How to achieve automatic mapping and conversion of heterogeneous semantic tags during knowledge graph integration?

In the process of knowledge graph integration, the automatic mapping and conversion of heterogeneous semantic tags typically requires the integration of ontology alignment, rule engines, and machine learning technologies. When dealing with tags that have differences in format, definition, or domain across multi-source data, a standardized intermediary is needed to achieve unified association. The specific implementation paths include: - Ontology alignment: Construct a shared domain ontology as an intermediary, and map heterogeneous tags to ontology concepts through term similarity calculation (such as semantic matching based on WordNet or BERT); - Rule engine: Preset conversion rules (such as regular expressions, OWL axioms) to process structured tags, for example, uniformly mapping "客户ID" and "用户编号" to "user identifier"; - Machine learning: For complex or ambiguous tags, use labeled data to train classification models (such as SVM, BERT) to automatically identify tag semantics and complete mapping. It is recommended to start by building a core domain ontology, process high-frequency tags with business rules, and then train models with a small amount of labeled data to optimize edge cases, thereby improving the data consistency and integration efficiency of the knowledge graph.