Researcher, Intelligent Data Engineering Lab (INDE Lab), Informatics Institute, University of Amsterdam
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I began my career in 1982 as a Senior Research Programmer at Carnegie Mellon University's Robotics Institute. In 1984, I became one of the first knowledge engineers of the expert-systems era at Inference Corporation, where I helped build ART, one of the period's leading commercial AI tools. I went on to serve as founder and CTO of three startups (Limbex, TriVida, and Siderean) and as Chief Architect at Elsevier. After forty years in industry, I have returned to academic research at the University of Amsterdam's INDE Lab. I hold a PhD in Computer Science from the University of Amsterdam and a BS in Applied Mathematics (Computer Science) from Carnegie Mellon University.
I argue that knowledge engineering is explicitation: making the practice of a community of experts explicit, so that it can be reasoned with, evaluated, and answered for. The last of these has been the hard part, a constant problem from the rule-based systems of the 1980s to today's large language models: how do we build AI applications such that we can be accountable for their commitments?
My recent work shows how people who must answer for a knowledge base can use large language models as instruments in that work, without surrendering their own accountability for it. Prompted to evaluate assertions in a knowledge graph against a natural language definition, a model can produce a judgment and a rationale — flagging candidate factual errors and subtler disagreements about word meaning — in a form people can examine, challenge, and overrule, rather than a verdict they must take on trust. And because a model's judgments can conflict, a bilateral, paraconsistent reasoner lets people see exactly where those judgments are inconsistent and still reason soundly over them, keeping the contradictions visible and people answerable for what they do with them.
This work continues on three fronts: structured human–LLM dialogue protocols for building knowledge bases collaboratively, modal and substructural logics for reasoning about LLM judgment, and methods for probing which inferential rules an LLM actually endorses. Taken together, they point toward a reconceptualized practice of knowledge engineering built on dialogue between people and LLMs — dialogue through which people arrive at, and remain answerable for, the commitments a knowledge base records.
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Knowledge engineering with large language models — interview, Weaviate Podcast #139, 1 June 2026. Video
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PhD thesis defense layman's talk — University of Amsterdam, 30 April 2026. Video · Slides
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Elenchus: Generating Knowledge Bases from Prover–Skeptic Dialogues — invited talk, Commonsense AI group, Vrije Universiteit Amsterdam, 29 April 2026. Slides
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BBL: A Bilateral Modal Logic for LLM Factuality Evaluation — TLLM 2026, Tsinghua University, 3 April 2026. Slides
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A Brief History of Knowledge Engineering: A Practitioner’s Perspective — invited talk, Dagstuhl Seminar, 12 September 2022. Dagstuhl Reports
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Data science in practice at Elsevier — Harvard Data Science Initiative Industry Seminar, 15 September 2020. Video
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Oral History of Brad Allen — Computer History Museum, 16 May 2018. Video · Transcript
- Neurosymbolic Knowledge Engineering with Natural Language. Bradley P. Allen. PhD thesis, University of Amsterdam, 2026.
- Conceptual Engineering Using Large Language Models. Bradley P. Allen. In Vincent C. Müller, Leonard Dung, Guido Löhr, and Aliya Rumana, editors, Philosophy of Artificial Intelligence, Synthese Library, vol. 533. Springer, Cham, 2026.
- Elenchus: Generating Knowledge Bases from Prover–Skeptic Dialogues. Bradley P. Allen. arXiv:2603.06974, 2026.
- Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations. Bradley P. Allen, Prateek Chhikara, Thomas Macaulay Ferguson, Filip Ilievski, and Paul Groth. Proceedings of the 19th International Conference on Neurosymbolic Learning and Reasoning (NeSy 2025), PMLR 284:392–419, 2025.
- Standardizing Knowledge Engineering Practices with a Reference Architecture. Bradley P. Allen and Filip Ilievski. Transactions on Graph Data and Knowledge, 2(1):5:1–5:23, 2024.
- Knowledge Engineering using Large Language Models. Bradley P. Allen, Lise Stork, and Paul Groth. Transactions on Graph Data and Knowledge, 1(1):3:1–3:19, 2023.
- Case-Based Reasoning: Business Applications. Bradley P. Allen. Communications of the ACM, 37(3):40–43, 1994.
- Job-Shop Scheduling: An Investigation in Constraint-Directed Reasoning. Mark S. Fox, Bradley P. Allen, and Gary Strohm. AAAI, pages 155–158, 1982.
Copyright © 2026 Bradley P. Allen. Last updated 8 June 2026.