  Wikipedia articles are hierarchically organized through categories and lists,
providing one of the most comprehensive and universal taxonomy, but its open
creation is causing redundancies and inconsistencies. Assigning DBPedia classes
to Wikipedia categories and lists can alleviate the problem, realizing a large
knowledge graph which is essential for categorizing digital contents through
entity linking and typing. However, the existing approach of CaLiGraph is
producing incomplete and non-fine grained mappings. In this paper, we tackle
the problem as ontology alignment, where structural information of knowledge
graphs and lexical and semantic features of ontology class names are utilized
to discover confident mappings, which are in turn utilized for finetuing
pretrained language models in a distant supervision fashion. Our method SLHCat
consists of two main parts: 1) Automatically generating training data by
leveraging knowledge graph structure, semantic similarities, and named entity
typing. 2) Finetuning and prompt-tuning of the pre-trained language model BERT
are carried out over the training data, to capture semantic and syntactic
properties of class names. Our model SLHCat is evaluated over a benchmark
dataset constructed by annotating 3000 fine-grained CaLiGraph-DBpedia mapping
pairs. SLHCat is outperforming the baseline model by a large margin of 25% in
accuracy, offering a practical solution for large-scale ontology mapping.
