18 résultats avec le mot-clé: 'aml results for oaei'
The adaptations made for the evaluation were: the preprocessing of cross-references from Uberon and DOID for use in the Anatomy and Large Biomedical Ontologies tracks, due to
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The AML workflow for the OAEI 2015 is the same as last year, comprising the nine steps shown in Figure 1: ontology loading and profiling, translation, baseline match- ing,
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AML was the only system to participate in all tracks this year, and was either the best performing or among the top performing systems in nearly all tasks, including the new
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In OAEI 2019, AML’s development focused mainly on expanding its range of complex matching algorithms, but there were also improvements on its instance match- ing pipeline and
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In OAEI 2020, AML’s development focused mainly on expanding its range of complex matching algorithms, but there were also im- provements on its instance matching pipeline and on
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We were also unable to fully integrate the code for complex matching with the main AML code-base before the OAEI deadline, and thus participated in the Complex Match- ing track using
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This consequence of quasiregularity is usually proved via the funda- mental fact that quasiregular mappings are free extremals for certain variational integrals;
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Creemos que el hecho de presentar de esta manera a los grandes nombres literarios podría hacer que los alumnos se interesaran por ellos y que por sí mismos se acercaran a sus obras,
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During our first participation in the OAEI cam- paign, POMap succeeded to be one of the top three performing systems in the Anatomy track.. In the remaining of this paper, we
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Extensional matcher In many situation, to-be-matched ontologies provide data (in- stances), therefore, the aim of Extensional module is to discover new mappings which are complement
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Especially in the Anatomy and Large Biomedical Ontologies tracks GOMMA’s techniques such as composition- based matching, parallel matching and blocking showed to be valuable for
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In test 202, although the identifiers of the entities were replaced by random strings and their labels and comments suppressed, ASMOV was still able to leverage other
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Zhishi.links we proposed here is an efficient and flexible instance matching system, which utilizes distributed framework to index and process semantic resources, and uses
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The similar neighbors filter uses the instance alignment (generated by the previous string processing step) to count for each instance correspondence how many resources or literals
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The interactions among different textual information are taken into considera- tion, which allows instances to be fine-grained divided. It also has the ability to infer indexes
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SANOM is also competitive with LogMap on the anatomy track, which is the best performing system in this track with no usage of particular biomedical background knowledge.. SANOM
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Some parameters are tuned and set in the experiments. We also define MF factor threshold as 0.9 to determine whether use WordNet based strategy instead of
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ASE (Aligning Smart Entities) is an automated ontology alignment tool based on AUTOMSv2 tool (http://ai-lab-webserver.aegean.gr/kotis/AUTOMSv2), a baseline tool we have
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