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Open data initiatives continue to grow, and with them, the number of independent catalogs published across various platforms. Comparing these catalogs helps with tasks such as improving search quality, linking related datasets, and maintaining consistent metadata. This paper surveys methods for measuring similarity between open data catalogs. Approaches include matching structured elements like triples, applying hashing techniques to domain-specific terms, and using semantic representations to align catalog contents. Each method comes with different assumptions about how metadata is structured and how records can be matched. We also examine practical limitations, such as poor metadata quality, differences in schema, and limited availability of shared benchmarks. The final section outlines directions for future work, including cross-lingual catalog matching, automated metadata enrichment, and techniques that better scale to large catalog collections.