Integrating Artificial Intelligence with Knowledge Graphs for Context-Aware Reasoning in Complex Problem Solving
Keywords:
context-aware reasoning, artificial intelligence, knowledge graphs, complex problem solving, semantic inference, ontology integration, relational modeling, adaptive decision-making, graph reasoning, entity linkingAbstract
Artificial Intelligence (AI) has shown remarkable progress in problem-solving tasks; however, its ability to reason contextually in complex, dynamic environments remains limited. Integrating AI with Knowledge Graphs (KGs) offers a promising approach to address this gap by enhancing semantic understanding, relational mapping, and contextual awareness. This paper presents a conceptual and methodological framework for merging AI algorithms with KGs to facilitate context-aware reasoning in multifaceted problem domains such as scientific discovery, intelligent decision support, and adaptive planning. The proposed integration emphasizes the importance of entity linking, temporal-spatial reasoning, and ontology-driven contextual inference. We explore existing architectures, highlight challenges, and provide a future research roadmap aimed at advancing AI reasoning capabilities through structured knowledge integration.
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Copyright (c) 2023 Geraldine Carey Colleen (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
