before_revision: Discovering precise and specific rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics. In this paper, we provide a fundamental theory for knowledge graph reasoning based on  ending anchored rules. Our theory provides precise reasons answering why or why not a triple is correct. Then, we implement our theory by what we called the EARDict model. Results show that the EARDict model achieves new state-of-the-art performances on benchmark knowledge graph completion tasks, including a Hits@10 score of 80.38 percent on WN18RR.
after_revision: Discovering precise and specific rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics. In this paper, we provide a fundamental theory for knowledge graph reasoning based on the ending anchored rules. Our theory provides precise reasons explaining why or why not a triple is correct. Then, we implement our theory by what we call the EARDict model. Results show that our EARDict model significantly outperforms all the benchmark models on two large datasets of knowledge graph completion , including achieving a Hits@10 score of 96.6 percent on WN18RR.
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