Handling duplicates with similar content but different names involves identifying and managing entities or data entries that represent the same core information but are labeled inconsistently. It differs from detecting exact duplicates because it requires recognizing semantic similarity despite variations in naming conventions, often using techniques like fuzzy matching, natural language processing (NLP), or entity resolution algorithms that compare attributes beyond just the name.

In practice, this is crucial in database management to merge customer records where "John Smith" and "J. Smith" refer to the same person. Search engines also employ this to group near-identical articles on the same topic published under different headlines, ensuring users see consolidated results. E-commerce platforms use it to link the same product sold by various retailers under different listing titles.

The main advantage is significantly improved data accuracy, integrity, and user experience by preventing redundant information. However, limitations include the risk of incorrect merges (false positives) if algorithms aren't finely tuned, potentially leading to data loss or misrepresentation. Ethical considerations involve transparency in how automated decisions affect content visibility or data grouping. Future advances in AI promise greater accuracy in semantic understanding.