Ontology Embedding

Ontology Embedding is a technique that maps concepts and relationships from an ontology structure into a low-dimensional vector space, aiming to capture semantic information through numerical representation to enhance the understanding and reasoning capabilities of machine learning models regarding complex knowledge structures. The goal of this technology is to achieve efficient and scalable knowledge representation, improving model performance in areas such as natural language processing, knowledge graph construction and querying, and promoting the development of intelligent applications.