11th Workshop on
Automated Knowledge Base Construction

Co-located with EMNLP 2026 in Budapest, Hungary — October 28, 2026.

While Large Language Models (LLMs) have revolutionized NLP, they remain prone to hallucinations, reasoning “mode-collapse” in open-ended generation, and a lack of factual provenance. The Automated Knowledge Base Construction (AKBC) workshop addresses a key missing piece of the generative era: structured knowledge. Knowledge Bases (KBs) serve as ground truth for fact verification, the semantic backbone for constrained decoding in generation, and as a resource behind Retrieval-Augmented Generation (RAG).

The workshop contributes to the growing momentum around integrating structured knowledge into generative models, both at training time and at inference time.

It follows a successful series of previous editions: as an independent conference in 2022, 2021, and 2020, as workshop in 2017 at NIPS, in 2016 at NAACL, in 2014 at NIPS, in 2013 at CIKM, in 2012 at NAACL, and in 2010 as a stand-alone event in Grenoble, France.

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News

September 8th, 2026: Out of 58 submissions, 27 papers were accepted — see the list of accepted papers
September 8th, 2026: Parisa Kordjamshidi (Michigan State University) is confirmed as a keynote speaker
September 1st, 2026: Shared task results are out — see the winners on the shared task page and the full final leaderboard
August 12th, 2026: Denny Vrandečić (Wikimedia) is confirmed as a keynote speaker
July 30th, 2026: Amir Globerson (Tel Aviv University & Google) is confirmed as a keynote speaker
July 23rd, 2026: Dan Roth (University of Pennsylvania & Oracle) is confirmed as a keynote speaker
May 28th, 2026: Alon Halevy (Google) is confirmed as a keynote speaker
May 27th, 2026: The workshop day is fixed to October 28, 2026
May 21st, 2026: We are excited to announce Heng Ji (UIUC) as a keynote speaker
May 21st, 2026: We are excited to announce Mausam (IIT Delhi) as a keynote speaker
April 27th, 2026: We receive news that the workshop will be held on October 28, 2026
April 13th, 2026: The Web page of AKBC goes live

Important Dates

Direct submission (research + vision)	July 27, 2026
Direct submission (shared task)	August 15, 2026
ARR commitment (research)	August 25, 2026
Notification of acceptance	September 1, 2026
Camera ready due	September 10, 2026
Workshop date	October 28, 2026

Call for Papers

The 11th Workshop on Automated Knowledge Base Construction (AKBC) returns in 2026, bringing together researchers and practitioners working on the construction, integration, and use of structured knowledge in the era of large language models (LLMs). As LLMs continue to transform NLP, challenges such as hallucinations, lack of provenance, and limited reasoning reliability highlight the need for robust, explicit, and usable knowledge representations. AKBC sits at the intersection of natural language processing, knowledge representation, databases, and machine learning, with a particular focus on how symbolic structure can ground, constrain, and enhance generative models.

Topics of interest include, but are not limited to:

Knowledge for generative models: knowledge-aware pretraining and fine-tuning; factuality, attribution, and verification; neuro-symbolic and hybrid methods; injecting and editing knowledge in LLMs
Building and maintaining knowledge: extraction and consolidation from text and multimodal data; knowledge graphs, ontologies, and schema alignment; KB construction, completion, and continual updates
Retrieval and reasoning: retrieval-augmented generation with structured sources; graph-based and knowledge-intensive question answering; multi-hop reasoning and interaction with KBs
Vision papers: new roles for structured knowledge in generative models; new architectures, benchmarks, and research agendas for knowledge-grounded generation; the future of knowledge bases, reasoning, and trustworthy AI

Submission Types

Regular research papers (8 pages + references), via ARR commitment or direct submission
Vision papers (4 pages + references), via direct submission: bold ideas, emerging directions, and unifying perspectives
Shared task papers (4 pages + references), via direct submission: system descriptions and analyses for the AKBC shared task

All submissions follow the EMNLP formatting instructions, are double-blind, and appear in the workshop proceedings, so double submissions to other venues with proceedings are not allowed. An optional limitations section and appendix do not count towards the page limit, but we cannot guarantee that the appendix will be part of the proceedings.

Accepted papers are presented as posters, with a selection invited for lightning talks. Remote participation is possible for attendees and authors; remote authors upload their poster to the workshop website instead of presenting physically.

Accepted Papers

A Rhetorical Citation Event Graph Model for Characterizing Scientific Findings: Resource Construction and Application to Plant HealthAnne-Sophie Foussat, Robert Bossy, Vincent Guigue, Sauvion, Claire Nédellec
A Temporal Knowledge Graph for Music Festival Lineup ForecastingJulia Gastinger, Thilo Ignaz Dieing, Christian Meilicke, Heiner Stuckenschmidt
Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5Shrestha Ghosh, Luca Giordano, Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?Younes Boufouss, Luc Pommeret, Thomas Gerald, Patrick Paroubek, Sophie Rosset
Distill-RE: Few-Shot Relation Extraction via Teacher LLM Reasoning DistillationDanielle Sullivan-Pao, Sheila Alemany, Olga Simek
Edge Knowledge Bases for Sustainable and Explainable AIAparna S. Varde, Emil Njor, Francesco Daghero, Mahyar Tourchi Moghaddam
Epistemic Twins: Enabling a Symbolic Science of Language Model KnowledgeSimon Razniewski, Shrestha Ghosh, Luca Giordano, Yujia Hu, Tuan-Phong Nguyen
EventSocraticKG: Event-Centric Knowledge Graph Construction for Narrative Retrieval-Augmented GenerationWilliam Rosengren
Evidence-Carrying Claims: Fail-Closed Verification Gates for Language-Model-Assisted Research as Knowledge Base ConstructionRob Sneiderman
Evidence-Guided Schema Normalization for Temporal Tabular ReasoningAshish Thanga, Vibhu Dixit, Abhilash Shankarampeta, Vivek Gupta
Explaining Textual Entailment with Lexical Entailments: Using LLMs to Supply Lexical Relations for Formal ProofsJorryt de Jong, Stefan Moraca, Ettore Cesari, Lasha Abzianidze
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL PairsDarian Lee, Shannon Rumsey, Jack St. Clair, Xinyi Tang, Aditya Bansal, Yuanming Shi
Fine-Grained Table Retrieval Through the Lens of Complex QueriesWojciech Kosiuk, Xingyu Ji, Yeounoh Chung, Fatma Ozcan, Madelon Hulsebos
From Imitation to Reasoning: Sample-Efficient LLM Agents for Knowledge Graph CompletionChenjun Zheng, Zhe Wang, Zhigang Wang, Wenjia Zhang, Xiaolin Yang
GRACE: Graph-Grounded Reflective Agent Copilot Engine for Expert-in-the-Loop Knowledge ExpansionJohn Seon Keun Yi, Joshua R Minot, Dokyun Lee
GRACE-Mem: Graph Retrieval with Agentic Curation of Evidence for Long-Term Conversational MemoryHeng Wang, Xuan Ni Chen, Pei-Yun Chang, Chih-Hsi Chen, Hsin-Yu Huang, Chienyi Lin, I-Ling Chung
HTTP for Meaning: Towards Linguistically Complete Knowledge Representations with Knowledge State ProtocolIan Yuen
LMKG: A Flexible Method for Ontology-Aware Knowledge Graph Extraction from TextGianmarco Pappacoda, Paolo Torroni
MUSART: A Scalable Multi-Domain Benchmark for Measuring Factual Recall in Open-Weight Language ModelsRajaa El Hamdani, Thomas Bonald, Fragkiskos D. Malliaros
Operationalization Is Part of the Claim: Operationalization-Aware Knowledge in the Era of Generated AnswersJan-Christoph Kalo
Revisiting RAG Retrievers: An Information Theoretic BenchmarkWenqing Zheng, Dmitri Kalaev, Noah Fatsi, Daniel Barcklow, Owen Reinert, Igor Melnyk, Senthil Kumar, C. Bayan Bruss
Scaling E-Commerce Attribute Extraction with Parallel DecodingNikhita Vedula, Dushyanta Dhyani, Bryan Wang, Shervin Malmasi
Schema-Guided Extraction of Interaction Knowledge Bases from Narrative Text: A Folktale Case StudyHannah Bansal, Daniel Cabrera Lozoya, Karin Verspoor
The Canonical Order Problem: When Large Language Models Are Unreliable Knowledge Bases for Multi-Valued RelationsTimo Pierre Schrader, Annemarie Friedrich, Simon Razniewski, Lukas Lange
When Relevance Is Not Evidence: A Controlled Diagnostic of Claim-Conditioned Support RankingKe Lu
When String Matching Stands In for a Knowledge Base: A Role-Aware Audit of WikiBigEditEdison Yang, Neel Marripalapu, Akshaj Agadi
Zero-Shot Relation Classification with Large Language Models: Binary Prompting and Relevance ElicitationSheila Alemany, Danielle Sullivan-Pao, Olga Simek

Shared Task

Large language models contain a substantial amount of factual knowledge. Turning that knowledge into reliable knowledge base entries, however, is much harder than answering a single factual question.

Given a subject s and a relation r, predict the complete set of correct object strings {o₁, o₂, …, oₖ}. Unlike standard factual QA, a subject may have zero, one, or many correct objects. The goal is to construct a complete and precise KB entry.

Further details are on the shared task page.