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An Interpretable Knowledge Transfer Model for Knowledge Base Completion

arXiv 1704.05908 ↗First posted 19 Apr 2017cs.CL cs.AI cs.LGcited by 17

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Abstract (arXiv)

Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets---WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2017.

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Citing papers per year: 2021: 4; 2022: 4; 2023: 2; 2024: 5; 2025: 1; 2026: 1.

17 papers.

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  1. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  2. 2021Explainable AI: current status and future directionscited by 32
  3. 2021A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learningcited by 31
  4. 2021Is Sparse Attention more Interpretable?cited by 31
  5. 2021Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution Viewcited by 15
  6. 2023Knowledge Graph Embedding: An Overviewcited by 10
  7. 2024Negative Sampling in Knowledge Graph Representation Learning: A Reviewcited by 9
  8. 2024Comprehensible Artificial Intelligence on Knowledge Graphs: A surveycited by 8
  9. 2022TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representationcited by 7
  10. 2022Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actionscited by 5
  11. 2023Accelerating the Global Aggregation of Local Explanationscited by 4
  12. 2025Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Aheadcited by 4
5 more
  1. 2022Towards Explaining Subjective Ground of Individuals on Social Mediacited by 2
  2. 2024Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Surveycited by 1
  3. 2024Explaining Graph Neural Networks for Node Similarity on Graphscited by 0
  4. 2024Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Modelscited by 0
  5. 2026Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapescited by 0