mlsanityData to 14 Oct 2026

Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence

arXiv 2211.00914 ↗First posted 2 Nov 2022cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms. The reasoning method based on path features is widely used in the field of knowledge graph reasoning and completion on account of that its have strong interpretability. However, reasoning methods based on path features still have several problems in the following aspects: Path search isinefficient, insufficient paths for sparse tasks and some paths are not helpful for reasoning tasks. In order to solve the above problems, this paper proposes a method called DC-Path that combines dynamic relation confidence and other indicators to evaluate path features, and then guide path search, finally conduct relation reasoning. Experimental result show that compared with the existing relation reasoning algorithm, this method can select the most representative features in the current reasoning task from the knowledge graph and achieve better performance on the current relation reasoning task.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

18 of 33 references.

References · 18

  1. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  2. 2016Complex Embeddings for Simple Link Predictioncited by 902
  3. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  4. 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
  5. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  6. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  7. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 266
  8. 2019Quaternion Knowledge Graph Embeddingscited by 175
  9. 2017Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learningcited by 161
  10. 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
  11. 2018Multi-Hop Knowledge Graph Reasoning with Reward Shapingcited by 102
  12. 2017Analogical Inference for Multi-Relational Embeddingscited by 69
6 more
  1. 2017Knowledge Graph Embedding with Iterative Guidance from Soft Rulescited by 51
  2. 2019Interaction Embeddings for Prediction and Explanation in Knowledge Graphscited by 40
  3. 2018Hypernetwork Knowledge Graph Embeddingscited by 38
  4. 2018M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Searchcited by 34
  5. 2016Probabilistic Reasoning via Deep Learning: Neural Association Modelscited by 20
  6. 2019Collaborative Policy Learning for Open Knowledge Graph Reasoningcited by 18

Built on by

Not cited yet.