Statistical Relational Learning with Soft Quantifiers

Abstract

Abstract. Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as "most" and "a few". In this paper, we define the syntax and semantics of PSL Q , a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modeling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results.

Research progress of large-scale knowledge graph completion technology

“…[49,50] 不再一一赘述了. 总之, 统计关系学 习方法除了 Farnadi 等 [51,52] 提出了在一阶逻辑中引入软性约束之外没有太大的新进展, 在知识图谱 补全中的应用也多作为一种关联程度评价方法结合到预测过程中 [53∼62] .…”