Ontology-Based Knowledge Organization and Multimodal Information Integration for Chinese Zisha Teapot Heritage
Year: 2026
Type: article
Abstract: Chinese Zisha teapot heritage information is dispersed across catalogs, scholarly literature, and museum collections, presenting challenges for consistent knowledge organization, automated recognition, and structured information access. This study presents a provenance-aware framework combining ontology-based knowledge organization, multimodal teapot-type recognition, and graph-backed question answering. A domain ontology and Neo4j knowledge graph were constructed to organize catalog-derived information, with the initial graph containing 607 core teapot entities. For six catalog-derived operational categories, 543 image–text pairs were evaluated using Chinese-CLIP representations, unimodal baselines, feature concatenation, equal-weight fusion, and bidirectional cross-attention under grouped cross-validation and text-field ablation. Equal-weight fusion achieved a mean Macro-F1 of 84.41%±0.78% under author-based grouping, while cross-attention showed no statistically significant advantage over simpler fusion methods. Additionally, 543 archived recognition predictions were incorporated into the extended knowledge graph as separate, provenance-linked assertions. On a locked 120-question benchmark, the structured question-answering system achieved 85.00% accuracy, which was reproduced through direct Neo4j execution. A partial alignment with CIDOC CRM was further examined through RDF validation and limited Omeka S metadata exchange. The results demonstrate the feasibility of traceable knowledge integration and structured retrieval for Zisha teapot heritage, while highlighting limitations concerning dataset scale, external generalization, and semantic interoperability.
Source: Information
DOI: 10.3390/info17111112
Institution: Nanjing Forestry University
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7225382135