SESAME 2025 - 1st Smarter Extraction of ScholArly MEtadata using Knowledge Graphs and Language Models (SESAME) Workshop

“Smarter Extraction of ScholArly MEtadata using Knowledge Graphs and Language Models” and abbreviated as “SESAME”. The mission statement of SESAME is to bring together researchers and practitioners to explore how AI-driven curation approaches leveraging large language models and knowledge graphs to strengthen digital libraries infrastructures. The proposed workshop is intended for a broader spectrum of participants within the JCDL community, including researchers, data curators, and policy makers. It is particularly relevant to those working in digital library infrastructures, metadata curation, knowledge graph construction, information extraction, and natural language processing. Hence, participants from research backgrounds fields such as scientometrics, open science, and AI ethics will also find value, as the workshop addresses cross-cutting issues of data interoperability and transparency. The workshop aim to bring scientific community at platform encompssing of digital libraries, metadata workflows, large language models and knowledge graph. The workshop will combine foundational discussions with advanced perspectives, making it accessible to researchers across the discpline. The planned sessions keynotes talks, and collaborative activities will further ensure that participants of diverse backgrounds can contribute meaningfully to discussions and prospective conclusions. Emphasize the bridge between LLMs and linked data / KGs for high-quality scholarly metadata: Author Disambiguation, Affiiation normalization, citation context understanding, and evaluation.
Topics of Interest
Research Artifacts 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 metadata interoperability
Large Language Models (LLMs) and NLP for Metadata
Research artifacts metadata extraction using LLMs
Prompt engineering, fine-tuning for scholarly information extraction
Evaluation, reliability and issues for LLM-generated metadata
Comparative studies of LLM-based vs traditional methods
LLMs for 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 infrastructures
Applications of KGs for discovery, recommendation, and impact
Digital Libraries and Infrastructure
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
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
The workshop invites original research on the above mentioned topics in three categories. Each submission will be reviewed by domain experts according to the JCDL guidelines.
Long Papers: 6–8 pages (Excluding References)
Short Papers: 2–4 pages (Excluding References)
Demo Papers: 2–4 pages (Excluding References)