AES combines source-based documentation, controlled taxonomies, relational modelling, evidence chains and human-led validation to produce reliable, interconnected and reusable knowledge on traditional arts and crafts. AI-assisted analysis is used as an exploratory research layer: it helps reveal semantic proximity, recurring patterns, possible relationships and documentation gaps, while interpretation and validation remain source-based and human-led.
AES Knowledge Core Research Chain
Presence
Identifying the cultural presence of a craft, master, object, motif or practice.
Relationship
Mapping semantic and documentary links across people, places, objects and concepts.
Source
Collecting and assessing reliable written, visual, oral and archival sources.
Research Finding
Generating structured findings through comparative reading and contextual analysis.
Evidence
Linking findings to traceable evidence with explicit references and provenance.
Documented Example
Publishing documented examples within AES profiles, records and public interfaces.
Research Workflow
Controlled Taxonomies and Profiles
Controlled vocabularies and structured profiles connect crafts, masters, motifs, materials, techniques, periods and places.
Source and Evidence Verification
Sources are evaluated, compared and linked to findings through traceable evidence chains.
Terminology and Multilingual Equivalence
Terms are managed across languages through controlled equivalence, context and conceptual relationships.
Provenance, Versioning and Traceability
Data provenance and changes are recorded so research records can be traced and reassessed.
Human-Led Validation
Interpretation, validation and final research decisions remain human-led. Research outputs are reviewed against disciplinary knowledge, contextual evidence and source provenance before being treated as verified findings.
AI-Assisted Support
AI-assisted tools support semantic exploration, pattern recognition, multilingual comparison, potential relationship discovery and research-gap identification. Suggestions remain analytical prompts until human review and source verification are completed.
Data Outputs and Reuse
CSV Export
Tabular data for comparative analysis, filtering and reuse.
Research data →JSON Export
Structured machine-readable data for digital applications.
Structured data →GraphML Export
Relationship data for network and knowledge-graph analysis.
Network data →Profile PDFs
Downloadable profile documents with contextual and source information.
Profile documents →Structured exports allow AES records and bibliographic relationships to move beyond the interface into comparative research, teaching, data analysis and new digital heritage studies.
Methodological Principles
Transparency
Clear methods, visible criteria and traceable sources.
Source Criticism
Critical evaluation, comparison and contextual reading of sources.
Contextual Interpretation
Findings are interpreted within cultural, historical and geographical contexts.
Reproducibility
Structured data and traceable methods enable verification and reuse.
Open yet Responsible Access
Knowledge is shared while respecting cultural rights, provenance and sensitivities.