supported by the FCT through the LASIGE Strategic Project (UID/CEC/00408/2013) and the research grant PTDC/EEI-ESS/4633/2014.
Irini Fundulaki and Tzanina Saveta were supported by the EU’s Horizon 2020 research and innovation programme under grant agreement No 688227 (Hobbit).
Jana Vatasˇcˇinova´ and Ondrˇej Zamazal were supported by the CSF grant no. 1823964S.
Patrick Lambrix, Huanyu Li, Mina Abd Nikooie Pour and Ying Li have been supported by the Swedish e-Science Research Centre (SeRC), the Swedish Research Council (Vetenskapsra˚det, dnr 2018-04147) and the Swedish National Graduate School in Computer Science (CUGS).
Lu Zhou and Pascal Hitzler have been supported by the National Science Foundation under Grant No. 2033521, KnowWhereGraph: Enriching and Linking CrossDomain Knowledge Graphs using Spatially-Explicit AI Technologies and the Andrew W. Mellon Foundation through the Enslaved project (identifiers 1708-04732 and 190206575).
Beyza Yaman has been supported by the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 801522, by Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology [grant number 13/RC/2106] and Ordnance Survey Ireland.
The Biodiversity and Ecology track has been partially funded by the German Research Foundation in the context of the GFBio Project (grant No. SE 553/7-1) and the CRC 1076 AquaDiva, the Leitprojekt der Fraunhofer Gesellschaft in the context of the MED2ICIN project (grant No. 600628) and the German Network for Bioinformatics Infrastructure - de.NBI (grant No. 031A539B). In 2020, the track was also supported by the Data to Knowledge in Agronomy and Biodiversity (D2KAB – www.d2kab.org) project that received funding from the French National Research Agency (ANR-18CE23-0017). We would like to thank FAO AIMS and US NAL as well as the GACS project for providing mappings betweem AGROVOC and NALT. We would like to thank Christian Pichot and the ANAEE France project for providing mappings betweem ANAEETHES and GEMET. 5. Alsayed Algergawy, Daniel Faria, Alfio Ferrara, Irini Fundulaki, Ian Harrow, Sven Hertling, Ernesto Jime´nez-Ruiz, Naouel Karam, Abderrahmane Khiat, Patrick Lambrix, Huanyu Li, Stefano Montanelli, Heiko Paulheim, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko, Andrea Splendiani, E´lodie Thie´blin, Ca´ssia Trojahn, Jana Vatascinova´, Ondrej Zamazal, and Lu Zhou. Results of the ontology alignment evaluation initiative 2019. In Proceedings of the 14th International Workshop on Ontology Matching, Auckland, New Zealand, pages 46–85, 2019. 6. R Amini, L Zhou, and P Hitzler. Geolink cruises: A non-synthetic benchmark for coreference resolution on knowledge graphs. In 29th ACM International Conference on Information and Knowledge Management, 2020. 7. Benhamin Ashpole, Marc Ehrig, Je´r oˆme Euzenat, and Heiner Stuckenschmidt, editors. Proc.
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