We present ACLBot, an interactive chatbot designed to support literature exploration in the ACL Anthology by combining structured knowledge graph querying with large language model (LLM) generative AI. ACLBot integrates a Neo4j-based knowledge graph constructed by extracting data on publications, authors, topics, and research trends from the ACL Anthology, and automatically generates knowledge graph queries to retrieve relevant information in response to user questions. Retrieved results are re-injected into the LLM to produce concise, contextually grounded summaries. We describe the system’s architecture, including its query generation pipeline, knowledge graph integration, and visualization components for highlighting temporal trends in research. To assess usability and effectiveness, we conducted a user evaluation with researchers, collecting qualitative and quantitative feedback on response accuracy, informativeness, and utility for literature discovery. Results indicate that ACLBot effectively supports exploratory search, helps identify relevant works and trends, and offers a promising framework for integrating structured information with generative AI for scientific information retrieval.