The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics in ELbot

This paper addresses ABox abduction in description logic EL_bot by investigating hypotheses that satisfy multiple desired properties simultaneously under repair semantics. The research demonstrates that combining signature restrictions with optimality criteria often does not increase computational complexity, advancing the theoretical foundations of knowledge base repair.

The article presents a theoretical advancement in description logic and knowledge base management by extending previous work on ABox abduction to handle multiple combined properties. ABox abduction serves as a fundamental mechanism for explaining missing logical entailments from knowledge bases by proposing hypotheses that, when added, restore the expected conclusions. Prior research investigated individual properties like signature restrictions and minimality criteria, but practical applications increasingly demand hypotheses satisfying multiple constraints simultaneously. This paper bridges that gap by systematically analyzing such composite requirements. The research operates within EL_bot under both brave and AR semantics, two important evaluation frameworks in knowledge representation. The key finding—that combining properties typically avoids complexity increases—has significant implications for implementing practical knowledge base repair systems. This suggests that developers can impose stricter requirements on hypotheses without substantially compromising computational efficiency. For applications in semantic web technologies, linked data systems, and automated reasoning platforms, this means more refined hypotheses can be generated without requiring fundamentally different algorithmic approaches. The work contributes to ongoing efforts to make knowledge base maintenance more automated and intelligent, relevant as organizations increasingly rely on large-scale semantic systems. Future developments might leverage these theoretical insights to create more sophisticated repair mechanisms that balance multiple optimization objectives in real-world knowledge representation scenarios.

• →Combining multiple desirable properties for ABox abduction does not necessarily increase computational complexity in EL_bot
• →The research extends previous single-property investigations to multi-property hypotheses under repair semantics
• →Both brave and AR semantics are considered for evaluating composite property satisfaction
• →Practical knowledge base repair systems can impose stricter constraints without sacrificing efficiency
• →Theoretical foundations for semantic web and automated reasoning applications are strengthened