As organizations ingest ever-larger volumes of data from the web, they increasingly rely on knowledge graphs (KGs) to model entities (people, organizations, products, places) and their relationships in a structured way. However, web data is heterogeneous, noisy, and heavily duplicated. The same entity may appear thousands of times across sites, with different names, formats, partial data, or conflicting attributes. Without robust deduplication and canonicalization, a scraped knowledge graph quickly becomes fragmented, inaccurate, and operationally useless.