RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
This addresses knowledge graph completion for AI applications, but it is incremental as it builds on existing neighbor aggregation methods.
The paper tackles the problem of weak connections and lack of parameter sharing in knowledge graph representation learning by proposing RMNA, a model that uses rule mining to transform valuable multi-hop neighbors into one-hop neighbors for aggregation, achieving competitive performance compared to state-of-the-art models.
Although the state-of-the-art traditional representation learning (TRL) models show competitive performance on knowledge graph completion, there is no parameter sharing between the embeddings of entities, and the connections between entities are weak. Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings. However, existing NARL models either only utilize one-hop neighbors, ignoring the information in multi-hop neighbors, or utilize multi-hop neighbors by hierarchical neighbor aggregation, destroying the completeness of multi-hop neighbors. In this paper, we propose a NARL model named RMNA, which obtains and filters horn rules through a rule mining algorithm, and uses selected horn rules to transform valuable multi-hop neighbors into one-hop neighbors, therefore, the information in valuable multi-hop neighbors can be completely utilized by aggregating these one-hop neighbors. In experiments, we compare RMNA with the state-of-the-art TRL models and NARL models. The results show that RMNA has a competitive performance.