基于推理闭包的嵌入方法，提升知识库补全精度
DELE: Deductive $\mathcal{EL}^{++}$ Embeddings for Knowledge Base Completion
- 利用本体推理闭包区分可推导与不可推导语句
- 设计新型负样本损失函数，显著提升补全准确率
- 适合需要高精度逻辑推理的知识图谱研究者
本体嵌入将本体中的类、关系和个体映射到ℝⁿ空间，可在该空间中计算实体相似性或推断新公理。针对描述逻辑ℒ⁺⁺本体，已有多种基于优化的嵌入方法能显式生成本体模型，但存在局限：无法区分无法证明与被证伪的语句，可能误将蕴含语句当作负例；且未利用本体的推理闭包来识别已推导但未声明的语句。本文评估了若干ℒ⁺⁺本体嵌入方法，并引入多项改进，特别设计了同时考虑推理闭包与不同类型的负例的新负损失函数，提出了适用于知识库补全的评估方法。实验表明，所提嵌入方法在知识库/本体补全任务上优于基线方法。
原文摘要 · Abstract (English)
Ontology embeddings map classes, roles, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic $\mathcal{EL}^{++}$, several optimization-based embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations; they do not distinguish between statements that are unprovable and provably false, and therefore they may use entailed statements as negatives. Furthermore, they do not utilize the deductive closure of an ontology to identify statements that are inferred but not asserted. We evaluated a set of embedding methods for $\mathcal{EL}^{++}$ ontologies, incorporating several modifications that aim to make use of the ontology deductive closure. In particular, we designed novel negative losses that account both for the deductive closure and different types of negatives and formulated evaluation methods for knowledge base completion. We demonstrate that our embedding methods improve over the baseline ontology embedding in the task of knowledge base or ontology completion.
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