TY - GEN
T1 - ANSWER
T2 - 7th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2023
AU - Qi, Zhixin
AU - Zhang, Haoran
AU - Wang, Hongzhi
AU - Chao, Zemin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Efficient access to knowledge graphs is identified as the basic premise to make full use of knowledge graphs. Since the query processing efficiency is mainly affected by index configuration, it is necessary to create effective indexes for knowledge graphs. However, none of existing studies of index selection focuses on the characteristics of knowledge graphs. To fill this gap, we propose an automatic index selector for knowledge graphs based on reinforcement learning, named ANSWER, to select an appropriate index configuration according to the historical workloads. However, it is challenging a learn a well-trained index selection model due to the large action space of reinforcement learning model and the requirement of lightweight embedding strategies. To address this problem, we first develop a novel predicate filter, which not only determines which vertical partitioning tables are valuable to create indexes, but also reduces the action space of model. Based on the filtered predicates, we derive an effective and lightweight encoder to not only embed the main features of workloads into the model, but also guarantee the high-efficiency of ANSWER. Experimental results on real-world knowledge graphs demonstrate the effectiveness of ANSWER in terms of knowledge graph query processing.
AB - Efficient access to knowledge graphs is identified as the basic premise to make full use of knowledge graphs. Since the query processing efficiency is mainly affected by index configuration, it is necessary to create effective indexes for knowledge graphs. However, none of existing studies of index selection focuses on the characteristics of knowledge graphs. To fill this gap, we propose an automatic index selector for knowledge graphs based on reinforcement learning, named ANSWER, to select an appropriate index configuration according to the historical workloads. However, it is challenging a learn a well-trained index selection model due to the large action space of reinforcement learning model and the requirement of lightweight embedding strategies. To address this problem, we first develop a novel predicate filter, which not only determines which vertical partitioning tables are valuable to create indexes, but also reduces the action space of model. Based on the filtered predicates, we derive an effective and lightweight encoder to not only embed the main features of workloads into the model, but also guarantee the high-efficiency of ANSWER. Experimental results on real-world knowledge graphs demonstrate the effectiveness of ANSWER in terms of knowledge graph query processing.
KW - Index selection
KW - Knowledge graph
KW - Reinforcement learning
KW - Vertical partitioning
UR - https://www.scopus.com/pages/publications/85192771317
U2 - 10.1007/978-981-97-2390-4_27
DO - 10.1007/978-981-97-2390-4_27
M3 - 会议稿件
AN - SCOPUS:85192771317
SN - 9789819723898
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 393
EP - 407
BT - Web and Big Data - 7th International Joint Conference, APWeb-WAIM 2023, Proceedings
A2 - Song, Xiangyu
A2 - Feng, Ruyi
A2 - Chen, Yunliang
A2 - Li, Jianxin
A2 - Min, Geyong
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 October 2023 through 8 October 2023
ER -