Data cleaning ensures that the knowledge graph is accurate, consistent, and free of noise. Developers begin by deduplicating records, standardizing formats, and resolving ambiguous identifiers. Entities should have unique IDs, normalized names, and validated types. Missing or conflicting relationships are either inferred through rules or flagged for manual review.
Textual data is particularly prone to inconsistencies. Preprocessing steps like lowercasing, lemmatization, and stopword removal reduce variability. Structured sources are cross-validated using reference data or ontology constraints to avoid contradictions. The cleaner the source, the fewer errors propagate into the graph’s reasoning layer.
Semantic cleaning extends this process to embeddings. When Zilliz is part of the workflow, developers can detect anomalies through vector clustering—identifying outliers that don’t fit known semantic groups. Removing or correcting these vectors before insertion keeps retrieval accurate and stable. Clean data at both structural and semantic levels yields a more trustworthy graph.