Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)

Association for Computational Linguistics, 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.

Chapters

Schema-Guided Extraction of Interaction Knowledge Bases from Narrative Text: A Folktale Case Study

Ugleva A., Kovalchuk S., Kubryak O., , in: Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026).: Association for Computational Linguistics, 2026. P. 261–283.

We present MAGIC, a six-dimensional framework for extracting structured knowledge bases of hero and magic entity interactions from narrative texts of folktales and fairytales: each interaction between a story’s protagonist and a magical helper, villain, or object is decomposed along trigger modality, actor profile and agency balance, interaction mechanics, outcome valence, narrative function, and narrative ...

Added: October 28, 2026

Keywords: computational linguistics, knowledge, empirical methods, large language models