Learning problems of 5 QA systems on QALD10, QALD9+DB, and QALD9+WK

This project provides learning problems of 5 QA Systems on QALD10, QALD9+DB, and QALD9+WK in JSON format. Each one is the performance of a QA system and the positive and negative examples represent questions that the systems answered correctly (F1 >= 0.5) or incorrectly (F1 < 0.5), respectively.The following JSON snippet shows an example of a learning problem for the MST5 on QALD10 dataset: { "problems": { "MST5_QALD10": { "positive_examples": [ "https://github.com/KGQA/QALD-10/blob/main/data/qald_10/qald_10.json#Q0", ... ], "negative_examples": [ "https://github.com/KGQA/QALD-10/blob/main/data/qald_10/qald_10.json#Q1", ... ] } }}Three knowledge graphs built for QALD 9 plus DBpedia, QALD 9 plus Wikidata and QALD10 can be found here.The three knowledge graphs are used to solve the learning problems using concept learner provided by ontolearn.