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AIKT-Knowledge_Graph

  • 表示学习方法
    • 基于事实的表示学习方法
      • 基于距离的评分函数：翻译模型
        • TransE及其拓展方法
          • TransE: Translating embeddings for modeling multi-relational data.
          • TransH: Knowledge graph embedding by translating on hyperplanes.
          • TransR: Learning entity and relation embeddings for knowledge graph completion.
          • TransD, TransSparse, TransM, ManifoldE, TransF, TransA
        • 高斯分布表示向量
          • KG2E: Learning to represent knowledge graphs with gaussian embedding.
      • 基于相似度的评分函数：语义匹配模型
        • RESCAL及其拓展方法
          • RESCAL: A three-way model for collective learning on multi-relational data.
          • HolE: Holographic embeddings of knowledge graphs.
          • DisMult: Embedding entities and relations for learning and inference in knowledge bases.
          • ComplEx: Complex embeddings for simple link prediction.
        • 基于神经网络的语义匹配模型
          • SME: A semantic matching energy function for learning with multi-relational data.
          • NAM: Probabilistic reasoning via deep learning: Neural association models.
      • 模型训练方法
        • 根据开放世界假设进行训练
        • 根据封闭世界假设进行训练
    • 融合多源信息的表示学习方法
      • 实体类型
        • SSE: Semantically smooth knowledge graph embedding.
        • TKRL: Representation learning of knowledge graphs with entity descriptions.
      • 关系路径
        • Modeling relation paths for representation learning of knowledge bases.
      • 逻辑规则
        • Knowledge base completion using embeddings and rules.
        • Jointly embedding knowledge graphs and logical rules.
      • 其它多源信息：文本描述信息, 实体属性, 图结构等
  • 知识图谱应用
    • 链路预测
      • Modeling relation paths for representation learning of knowledge bases.
    • 三元组分类
      • Learning entity and relation embeddings for knowledge graph completion.
    • 实体识别
      • A semantic matching energy function for learning with multi-relational data.
      • A three-way model for collective learning on multi-relational data.
    • 关系抽取
      • Relation extraction with matrix factorization and universal schemas.
      • Connecting language and knowledge bases with embedding models for relation extraction.
    • 自动问答
    • 推荐系统