在EL_bot中联合多种性质提升知识库补全效果
The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics in ELbot
- 同时满足多个语义性质的假设可提升解释质量
- 多性质组合未增加计算复杂度，保持高效
- 适合需要高可靠性推理的应用场景
归因是解释知识库中缺失蕴含关系的核心方法，通过提出一个假设，若加入知识库则能使原缺失蕴含成立。在修复语义下，已有研究关注签名限制、规模最小化及引入冲突最少等理想性质与最优性准则。然而，同时满足多个性质或结合性质与最优性的假设尚未被系统探讨。本文研究了在EL_bot语言下，针对勇敢语义和AR语义的ABox归因问题，探索满足多重性质或额外最优性准则的假设。主要发现是：通常附加更多性质并不会导致计算复杂度上升，表明此类假设在理论和实践上均具可行性。
原文摘要 · Abstract (English)
Abduction is a central approach to explain missing entailments from a knowledge base by providing a hypothesis, that would, if added to the knowledge base, make the missing entailment become true. Abduction under repair semantics has recently been investigated in detail, where several desirable properties and optimality criteria were considered, such as signature-restrictions and minimality in size and of introduced conflicts. Naturally, hypotheses that satisfy more than one of these properties or combine a property with an optimality criterion would be even more desirable for applications. So far, such hypotheses have not been investigated in the literature. In the present paper, we consider the ABox abduction problem for hypotheses satisfying more than one property or additional optimality criteria, for EL_bot under brave and AR semantics. Our main observation is that often requiring additional properties for hypotheses does not lead to an increase of complexity.
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