Knowledge Representation
Ontologies, knowledge graphs and semantic models that make scholarly knowledge explicit, connected and reusable.
Fields of inquiry
Research Areas
TALOS brings computational methods into dialogue with the humanities and social sciences, developing responsible ways to represent, analyse and connect complex forms of knowledge.
Fields of inquiry
Six areas
The six areas form one research programme. Cultural and social questions shape the data we create, knowledge representation gives those data structure, and language and AI methods help us examine them at new scales.
Digital Humanities
Computational approaches to texts, collections and research questions across the humanities.
Language Technologies
Natural-language processing for historical and modern languages, from linguistic annotation to language models.
Cultural Heritage Informatics
Digital methods for documenting, interpreting and connecting artefacts, monuments, archives and collections.
AI for Society
Critical and participatory research on how artificial intelligence affects education, culture and public life.
Data Science & AI
Machine learning, data analysis and explainable AI methods adapted to complex humanistic and social evidence.
Area 01
Knowledge Representation
Knowledge representation turns concepts, sources and scholarly claims into structures that both people and machines can interpret. TALOS develops models that preserve provenance, uncertainty and competing interpretations while enabling connections across collections, disciplines and languages.
Methods
- Ontology and ontoterminology engineering, conceptual modelling and competency questions.
- Knowledge graphs, linked open data, semantic annotation and SPARQL querying.
Selected outputs
Explore the open ontologies and datasets, the semantic tools, and the PhiloGraphia knowledge graphs.
Area 02
Digital Humanities
Digital Humanities at TALOS begins with the questions and practices of humanities scholarship. We design computational workflows for studying texts, people, places, objects and ideas while keeping sources, editorial decisions and interpretation visible.
Methods
- Digital scholarly editing, corpus construction, annotation and research-data modelling.
- Network analysis, spatial analysis, visualisation and exploratory interfaces.
Selected outputs
See TALOS research projects, browse the open research datasets, and discover our Digital Humanities teaching and training.
Area 03
Language Technologies
Language Technologies investigates how computational systems can analyse and generate language without erasing linguistic, historical or textual complexity. Our work spans ancient and modern languages and connects foundational NLP research with the needs of philologists, historians and other domain specialists.
Methods
- Tokenisation, morphological analysis, lemmatisation, named-entity recognition and semantic annotation.
- Corpus analysis, language modelling, information extraction and multilingual NLP.
Selected outputs
Explore the laboratory’s language-focused projects, publications and text and semantic-analysis tools.
Area 04
Cultural Heritage Informatics
Cultural Heritage Informatics connects the material record with the information needed to understand it. TALOS develops interoperable approaches for describing artefacts, monuments, inscriptions, coins, archival documents and the events and places associated with them.
Methods
- Collection and archaeological-data modelling, digitisation and semantic documentation.
- Provenance modelling, entity linking, controlled vocabularies and FAIR data workflows.
Selected outputs
Browse the cultural-heritage datasets and ontologies or see how they are used across TALOS projects.
Area 05
AI for Society
AI for Society examines artificial intelligence as a social, educational and ethical phenomenon. We study how AI systems reshape knowledge, work, learning and public institutions, and how people can participate meaningfully in decisions about their use.
Methods
- AI literacy, curriculum research, participatory inquiry and educational evaluation.
- Critical analysis of fairness, accountability, transparency and the social effects of AI.
Selected outputs
Discover our education and capacity-building programmes, collaborative projects and related research outputs.
Area 06
Data Science & AI
Data Science & AI develops computational methods for evidence that is heterogeneous, incomplete and open to interpretation. The aim is not only stronger performance, but systems whose results can be examined, explained and assessed by domain experts.
Methods
- Machine learning, deep learning, statistical analysis and multimodal data integration.
- Explainable and hybrid AI, evaluation, uncertainty estimation and reproducible workflows.
Selected outputs
See the laboratory’s AI research projects, open software and research publications.