Ramin Okhrati
Papers
1
Total Citations
53
H-Index
1
About
Ramin Okhrati is a researcher at the intersection of natural language processing, knowledge graphs, and deep learning, with a focus on transforming how humans interact with structured data. His most cited work introduces a novel Text-to-GraphQL (Text2GQL) model for intelligent medical consultation chatbots, which converts user questions into Graph Query Language queries against graph databases—a significant advancement in semantic parsing that bridges natural language and logical expressions. This paper has garnered 53 citations since 2022, reflecting its impact on making graph databases more accessible through conversational interfaces. Beyond this flagship contribution, Okhrati’s research spans deep learning architectures for knowledge graph integration, demonstrating how neural models can enhance query generation and reasoning over complex relational data. His work is particularly notable for its practical applications in healthcare, where it enables more efficient, direct communication between patients and AI-driven systems. By tackling the challenge of translating ambiguous human questions into precise graph queries, Okhrati has advanced the field of semantic parsing and opened new pathways for intelligent chatbots that rely on structured knowledge bases.
Research Focus
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Top Papers
- 1