HEURISTIC-DRIVEN, TYPE-SPECIFIC EMBEDDING IN PARALLEL SPACES FOR ENHANCING KNOWLEDGE GRAPH REASONING

Knowledge Graph Reasoning aims to derive new insights from existing Knowledge Graphs (KGs) and address any missing or incomplete data. Existing models primarily rely on explicit information while neglecting the implicit constraints imposed by entity types on relations types. For example, when the entity type is "person-person," the relation type should be constrained to interpersonal connections like "co-worker." Based on this perspective, we introduce a priori knowledge-based approach for inferring relations types. This approach utilizes the relations type distribution across different entity types in the dataset to guide the inference process. Additionally, recognizing that mapping all different relations types to a single space can decrease inference accuracy due to the diversity of semantics, we propose a parallel spaces KG embedding model that partitions the entire KG into multiple subspaces. Each subspace is dedicated to learning information associated with a specific relation type. Experimental results on three KG reasoning benchmarks demonstrate that our model outperforms other baselines in accuracy. Importantly, our model shows significant advantages when applied to datasets with a substantial number of relations.