Knowledge graph representation learning via generated descriptions

Knowledge graph representation learning (KGRL) aims to project the entities and relations into a continuous low-dimensional knowledge graph space to be used for knowledge graph completion and detecting new triples. Using textual descriptions for entity representation learning has been a key topic. However, the current work has two major constraints: (1) some entities do not have any associated descriptions; (2) the associated descriptions are usually phrases, and they do not contain enough information. This paper presents a novel KGRL method for learning effective embeddings by generating meaningful descriptive sentences from entities’ connections. The experiments using four public datasets and a new proposed dataset show that the New Description-Embodied Knowledge Graph Embedding (NDKGE for short) approach introduced in this paper outperforms most of the existing work in the task of link prediction.