GreenKGC: A Lightweight Knowledge Graph Completion Method

A (head entity, relation, tail entity) factual triple, denoted by (h, r, t), is a basic component in KGs. In many knowledge-centric artificial intelligence (AI) applications, such as question answering [Huang et al., 2019, Saxena et al., 2020], information extraction [Hoffmann et al., 2011, Daiber et al., 2013], and recommendation [Wang et al., 2019, Xian et al., 2019], KG plays an important role as it provides explainable reasoning paths to predictions. However, most KGs suffer from the incompleteness problem; namely, a large number of factual triples are missing, leading to performance degradation in downstream applications. Thus, there is growing interest in developing KG completion (KGC) methods to solve the incompleteness problem by inferring undiscovered factual triples based on existing ones. Knowledge graph embedding (KGE) methods have been widely used to solve the incompleteness problem. Embeddings for entities and relations are stored as model parameters and updated by maximizing triple scores among observed triples while minimizing those among negative triples. The number of free parameters in a KGE model is linear to the embedding dimension and the number of entities and relations in KGs, i.e. O((|E| + |R|)d), where |E| is the number of entities, |R| is the number of relations, and d is the embedding dimension. Since KGE models usually require a higher-dimensional embedding space for a better reasoning capability, they require large model sizes (i.e.