Benchmarking Class Expression Learning

Class Expression Learning is a challenging but important task. Given sets of positive and negative examples, the goal is to generate a class expression that includes as many positive examples as possible but excludes the negative examples. However, the search for such a class expression happens in an infinite space and, hence, can become quite challenging. A classic approach to this problem is a loop in which the currently best known expression is chosen and further refined using a refinement operator. The image below shows such a tree of refinements. The green nodes are the ones that are chosen for a further refinement.

However, the benchmarks that exist to evaluate the performance of Class Expression Learning algorithms are quite small. The goal of this thesis is to come up with an approach to create a larger benchmark. This benchmark should use a knowledge graph of the size of Wikidata or DBpedia. positive and negative examples could be chosen based on Yago or Wikipedia categories.