Abstract: A system and method are provided for integrating and managing cross-domain ontologies in explainable artificial intelligence environments. The system features an ontology integration module that maintains concurrent domain-specific ontologies while establishing cross-domain relationships using semantic similarity algorithms, mapping concepts across domains. A relationship mapping engine identifies semantic similarities between concepts across different domain ontologies, while a domain extension mechanism detects emerging domains and establishes initial cross-domain mappings. These techniques advance the field of ontology integration and explainable artificial intelligence by providing a solution for maintaining and extending cross-domain relationships while adapting to emerging domains, thereby enabling more effective knowledge transfer across industry boundaries.

Abstract: A system and method are provided for generating explainable recommendations through an integrated approach combining ontology management and adaptive explanation frameworks. The system includes an ontology management module that maintains consistency while enabling cross-domain integration. The system also includes an explainable artificial intelligence (AI) framework that adapts explanation complexity to user expertise levels. The ontology management module employs semantic similarity algorithms to update and expand a multi-domain ontology, integrating knowledge across diverse domains. This unified ontological foundation supports the explainable AI framework in generating accurate recommendations with contextually appropriate explanations. The system advances the field of explainable AI by addressing the challenges of ontological consistency during updates while making AI recommendations more accessible through expertise-based explanation adaptation.