{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:15:01Z","timestamp":1775283301514,"version":"3.50.1"},"reference-count":109,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:p>\n                    Knowledge graph reasoning (KGR) answers logical queries over a knowledge graph (KG), and\n                    <jats:italic toggle=\"yes\">embedding-based KGR<\/jats:italic>\n                    (EKGR) becomes popular recently, which embeds both queries and KG entities such that the vector embeddings of a query and its answer entities are similar. Compared with traditional KGR methods based on subgraph matching, EKGR produces fewer intermediate results and is more robust to missing and noisy information in the KG. However, existing systems are inefficient for serving online EKGR queries because they can only batch queries of the same type for execution (i.e.,\n                    <jats:italic toggle=\"yes\">query-level batching<\/jats:italic>\n                    ) and hence have limited batching opportunities due to the heterogeneity of queries.\n                  <\/jats:p>\n                  <jats:p>\n                    To serve EKGR queries efficiently, we propose the Atom system with\n                    <jats:italic toggle=\"yes\">operator-level batching,<\/jats:italic>\n                    which decomposes queries into operators and batches operators of the same type from different queries for execution. The insight is that the types of operators are far fewer than the types of queries, and thus different queries typically share common operators, yielding more batching opportunities. To schedule the operators, Atom adopts a\n                    <jats:italic toggle=\"yes\">hybrid policy,<\/jats:italic>\n                    which improves system throughput and avoids starving rare operators. For efficiency, Atom incorporates system optimizations including\n                    <jats:italic toggle=\"yes\">two-level pipeline,<\/jats:italic>\n                    <jats:italic toggle=\"yes\">opportunistic submission,<\/jats:italic>\n                    <jats:italic toggle=\"yes\">pre-allocated memory buffer,<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">tailored GPU kernels.<\/jats:italic>\n                    Experiment results show that compared with existing systems, Atom can improve query throughput by over 20x and reduce query latency by over 5x. Micro experiments suggest that the designs and optimizations are effective in improving system performance.\n                  <\/jats:p>","DOI":"10.1145\/3677129","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T17:41:44Z","timestamp":1727718104000},"page":"1-29","source":"Crossref","is-referenced-by-count":9,"title":["Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level Batching"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8927-3325","authenticated-orcid":false,"given":"Qihui","family":"Zhou","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4081-913X","authenticated-orcid":false,"given":"Peiqi","family":"Yin","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2122-915X","authenticated-orcid":false,"given":"Xiao","family":"Yan","sequence":"additional","affiliation":[{"name":"Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2768-1224","authenticated-orcid":false,"given":"Changji","family":"Li","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9837-0904","authenticated-orcid":false,"given":"Guanxian","family":"Jiang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6313-6288","authenticated-orcid":false,"given":"James","family":"Cheng","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2020. beta github. https:\/\/github.com\/snap-stanford\/KGReasoning.git."},{"key":"e_1_2_1_2_1","unstructured":"2020. Open Neural Network Exchange (ONNX). https:\/\/github.com\/onnx\/onnx.git."},{"key":"e_1_2_1_3_1","unstructured":"2020. query2box github. https:\/\/github.com\/hyren\/query2box.git."},{"key":"e_1_2_1_4_1","unstructured":"2021. newlook github. https:\/\/github.com\/amayuelas\/NNKGReasoning.git."},{"key":"e_1_2_1_5_1","unstructured":"2021. Triton. https:\/\/github.com\/triton-inference-server."},{"key":"e_1_2_1_6_1","unstructured":"2022. gamma github. https:\/\/github.com\/dyang67\/GammaE.git."},{"key":"e_1_2_1_7_1","unstructured":"2022. mlpmixer github. https:\/\/github.com\/amayuelas\/NNKGReasoning.git."},{"key":"e_1_2_1_8_1","unstructured":"2023. halk github. https:\/\/github.com\/yuhanwu0001\/HaLk.git."},{"key":"e_1_2_1_9_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2--4, 2016. USENIX Association, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi"},{"key":"e_1_2_1_10_1","volume-title":"The Tenth International Conference on Learning Representations, ICLR 2022","author":"Amayuelas Alfonso","year":"2022","unstructured":"Alfonso Amayuelas, Shuai Zhang, Susie Xi Rao, and Ce Zhang. 2022. Neural Methods for Logical Reasoning over Knowledge Graphs. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022. OpenReview.net. https:\/\/openreview.net\/forum?id=tgcAoUVHRIB"},{"key":"e_1_2_1_11_1","volume-title":"Complex Query Answering with Neural Link Predictors. In 9th International Conference on Learning Representations, ICLR 2021","author":"Arakelyan Erik","year":"2021","unstructured":"Erik Arakelyan, Daniel Daza, Pasquale Minervini, and Michael Cochez. 2021. Complex Query Answering with Neural Link Predictors. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=Mos9F9kDwkz"},{"key":"e_1_2_1_12_1","volume-title":"Adapting Neural Link Predictors for Data-Efficient Complex Query Answering. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023","author":"Arakelyan Erik","year":"2023","unstructured":"Erik Arakelyan, Pasquale Minervini, Daniel Daza, Michael Cochez, and Isabelle Augenstein. 2023. Adapting Neural Link Predictors for Data-Efficient Complex Query Answering. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/55c518a17bd17dcb69aa14d69d085994-Abstract-Conference.html"},{"key":"e_1_2_1_13_1","volume-title":"An Empirical Study of Real-World SPARQL Queries. CoRR abs\/1103.5043","author":"Arias Mario","year":"2011","unstructured":"Mario Arias, Javier D. Fern\u00e1ndez, Miguel A. Mart\u00ednez-Prieto, and Pablo de la Fuente. 2011. An Empirical Study of Real-World SPARQL Queries. CoRR abs\/1103.5043 (2011). arXiv:1103.5043 http:\/\/arxiv.org\/abs\/1103.5043"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2302.13114"},{"key":"e_1_2_1_15_1","volume-title":"International Conference on Machine Learning, ICML 2023","volume":"1491","author":"Bai Yushi","year":"2023","unstructured":"Yushi Bai, Xin Lv, Juanzi Li, and Lei Hou. 2023. Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization. In International Conference on Machine Learning, ICML 2023, 23--29 July 2023, Honolulu, Hawaii, USA (Proceedings of Machine Learning Research, Vol. 202). PMLR, 1472--1491. https: \/\/proceedings.mlr.press\/v202\/bai23b.html"},{"key":"e_1_2_1_16_1","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018","author":"Berger Daniel S.","year":"2018","unstructured":"Daniel S. Berger, Benjamin Berg, Timothy Zhu, Siddhartha Sen, and Mor Harchol-Balter. 2018. RobinHood: Tail Latency Aware Caching - Dynamic Reallocation from Cache-Rich to Cache-Poor. In 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018, Carlsbad, CA, USA, October 8--10, 2018. USENIX Association, 195--212. https:\/\/www.usenix.org\/conference\/osdi18\/presentation\/berger"},{"key":"e_1_2_1_17_1","volume-title":"Neural Graph Databases. In Learning on Graphs Conference, LoG 2022","volume":"31","author":"Besta Maciej","year":"2022","unstructured":"Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, and Torsten Hoefler. 2022. Neural Graph Databases. In Learning on Graphs Conference, LoG 2022, 9--12 December 2022, Virtual Event (Proceedings of Machine Learning Research, Vol. 198). PMLR, 31. https:\/\/proceedings.mlr.press\/v198\/besta22a.html"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300086"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915236"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570391"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577528"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3190508.3190545"},{"key":"e_1_2_1_23_1","volume-title":"MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274 (2015). arXiv:1512.01274 http:\/\/arxiv.org\/abs\/1512.01274"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V36I4.20310"},{"key":"e_1_2_1_25_1","volume-title":"Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017","author":"Crankshaw Daniel","year":"2017","unstructured":"Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, and Ion Stoica. 2017. Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017, Boston, MA, USA, March 27--29, 2017. USENIX Association, 613--627. https:\/\/www.usenix. org\/conference\/nsdi17\/technical-sessions\/presentation\/crankshaw"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD46524.2019.00075"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2408776.2408794"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477132.3483571"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3386144"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098040"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742786"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3190657"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303754"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3190508.3190541"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/3554821.3554843"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389699"},{"key":"e_1_2_1_37_1","volume-title":"Embedding Logical Queries on Knowledge Graphs. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018","author":"Hamilton William L.","year":"2018","unstructured":"William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec. 2018. Embedding Logical Queries on Knowledge Graphs. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada. 2030--2041. https: \/\/proceedings.neurips.cc\/paper\/2018\/hash\/ef50c335cca9f340bde656363ebd02fd-Abstract.html"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465300"},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the VLDB 2023 PhD Workshop co-located with the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, August 28, 2023 (CEUR Workshop Proceedings","volume":"36","author":"Horchidan Sonia","year":"2023","unstructured":"Sonia Horchidan. 2023. Query Optimization for Inference-Based Graph Databases. In Proceedings of the VLDB 2023 PhD Workshop co-located with the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, August 28, 2023 (CEUR Workshop Proceedings, Vol. 3452). CEUR-WS.org, 33--36. https:\/\/ceur-ws.org\/Vol-3452\/paper9.pdf"},{"key":"e_1_2_1_40_1","volume-title":"Joint Proceedings of the ESWC 2023 Workshops and Tutorials co-located with 20th European Semantic Web Conference (ESWC 2023), Hersonissos, Greece, May 28--29, 2023 (CEUR Workshop Proceedings","volume":"6223","author":"Horchidan Sonia","year":"2023","unstructured":"Sonia Horchidan and Paris Carbone. 2023. ORB: Empowering Graph Queries through Inference. In Joint Proceedings of the ESWC 2023 Workshops and Tutorials co-located with 20th European Semantic Web Conference (ESWC 2023), Hersonissos, Greece, May 28--29, 2023 (CEUR Workshop Proceedings, Vol. 3443). CEUR-WS.org. https:\/\/ceur-ws.org\/Vol-3443\/ESWC_2023_DMKG_paper_6223.pdf"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539338"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC41404.2022.00057"},{"key":"e_1_2_1_44_1","volume-title":"K\u00d9ZU Graph Database Management System. In 13th Conference on Innovative Data Systems Research, CIDR 2023","author":"Jin Guodong","year":"2023","unstructured":"Guodong Jin, Xiyang Feng, Ziyi Chen, Chang Liu, and Semih Salihoglu. 2023. K\u00d9ZU Graph Database Management System. In 13th Conference on Innovative Data Systems Research, CIDR 2023, Amsterdam, The Netherlands, January 8--11, 2023. www.cidrdb.org. https:\/\/www.cidrdb.org\/cidr2023\/papers\/p48-jin.pdf"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588692"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2022.FINDINGS-ACL.119"},{"key":"e_1_2_1_47_1","volume-title":"16th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2019","author":"Kaffes Kostis","year":"2019","unstructured":"Kostis Kaffes, Timothy Chong, Jack Tigar Humphries, Adam Belay, David Mazi\u00e8res, and Christos Kozyrakis. 2019. Shinjuku: Preemptive Scheduling for \u03bcsecond-scale Tail Latency. In 16th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2019, Boston, MA, February 26--28, 2019. USENIX Association, 345--360. https: \/\/www.usenix.org\/conference\/nsdi19\/presentation\/kaffes"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3034786.3034792"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2305"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3615952"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613165"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3445814.3446717"},{"key":"e_1_2_1_53_1","volume-title":"Accelerating Distributed MoE Training and Inference with Lina. In 2023 USENIX Annual Technical Conference, USENIX ATC 2023","author":"Li Jiamin","year":"2023","unstructured":"Jiamin Li, Yimin Jiang, Yibo Zhu, Cong Wang, and Hong Xu. 2023. Accelerating Distributed MoE Training and Inference with Lina. In 2023 USENIX Annual Technical Conference, USENIX ATC 2023, Boston, MA, USA, July 10--12, 2023. USENIX Association, 945--959. https:\/\/www.usenix.org\/conference\/atc23\/presentation\/li-jiamin"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467375"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2016.154"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1111\/TGIS.12629"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342643"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.5220\/0005469003990403"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2209.05828"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613163"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3196959.3196990"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180143"},{"key":"e_1_2_1_64_1","unstructured":"NVIDIA Corporation. 2023. Event Management. Version 12.2."},{"key":"e_1_2_1_65_1","unstructured":"NVIDIA Corporation. 2023. Stream synchronization behavior. Version 12.2."},{"key":"e_1_2_1_66_1","volume-title":"High-Performance ML Serving. CoRR abs\/1712.06139","author":"Olston Christopher","year":"2017","unstructured":"Christopher Olston, Noah Fiedel, Kiril Gorovoy, Jeremiah Harmsen, Li Lao, Fangwei Li, Vinu Rajashekhar, Sukriti Ramesh, and Jordan Soyke. 2017. TensorFlow-Serving: Flexible, High-Performance ML Serving. CoRR abs\/1712.06139 (2017). arXiv:1712.06139 http:\/\/arxiv.org\/abs\/1712.06139"},{"key":"e_1_2_1_67_1","volume-title":"High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada. 8024--8035. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bdbca288fee7f92f2bfa9f7012727740-Abstract. html"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2014.7004278"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.14778\/3149193.3149198"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539405"},{"key":"e_1_2_1_71_1","volume-title":"Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases. CoRR abs\/2303.14617","author":"Ren Hongyu","year":"2023","unstructured":"Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, and Jure Leskovec. 2023. Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases. CoRR abs\/2303.14617 (2023). https:\/\/doi.org\/10.48550\/ ARXIV.2303.14617 arXiv:2303.14617"},{"key":"e_1_2_1_72_1","volume-title":"Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases. CoRR abs\/2303.14617","author":"Ren Hongyu","year":"2023","unstructured":"Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, and Jure Leskovec. 2023. Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases. CoRR abs\/2303.14617 (2023). https:\/\/doi.org\/10.48550\/ ARXIV.2303.14617 arXiv:2303.14617"},{"key":"e_1_2_1_73_1","volume-title":"8th International Conference on Learning Representations, ICLR 2020","author":"Ren Hongyu","year":"2020","unstructured":"Hongyu Ren, Weihua Hu, and Jure Leskovec. 2020. Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=BJgr4kSFDS"},{"key":"e_1_2_1_74_1","volume-title":"Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Ren Hongyu","year":"2020","unstructured":"Hongyu Ren and Jure Leskovec. 2020. Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/ e43739bba7cdb577e9e3e4e42447f5a5-Abstract.html"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735479.2735493"},{"key":"e_1_2_1_76_1","volume-title":"Faiss: A library for efficient similarity search. https:\/\/github.com\/facebookresearch\/faiss.","author":"Research Facebook","year":"2017","unstructured":"Facebook Research. 2017. Faiss: A library for efficient similarity search. https:\/\/github.com\/facebookresearch\/faiss."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840993"},{"key":"e_1_2_1_78_1","volume-title":"Proceedings of the The British International Conference on Databases 2021, London, United Kingdom, March 28, 2022 (CEUR Workshop Proceedings","volume":"19","author":"Ritter Daniel","year":"2021","unstructured":"Daniel Ritter, Luigi Dell?Aquila, Andrii Lomakin, and Emanuele Tagliaferri. 2021. OrientDB: A NoSQL, Open Source MMDMS. In Proceedings of the The British International Conference on Databases 2021, London, United Kingdom, March 28, 2022 (CEUR Workshop Proceedings, Vol. 3163). CEUR-WS.org, 10--19. https:\/\/ceur-ws.org\/Vol-3163\/BICOD21_ paper_3.pdf"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815072.2815073"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335419"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3--319--25010--6_15"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/42201.42203"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359658"},{"key":"e_1_2_1_84_1","unstructured":"AgensGraph Team. 2020. AgensGraph: Powerful Graph Database. https:\/\/github.com\/bitnine-oss\/agensgraph.git"},{"key":"e_1_2_1_85_1","unstructured":"LlamaIndex Team. 2023. Knowledge Graph RAG Query Engine. https:\/\/docs.llamaindex.ai\/en\/stable\/examples\/ query_engine\/knowledge_graph_rag_query_engine.html"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/321921.321925"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543873.3585579"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V37I4.25588"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3298305"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00174"},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2023.3325973"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V33I01.33015329"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2309.04565"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2305.05920"},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2206.07278"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00181"},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV"},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/D17--1060"},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2022.EMNLP-MAIN.47"},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457237"},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1145\/3582016.3582029"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2307.13701"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2304.07063"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599805"},{"key":"e_1_2_1_105_1","volume-title":"Orca: A Distributed Serving System for Transformer-Based Generative Models. In 16th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2022","author":"Yu Gyeong-In","year":"2022","unstructured":"Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, and Byung-Gon Chun. 2022. Orca: A Distributed Serving System for Transformer-Based Generative Models. In 16th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2022, Carlsbad, CA, USA, July 11--13, 2022. USENIX Association, 521--538. https:\/\/www. usenix.org\/conference\/osdi22\/presentation\/yu"},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","unstructured":"Yuxuan Zhang Yuanxiang Li Yini Zhang YilinWang Yongshen Yang XianWei and Jianhua Luo. 2023. Missing-edge aware knowledge graph inductive inference through dual graph learning and traversing. Expert Syst. Appl. 213 Part (2023) 118969. https:\/\/doi.org\/10.1016\/J.ESWA.2022.118969","DOI":"10.1016\/J.ESWA.2022.118969"},{"key":"e_1_2_1_107_1","volume-title":"Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Zhang Zhanqiu","year":"2021","unstructured":"Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, and Feng Wu. 2021. ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6--14, 2021, virtual. 19172--19183. https: \/\/proceedings.neurips.cc\/paper\/2021\/hash\/a0160709701140704575d499c997b6ca-Abstract.html"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1145\/1247480.1247540"},{"key":"e_1_2_1_109_1","volume-title":"PetS: A Unified Framework for Parameter-Efficient Transformers Serving. In 2022 USENIX Annual Technical Conference, USENIX ATC 2022","author":"Zhou Zhe","year":"2022","unstructured":"Zhe Zhou, Xuechao Wei, Jiejing Zhang, and Guangyu Sun. 2022. PetS: A Unified Framework for Parameter-Efficient Transformers Serving. In 2022 USENIX Annual Technical Conference, USENIX ATC 2022, Carlsbad, CA, USA, July 11--13, 2022. USENIX Association, 489--504. https:\/\/www.usenix.org\/conference\/atc22\/presentation\/zhou-zhe"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677129","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3677129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T17:11:21Z","timestamp":1774977081000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":109,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,10,1]]}},"alternative-id":["10.1145\/3677129"],"URL":"https:\/\/doi.org\/10.1145\/3677129","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]}}}