Introducing Semantic Tag Filtering: Enhancing Retrieval with Tag Similarity

Semantic tag filtering enhances traditional tag search by incorporating semantic similarity between tags. It addresses the flexibility limitations of traditional search by considering similar, non-identical tags in the search results. To implement this approach, a database of tagged samples is used. The algorithm involves extracting tag relationships through co-occurrence analysis or pre-trained neural networks, encoding queries and samples into vectors, performing semantic tag search using vector retrieval, and validating the results. By leveraging vector-based search engines, semantic tag filtering offers scalability and computational efficiency. It improves search accuracy by prioritizing relevant tags and disqualifying irrelevant ones. In cases where traditional search fails to return results, semantic tag filtering provides satisfactory alternatives.