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Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task

arXiv 2405.09477 ↗First posted 15 May 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the rapidly growing literature, insufficient attention has been paid to the cooperation between humans and AI on KG. However, humans' capability to analyze graphs conceptually may further improve the efficacy of KGE models with semantic information. To this effect, we carefully designed a human-AI team (HAIT) system dubbed KG-HAIT, which harnesses the human insights on KG by leveraging fully human-designed ad-hoc dynamic programming (DP) on KG to produce human insightful feature (HIF) vectors that capture the subgraph structural feature and semantic similarities. By integrating HIF vectors into the training of KGE models, notable improvements are observed across various benchmarks and metrics, accompanied by accelerated model convergence. Our results underscore the effectiveness of human-designed DP in the task of LP, emphasizing the pivotal role of collaboration between humans and AI on KG. We open avenues for further exploration and innovation through KG-HAIT, paving the way towards more effective and insightful KG analysis techniques.

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References · 16

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  4. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  5. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  6. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  7. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  8. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  9. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 259
  10. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 194
  11. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  12. 2020Knowledge Graph Embedding for Link Prediction: A Comparative Analysiscited by 85
4 more
  1. 2022Graph Neural Networks are Dynamic Programmerscited by 55
  2. 2023Maximum Independent Set: Self-Training through Dynamic Programmingcited by 5
  3. 2021Human in the Loop for Machine Creativitycited by 4
  4. 2023MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Modelscited by 4

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