Gypscie-KG: Building a Logic-Based Approach for Knowledge Graph Data Integration View in ML Systems

This paper presents Gypscie-KG, a system that integrates heterogeneous machine learning (ML) data into a knowledge graph (KG) using logicbased rules to enable semantic queries and reasoning. It also explores the use of ChatGPT to support relational-to-graph schema mapping, demonstrating AI’s potential in declarative data integration for complex ML systems.

Palavras-chave: Knowledge Graphs, Machine Learning, Logic-Based Rules, Heterogeneous Data, Large Language Models, First-Order Logic, Declarative Integration, Semantic Reasonin, Schema Mapping