Hendy Irawan

Papers

Total Citations

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About

Hendy Irawan is a researcher whose work sits at the intersection of artificial general intelligence (AGI) and graph database technologies. His primary research area focuses on developing scalable, graph-based memory and knowledge representation systems for cognitive architectures. Irawan’s most notable contribution is his pioneering implementation of a graph database backend for the OpenCog AGI framework using Neo4j. In this work, he designed the GraphBackingStore API, which extends OpenCog’s existing BackingStore C++ interface, enabling complex cognitive queries to be naturally mapped into Cypher queries and Neo4j graph traversals. This integration allows OpenCog to leverage the power of graph databases for efficient knowledge storage and retrieval—a critical component for AGI systems. While his most-cited paper has garnered 2 citations, its significance lies in bridging the gap between symbolic AI and modern graph database technology. Irawan’s work provides a practical foundation for researchers exploring how graph-based memory systems can support advanced reasoning and learning in artificial general intelligence, making his contribution a valuable reference point for those working on scalable cognitive architectures.