This conference will explore 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. For example, a machine can recognize that “Athens” is a city, distinguish it from the other cities with the same name, and link it to related concepts, which improves ability to perform tasks like searching, or making inferences. By tagging concepts, entities, and relations, semantic annotation enables machines to interpret and process data more accurately, connecting data points across software, allowing for better searchability, advanced queries and further reuse via natural language processing and machine learning.

Through this conference, we hope to foster collaboration and intellectual exchange amongst digital scholars of the ancient world. According to the principles of FAIR and Linked Open Data, we strive to promote openness and accessibility in all of the workflows and methods presented at the conference.

In order to further encourage the open exchange of ideas, the conference will also include a round-table discussion. We welcome accepted participants to suggest topics to discuss.