How to handle data updates and version control during the maintenance of a brand knowledge graph?

When maintaining a brand knowledge graph, data updates require the establishment of trigger mechanisms and validation processes, while version control needs clear labeling rules and change records to ensure data accuracy and traceability. Data updates are typically based on two types of scenarios: first, core information changes (such as adjustments to brand positioning or product iterations), which need real-time synchronization; second, regular data refreshes (such as quarterly integration of industry dynamics), which can be achieved by capturing public data sources through automated tools. After updates, manual verification (such as checking official materials) and semantic consistency checks are required to avoid conceptual conflicts. Version control can use the "timestamp + iteration number" labeling (e.g., 2024Q3_v2), while simultaneously maintaining a change log that records modification content, responsible persons, and associated business scenarios (such as marketing campaign updates). It is recommended to conduct version audits every six months, compare differences between historical versions, and promptly clean up redundant data. This process helps maintain the timeliness of the brand knowledge graph and supports accurate information output in AI search scenarios.