Overview
The workshop Smarter Extraction of ScholArly MEtadata using Knowledge Graphs, Language Models and Agents (SESAME) brings together researchers and practitioners interested in improving scholarly metadata through large language models, Agents, knowledge graphs, natural language processing, and linked-data technologies.
High-quality scholarly metadata is essential for search, discovery, research assessment, reproducibility, and the long-term operation of digital-library infrastructures. However, metadata about publications, authors, affiliations, datasets, software, models, citations, and other research objects is frequently incomplete, inconsistent, or distributed across heterogeneous sources.
SESAME focuses on methods that combine language models and structured knowledge to support reliable metadata extraction, normalization, enrichment, linking, validation, and evaluation. The workshop provides a platform for researchers, digital-library professionals, data curators, infrastructure providers, and policy experts to exchange methods, datasets, systems, evaluation practices, and lessons learned.
The workshop particularly welcomes work connecting large language models with linked data and knowledge graphs for tasks such as author disambiguation, affiliation normalization, citation-context understanding, persistent-identifier linking, provenance tracking, and transparent metadata curation.
Topics of Interest
Topics include, but are not limited to:
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Research Artifact Metadata Modeling and Granularity
- Metadata of Scholarly Publications, Datasets, Software, and Models
- Metadata Quality Assessment, Enrichment, and Curation
- Research Artifacts Provenance across Digital Libraries
- Cross-disciplinary Scholarly Metadata Interoperability
- Persistent identifiers, including DOI, ORCID, ROR, and related identifier systems
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Large Language Modelsa and Agents for Natural Language Processing (NLP) for Scholarly Metadata
- Research Artifacts Metadata Extraction using LLMs and Agents
- Prompt Engineering, fine-tuning for Scholarly Information Extraction
- Skill Development in Agents for Scholarly Information Extraction
- Evaluation, Reliability, and Issues for LLM-generated Scholarly Metadata
- Agentic approaches for Evaluating Scholarly Metadata
- Comparative Studies of LLM-based vs Agentic vs Traditional Methods
- LLMs and Agents for Scholarly Metadata Curation and Normalization
- AI-driven Curation, Preservation at scale, and long-term accessibility
-
Knowledge Graphs and Linked Data
- Construction of Scholarly Knowledge Graphs from Heterogeneous Metadata
- Linking and Aligning Entities across Repositories and Research Infrastructures
- Applications of KGs for Discovery, Recommendations and Impact
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Digital Libraries and Research Infrastructures
- Integration of Metadata workflows into Digital Library Systems
- Benchmarks, Datasets, and Shared tasks for Metadata Extraction and Modeling
- System Design for Metadata-intensive Digital Library Applications
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Societal, Ethical Impact and Future Policy Directions
- Ethical Implications of AI-driven Metadata Generation and Curation
- Metadata for Open Science, Reproducibility, and Research integrity
- Societal Impacts of Metadata Granularity on Scholarly Evaluation and Equity
- Policy Frameworks and Governance for Interoperable Metadata Infrastructures
Call for Papers
SESAME 2026 invites original research contributions addressing the above listed topics in three categories.
- Long Papers: 6–8 pages, excluding references
- Short or Position Papers: 2–4 pages, excluding references
- Demo, Dataset, or Benchmark Papers: 2–4 pages, excluding references
Important Dates (AoE)
All deadlines will use Anywhere on Earth (AoE) time.
- Paper submission: 15.08.2026
- Author notification: 01.09.2026
- Camera-ready submission: 10.09.2026
- Workshop date: To be announced
Submission
- Submission site: EasyChair
- Language: All submissions must be written in English.
- Format: All submissions must follow the CEUR Workshop Proceedings style.
- Review Policy: Double-blind peer review.
- Supplementary Material: Data, source code, and models are encouraged where appropriate.
Submissions should contain original work and should not be simultaneously under review elsewhere.
Program
The SESAME 2026 workshop program will be published after the paper-review process.
The workshop program will comprise of:
- Invited Keynotes Talks;
- Research Paper Presentations;
- Interactive Discussion Sessions
Organizers
Dr. Muhammad Asif Suryani
Knowledge Technologies for the Social Sciences (KTS),
GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany
Dr. Brigitte Mathiak
Knowledge Technologies for the Social Sciences (KTS),
GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany
Dr. Florian Reitz
Schloss Dagstuhl – Leibniz Center for Informatics,
Wadern, Germany
Dr. Florian Jäckel
Schloss Dagstuhl – Leibniz Center for Informatics,
Wadern, Germany
Florian Hauss
Data Science and Big Data Analytics,
Ulm University, Ulm, Germany
Prof. Dr. Ansgar Scherp
Data Science and Big Data Analytics,
Ulm University, Ulm, Germany
Program Committee
The SESAME 2026 Program Committee will be announced soon.
Registration
Registration information will be published later.
Venue and Participation
The workshop mode, participation format and further details will be shared later.
Code of Conduct
SESAME 2026 will follow the code of conduct as per JCDL guidelines.
Previous Editions
Contact
Questions? Email asif.suryani@gesis.org or open an issue in the SESAME GitHub repository.
© 2026 SESAME Organizers
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