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An original model for multi-target learning of logical rules for knowledge graph reasoning

arXiv 2112.06189 ↗First posted 12 Dec 2021cs.AI cs.LGcited by 0

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Abstract (arXiv)

Large-scale knowledge graphs provide structured representations of human knowledge. However, as it is impossible to collect all knowledge, knowledge graphs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logical rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logical rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model able to fully use training data which also considers multi-target scenarios. In addition, considering the deficiency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our model outperforms state-of-the-art methods on five benchmark datasets. The results also prove the effectiveness of the indicators.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 62 references that are in the map. First posted on arXiv 12 Dec 2021.

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References · 25

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  4. 2016Complex Embeddings for Simple Link Predictioncited by 897
  5. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  6. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  7. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  8. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  9. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  10. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  11. 2019KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoningcited by 216
  12. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 173
13 more
  1. 2020RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphscited by 117
  2. 2019Reinforcement Knowledge Graph Reasoning for Explainable Recommendationcited by 102
  3. 2019Probabilistic Logic Neural Networks for Reasoningcited by 69
  4. 2019Cognitive Graph for Multi-Hop Reading Comprehension at Scalecited by 65
  5. 2019Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoningcited by 63
  6. 2015Traversing Knowledge Graphs in Vector Spacecited by 51
  7. 2020Efficient Probabilistic Logic Reasoning with Graph Neural Networkscited by 50
  8. 2019Learn to Explain Efficiently via Neural Logic Inductive Learningcited by 38
  9. 2021Modeling Fine-Grained Entity Types with Box Embeddingscited by 36
  10. 2016TensorLog: A Differentiable Deductive Databasecited by 29
  11. 2021QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completioncited by 10
  12. 2013Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logiccited by 7
  13. 2019Logic Rules Powered Knowledge Graph Embeddingcited by 7

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