Unlocking the Knowledge Nexus: AI-Powered Graphs for Smarter User-Centric Knowledge Management

The exponential growth of organizational data, fueled by modern enterprises and the web, poses significant challenges to effective knowledge management, including cognitive overload and navigational disorientation [1-2]. This paper proposes an innovative framework for optimizing organizational memory management using conceptual graphs and semantic user profile modeling. Leveraging graph metrics such as density and spread—adapted from protein graph similarity measures [3]—we analyze knowledge connectivity and enhance information retrieval alongside personalized recommendation systems [4]. By integrating semantic ontologies (engineered via METHONTOLOGY [5]) with contemporary data processing techniques [6], our approach improves system efficiency. The user profile is represented as a conceptual graph, with a novel Labriji-inspired similarity function computing interest centers to filter relevant content. Empirical validation on the Open Directory Project (ODP) ontology and a simulated university dataset (20,000 documents) demonstrates a 25% increase in recommendation precision and 18% reduction in query latency compared to baselines like Wu-Palmer similarity [7]. This method addresses key gaps in adaptive information systems, offering extensible applications in education and collaborative environments. Future work explores multi-agent integration for dynamic ontology updates [8].