RDF2Vec-based Classification of Ontology Alignment Changes
Table 2: Classification Results
['[EMPTY]', '[ITALIC] f1-measure', '[ITALIC] accuracy', '[ITALIC] precision', '[ITALIC] recall']
Only changes close to the alignment were included in this evaluation, since it would otherwise be very easy to achieve accuracy values above 95%. Results for MLP did not vary based on the structure of the hidden layers, so one row represents all MLP results. All algorithms show a very similar performance regarding the evaluated metrics. The highest achieved precision is 0.81, which can be reached using MLP and linear SVM classification. These methods also reach the highest f1-measures of 0.75. Accuracy of all classifiers is only marginally higher than what can be achieved using random guessing, given the distribution of classes in the test set.