Towards Semantically Enriched Embeddings for Knowledge Graph Completion
It addresses the problem of enhancing knowledge graph completion for researchers and practitioners by proposing to leverage semantics from large language models, but it is incremental as it primarily surveys and suggests improvements rather than presenting new results.
This vision paper reviews existing algorithms for knowledge graph completion, focusing on embedding-based methods and their limitations in capturing semantic information, and suggests future directions by integrating large language models and description logic axioms.
Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. In a separate development, a vast amount of information has been captured within the Large Language Models (LLMs) which has revolutionized the field of Artificial Intelligence. KGs could benefit from these LLMs and vice versa. This vision paper discusses the existing algorithms for KG completion based on the variations for generating KG embeddings. It starts with discussing various KG completion algorithms such as transductive and inductive link prediction and entity type prediction algorithms. It then moves on to the algorithms utilizing type information within the KGs, LLMs, and finally to algorithms capturing the semantics represented in different description logic axioms. We conclude the paper with a critical reflection on the current state of work in the community and give recommendations for future directions.