The AI 2000 annual lists are automatically generated and determined by computer algorithms in the AMiner system, which uses Tsinghua AMiner's academic data to index more than 133 million expert profiles and 270 million publications since the 19th century. These algorithms track and rank scholars based on citation counts collected by top publications. Many world-renowned universities and organisations recognise the rankings, including the University of California, Berkeley, Cornell University, Duke University and the National University of Singapore.

As the most cited scholar in the field of knowledge engineering, Fellow Jens Lehmann is recognised for his work spanning knowledge graphs, representation learning, question answering, and conversational AI. A recurring theme across his research is bringing structured knowledge and machine learning together – from constructing and embedding large-scale knowledge graphs to grounding language models in reliable information. This line of work traces back to the DBpedia project, which he co-founded to extract structured content from Wikipedia and make it openly available as linked data, and extends through a sustained record of contributions that have shaped how structured knowledge is built, queried, and used in AI systems.

At the moment, he is working on Alexa+, the next generation of Alexa, where LLMs play a central role. In particular, he is focusing on two areas: improving the LLMs themselves through post-training techniques, and LLM routing – determining which LLM or system should handle a given user request.

About Jens Lehmann

Jens Lehmann is a Principal Scientist at Amazon, where he works at the Alexa AI organisation to advance large language models, conversational AI, and knowledge graphs. He is a SECAI Academic Fellow, holds an honorary professorship at TU Dresden, and has been selected as a fellow of ELLIS. Further, he is a member of InfAI. His academic activities at TU Dresden and InfAI support the Smart Data Analytics research group. His research interests include knowledge graphs, machine learning, and question answering and dialogue systems. His main research goal is to investigate and develop general intelligent systems by combining knowledge- and data-driven approaches.