SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs

Understand

Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and are a crucial component in many AI systems.

• There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query.
• Embedding-based methods solve both tasks by first computing an embedding for each entity and relation, then using them to form predictions.
• However, existing scalable KG embedding frameworks only support single-hop knowledge graph completion and cannot be applied to the more challenging multi-hop reasoning task.