Nanopublications LDF server

Matches in Nanopublications for { ?s ?p ?o <http://purl.org/np/RAxoQEFSMjgX8RdtKySC2IHo27UAb773Z797udcTkxc44#assertion>. }

• dataset-label type LiteralPlaceholder assertion.
• algorithm type GuidedChoicePlaceholder assertion.
• assertion type AssertionTemplate assertion.
• dataset type UriPlaceholder assertion.
• evaluation type LocalResource assertion.
• evaluation-measure type GuidedChoicePlaceholder assertion.
• evaluation-value type LiteralPlaceholder assertion.
• run type LocalResource assertion.
• st60 type OptionalStatement assertion.
• task type GuidedChoicePlaceholder assertion.
• type label "is a" assertion.
• label label "has the name" assertion.
• Run label "run of a Machine Learning experiment" assertion.
• hasOutput label "has as output" assertion.
• hasValue label "has the result value" assertion.
• realizes label "executes an implementation that implements the algorithm" assertion.
• specifiedBy label "measures" assertion.
• hasInput label "uses as input" assertion.
• dataset-label label "the name of the dataset" assertion.
• algorithm label "the used algorithm (e.g. Random Forest)" assertion.
• assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
• dataset label "the URL of the used dataset" assertion.
• evaluation label "an evaluation" assertion.
• evaluation-measure label "the evaluation measure (e.g. Accuracy)" assertion.
• evaluation-value label "value of the evaluation result (e.g. 0.81 if the accuracy is 81%)" assertion.
• run label "this run" assertion.
• task label "the task performed (e.g. Spam Detection)" assertion.
• STATO_0000415 label "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." assertion.
• STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
• st10 subject run assertion.
• st20 subject run assertion.
• st30 subject run assertion.
• st40 subject run assertion.
• st50 subject dataset assertion.
• st60 subject run assertion.
• st70 subject evaluation assertion.
• st80 subject evaluation assertion.
• st10 predicate type assertion.
• st20 predicate realizes assertion.
• st30 predicate achieves assertion.
• st40 predicate hasInput assertion.
• st50 predicate label assertion.
• st60 predicate hasOutput assertion.
• st70 predicate specifiedBy assertion.
• st80 predicate hasValue assertion.
• st10 object Run assertion.
• st20 object algorithm assertion.
• st30 object task assertion.
• st40 object dataset assertion.
• st50 object dataset-label assertion.
• st60 object evaluation assertion.
• st70 object evaluation-measure assertion.
• st80 object evaluation-value assertion.
• assertion hasStatement st10 assertion.
• assertion hasStatement st20 assertion.
• assertion hasStatement st30 assertion.
• assertion hasStatement st40 assertion.
• assertion hasStatement st50 assertion.
• assertion hasStatement st60 assertion.
• assertion hasStatement st70 assertion.
• assertion hasStatement st80 assertion.
• assertion description "Such a nanopublication describes the results of a Machine Learning experiment, such as the accuracy achieved by a applying a given ML technique on a given dataset. This template is based on <a href="https://ml-schema.github.io/documentation/ML%20Schema.html">ML Schema</a>." assertion.
• evaluation-measure possibleValue STATO_0000415 assertion.
• evaluation-measure possibleValue STATO_0000416 assertion.
• algorithm possibleValuesFromApi "https://www.wikidata.org/w/api.php?action=wbsearchentities&language=en&format=json&limit=5&search=" assertion.
• algorithm possibleValuesFromApi "https://www.ebi.ac.uk/ols/api/select?fieldList=iri,label,description&q=" assertion.
• algorithm possibleValuesFromApi "http://purl.org/nanopub/api/find_signed_things?type=http%3A%2F%2Fwww.w3.org%2Fns%2Fmls%23Algorithm&searchterm=" assertion.
• evaluation-measure possibleValuesFromApi "https://www.ebi.ac.uk/ols/api/select?ontology=stato&fieldList=iri,label,description&q=" assertion.
• evaluation-measure possibleValuesFromApi "http://purl.org/nanopub/api/find_signed_things?type=http%3A%2F%2Fwww.w3.org%2Fns%2Fmls%23EvaluationMeasure&searchterm=" assertion.
• task possibleValuesFromApi "https://www.wikidata.org/w/api.php?action=wbsearchentities&language=en&format=json&limit=5&search=" assertion.
• task possibleValuesFromApi "https://www.ebi.ac.uk/ols/api/select?fieldList=iri,label,description&q=" assertion.
• task possibleValuesFromApi "http://purl.org/nanopub/api/find_signed_things?type=http%3A%2F%2Fwww.w3.org%2Fns%2Fmls%23Task&searchterm=" assertion.
• assertion hasTag "Machine Learning" assertion.