Semantic Integration and Annotation Techniques for Tourism Data Based on the Semantic Web
DOI:
https://doi.org/10.63313/CS.8018Keywords:
Semantic Web, Tourism Knowledge Graph, Ontology Mapping, Semantic Annotation, Large Language Models, Data IntegrationAbstract
As culture and tourism integration accelerates, the tourism industry is increasingly challenged by heterogeneous data sources, inconsistent structures, and semantic fragmentation. Semantic Web technologies-through ontology construction, RDF modeling, semantic mapping, and logical reason-ing-offer a knowledge-driven approach to data integration and annotation. This paper comprehen-sively surveys key techniques for semantic tourism data integration and annotation, including data extraction, transformation, ontology alignment, and conflict resolution. It contrasts rule-based, ma-chine learning, and deep learning annotation paradigms, and evaluates mainstream tools for struc-tured and unstructured tourism data processing. The study further identifies technical bottlenecks in scalability, data quality, and semantic drift, proposing future directions such as LLM-assisted semantic annotation, incremental ontology evolution, and privacy-preserving reasoning. This research aims to provide a technical foundation and strategic reference for intelligent tourism systems and semantic data governance.
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