论文标题
从知识图同时学习嵌入和逻辑规则的混合模型
A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs
论文作者
论文摘要
知识图（KG）推理的问题已被基于规则的系统广泛探索，并且最近通过知识图嵌入方法进行了广泛的探索。尽管逻辑规则可以捕获公园中的确定性行为，但它们是脆弱的，而推断事实超出已知kg的矿体是具有挑战性的。概率嵌入方法可有效捕获全局软统计趋势，并且与它们的推理是有效的。尽管从丰富的培训数据中学到的嵌入表示形式具有表现力，但现实世界中的不完整和稀疏性会影响其有效性。我们旨在利用两种方法的互补特性来开发同时学习高质量规则和嵌入的混合模型。我们的方法使用交叉反馈范式，其中使用嵌入模型来指导对规则挖掘系统进行搜索以挖掘规则并推断新事实。对这些新事实进行了采样，并进一步用于完善嵌入模型。多个基准数据集的实验显示了我们方法比其他竞争性独立和混合基准的有效性。我们还在稀疏的KG设置中显示了其功效，并最终探索了负面抽样的联系。
The problem of knowledge graph (KG) reasoning has been widely explored by traditional rule-based systems and more recently by knowledge graph embedding methods. While logical rules can capture deterministic behavior in a KG they are brittle and mining ones that infer facts beyond the known KG is challenging. Probabilistic embedding methods are effective in capturing global soft statistical tendencies and reasoning with them is computationally efficient. While embedding representations learned from rich training data are expressive, incompleteness and sparsity in real-world KGs can impact their effectiveness. We aim to leverage the complementary properties of both methods to develop a hybrid model that learns both high-quality rules and embeddings simultaneously. Our method uses a cross feedback paradigm wherein, an embedding model is used to guide the search of a rule mining system to mine rules and infer new facts. These new facts are sampled and further used to refine the embedding model. Experiments on multiple benchmark datasets show the effectiveness of our method over other competitive standalone and hybrid baselines. We also show its efficacy in a sparse KG setting and finally explore the connection with negative sampling.
