Composite symbolic and non-symbolic artificial intelligence system for advanced reasoning and semantic search

Abstract

A semantic search system integrates with an AI platform to provide advanced search capabilities by leveraging automatically generated ontologies and knowledge graphs. The system employs natural language processing, machine learning, and large language models to create, update, and align ontologies from diverse data sources. It supports context-aware query interpretation, personalized results, and complex reasoning by incorporating user context, feedback, and domain knowledge. The system optimizes search performance and efficiency through indexing techniques, distributed computing, and continuous learning. With a modular architecture and scalable infrastructure, the semantic search system enables users to retrieve relevant, meaningful, and context-specific information from vast amounts of structured and unstructured data. The integration of the semantic search system with the AI platform's components, such as knowledge graphs and model blending, enhances the platform's overall reasoning, decision-making, and problem-solving capabilities, empowering users with intelligent and intuitive search experiences across various domains and applications.