这篇论文讲的是如何组合多个最优属性来生成解释，而且不增加复杂度，对做知识推理的朋友挺有用。
该论文研究了EL_bot本体语言中，在修复语义（brave和AR语义）下，ABox溯因问题中组合多个属性（如签名限制、最小冲突等）和最优性标准的假设。作者发现，同时满足多个属性或附加最优性标准通常不会增加计算复杂度。论文通过理论分析证明了这一结果，为知识库缺失蕴涵的解释提供了更实用的假设生成方法。
The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics for ELbot
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.