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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T ACLBot: A Knowledge Graph-Driven Assistant for ACL Anthology Research
%A Buchmann, Jan
%A Lynden, Steven
%A Jokinen, Kristiina
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F buchmann-etal-2026-aclbot
%X 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.
%R 10.63317/33kmjwr3vv44
%U https://aclanthology.org/2026.lrec-1.214/
%U https://doi.org/10.63317/33kmjwr3vv44
%P 2731-2741

Markdown (Informal)

[ACLBot: A Knowledge Graph-Driven Assistant for ACL Anthology Research](https://aclanthology.org/2026.lrec-1.214/) (Buchmann et al., LREC 2026)

• ACLBot: A Knowledge Graph-Driven Assistant for ACL Anthology Research (Buchmann et al., LREC 2026)

ACL

• Jan Buchmann, Steven Lynden, and Kristiina Jokinen. 2026. ACLBot: A Knowledge Graph-Driven Assistant for ACL Anthology Research. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 2731–2741, Palma de Mallorca, Spain. ELRA Language Resource Association.