Navigable Proximity Graph-driven Native Hybrid Queries With Structured And Unstructured Constraints

Abstract

As research interest surges, vector similarity search is applied in multiple fields, including data mining, computer vision, and information retrieval. \{Given a set of objects (e.g., a set of images) and a query object, we can easily transform each object into a feature vector and apply the vector similarity search to retrieve the most similar objects. However, the original vector similarity search cannot well support \textit\{hybrid queries\}, where users not only input unstructured query constraint (i.e., the feature vector of query object) but also structured query constraint (i.e., the desired attributes of interest). Hybrid query processing aims at identifying these objects with similar feature vectors to query object and satisfying the given attribute constraints. Recent efforts have attempted to answer a hybrid query by performing attribute filtering and vector similarity search separately and then merging the results later, which limits efficiency and accuracy because they are

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• arxiv keywang2022navigable

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