SA4AW Conference

About the Conference

The Semantic Annotation for the Ancient World conference focuses on methods, standards, and tools for the semantic annotation of historical, literary, and cultural heritage materials.

SA4AW Conference 2026

The 2nd Semantic Annotation for the Ancient World (SA4AW) conference examines the role of semantic annotation in dialogue with hybrid AI approaches, large language models (LLMs), deep learning, and knowledge graphs in the study of the ancient world and cultural heritage. Semantic annotation—whether manual, automated, or human-in-the-loop—enables the identification and linking of concepts, entities, and relationships, transforming raw textual and visual data into structured, interoperable knowledge.

SA4AW Conference 2025

This conference explored the contribution of semantic annotation, along with that of hybrid AI, deep learning, and knowledge graphs to ancient world studies. Semantic annotation is the process of tagging or (manually or automatically) labelling pieces of content—such as words, phrases, or objects in texts or images—with meaningful metadata to provide context and clarify meaning. Semantic annotation allows machines to process the meaning and relationships of content within a dataset, transforming raw data into structured knowledge.

Contact Information

Phone+30 28310 77140
Emailsa4aw.contact@gmail.com
AddressUniversity of Crete Research Center for the Humanities, the Social and Education Sciences, Gallos, Rethymno, GR 74100, Crete

Semantic Annotation for the Ancient World - SA4AW Conference

About the Conference

We welcome contributions relating to

Ontology-based models and frameworks for semantic annotation.

Computational philology approaches that rely on or produce semantically enriched corpora

Methods for multilingual and cross-lingual semantic annotation of historical and literary corpora

Automatic, semi-automatic, and assisted annotation pipelines, including annotation interfaces and workflow design

Semantic enrichment of texts and images through entity recognition, linking to controlled vocabularies, and ontology alignment

Use of Large Language Models (LLMs) and Generative AI for annotation, enrichment, disambiguation, and knowledge extraction

Human–AI collaborative annotation strategies, including expert validation, iterative refinement, and quality control

Integration of annotated corpora with Knowledge Graphs and Linked Open Data ecosystems

Digital Classics and Cultural Heritage infrastructures supporting annotation, curation, and data interoperability

Ethical and epistemological considerations in AI-supported semantic annotation, including transparency, provenance, and interpretability