Bio-ML: A ML-friendly Biomedical track for Equivalence and Subsumption Matching.
This track presents an unified evaluation framework suitable for both ML-based and non-ML-based OM systems.
The goal of the Digital Humanities track is to evaluate matching system performance
when dealing with small datasets in different languages and specialist terms from archaeology,
cultural history and the interlink of DH and computer science.
replacement is to use AddNegativesViaMatcher which has the same functionality
(but it really uses only correspondences with equivalence relation as positive correspondences) .
This filter extracts the corresponding text for a resource (with the specified and customizable extractor) given all correspondences in the input alignment.
public static class TrackRepository.Archaeology
+extends Object
+
The multilingual archaeology track is newly established for OAEI 2024.
+ It aims at the alignment of multilingual domain specific vocabularies.
+ The high quality reference is compiled manually, ensuring semantic, lexical and part-of-speech similarity.
+ It offers different test cases from the archaeology domain.
+ Each source and target ontology is monolingual in one of the following languages:
+ English, German, French, Italian. The goal is to evaluate the matching system perfomance when
+ dealing with small datasets in different languages and specialist terms.
Bio-ML: A ML-friendly Biomedical track for Equivalence and Subsumption Matching.
- This track presents an unified evaluation framework suitable for both ML-based and non-ML-based OM systems.
The Circular Economy track is about matching relevant Circular Economy ontologies.
+
Bio-ML: A ML-friendly Biomedical track for Equivalence and Subsumption Matching.
+ This track presents an unified evaluation framework suitable for both ML-based and non-ML-based OM systems.
The goal of the Digital Humanities track is to evaluate matching system performance
- when dealing with small datasets in different languages and specialist terms from archaeology,
- cultural history and the interlink of DH and computer science.
The goal of the Digital Humanities track is to evaluate matching system performance
+ when dealing with small datasets in different languages and specialist terms from archaeology,
+ cultural history and the interlink of DH and computer science.
The hydrography dataset is composed of four source ontologies (Hydro3, HydrOntology_native, HydrOntology_translated, and Cree) that each should be aligned to a single target Surface Water Ontology (SWO).
The hydrography dataset is composed of four source ontologies (Hydro3, HydrOntology_native, HydrOntology_translated, and Cree) that each should be aligned to a single target Surface Water Ontology (SWO).
This track consists of finding alignments between food concepts
- from CIQUAL, the French food nutritional composition database, and food concepts from SIREN.
+
Conference Testsuite V1 which is used all the time.
This track consists of finding alignments between food concepts
+ from CIQUAL, the French food nutritional composition database, and food concepts from SIREN.
This track consists of finding alignments between food concepts
- from CIQUAL, the French food nutritional composition database, and food concepts from SIREN.
+
Conference Testsuite V1 with all test cases ( even without reference alignment
The Knowledge Graph Track contains nine isolated knowledge graphs with instance and schema data.
+
This track consists of finding alignments between food concepts
+ from CIQUAL, the French food nutritional composition database, and food concepts from SIREN.
This track consists of finding alignments between food concepts
- from CIQUAL, the French food nutritional composition database, and food concepts from SIREN.
The Knowledge Graph Track contains isolated knowledge graphs with instance and schema data.
+
This track consists of finding alignments between food concepts
+ from CIQUAL, the French food nutritional composition database, and food concepts from SIREN.
Bio-ML: A ML-friendly Biomedical track for Equivalence and Subsumption Matching.
This track presents an unified evaluation framework suitable for both ML-based and non-ML-based OM systems.
The goal of the Digital Humanities track is to evaluate matching system performance
when dealing with small datasets in different languages and specialist terms from archaeology,
cultural history and the interlink of DH and computer science.
AddNegativesViaMatcher
which has the same functionality (but it really uses only correspondences with equivalence relation as positive correspondences) .