High-Throughput Vector Similarity Search in Knowledge Graphs
Summary: HQI enables high-throughput batch hybrid vector+predicate queries over knowledge graphs. It employs workload-aware vector partitioning to tailor index layouts and a multi-query optimizer, achieving 31× throughput over prior hybrid query methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jason Mohoney (Apple; University of Wisconsin)
- 2. Anil Pacaci (Apple)
- 3. Shihabur Rahman Chowdhury (Apple)
- 4. Ali Mousavi (Apple)
- 5. Ihab F. Ilyas (Apple)
- 6. Umar Farooq Minhas (Apple)
- 7. Jeffrey Pound (Apple)
- 8. Theodoros Rekatsinas (Apple)
BibTeX Citation
@inproceedings{mohoney_sigmod23,
title = {{High-Throughput Vector Similarity Search in Knowledge Graphs}},
author = {Mohoney, Jason and Pacaci, Anil and Chowdhury, Shihabur Rahman and Mousavi, Ali and Ilyas, Ihab F. and Minhas, Umar Farooq and Pound, Jeffrey and Rekatsinas, Theodoros},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589777},
url = {https://dl.acm.org/doi/10.1145/3589777},
year = {2023}
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