Supervisors

Suitable for

Abstract

Ontology and its counterparts such as logical constraints play a key role in managing the quality of knowledge graphs (KGs) such as Wikidata and DBpedia. KG embedding is widely investigated due to its usability in KG completion and other KG predictive analytics. However the current semantic embedding methods cannot fully utilize the ontology and its logical constraints, and thus the learned sub-symbolic knowledge is incomplete. This project aims to inject the knowledge expressed by an ontological schema into KG embeddings.

"Research question: How to represent the logical relationship of an ontology in the embedding space? How to jointly learn the embedding of the KG and its ontology?

Expection: embedding algorithms for KG and its ontology."

Desirable: Experience with satellite imagery, exposure in basic group theory.

Prerequisites: 1. Chen, Jiaoyan, et al. ""OWL2Vec*: Embedding of OWL Ontologies."" arXiv preprint arXiv:2009.14654 (2020). 2. Zhang, Wen, et al. ""Iteratively learning embeddings and rules for knowledge graph reasoning."" The World Wide Web Conference. 2019. 3. Hao, Junheng, et al. ""Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts."" KDD 2019. 4. Chekol, Melisachew Wudage, and Giuseppe Pirrò. ""Refining Node Embeddings via Semantic Proximity."" ISWC 2020."