TY - GEN
T1 - Machine Learning for Query Optimization in Knowledge Graphs
AU - Acosta, Maribel
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Query optimization is a main component of graph databases and triple stores that host knowledge graphs (KGs). Traditional symbolic optimizers rely on heuristics and cost models that are prone to inaccuracies, leading to suboptimal execution plans. Recent advances in machine learning (ML) provide promising solutions to address these limitations by learning from data and queries to enhance cardinality estimation, cost prediction, and plan enumeration. This work surveys the emerging landscape of ML-based query optimization over KGs, including learned, neuro-symbolic, and (fully) neural approaches. We discuss the architecture and trade-offs of these systems, present preliminary results, and highlight open challenges for future research.
AB - Query optimization is a main component of graph databases and triple stores that host knowledge graphs (KGs). Traditional symbolic optimizers rely on heuristics and cost models that are prone to inaccuracies, leading to suboptimal execution plans. Recent advances in machine learning (ML) provide promising solutions to address these limitations by learning from data and queries to enhance cardinality estimation, cost prediction, and plan enumeration. This work surveys the emerging landscape of ML-based query optimization over KGs, including learned, neuro-symbolic, and (fully) neural approaches. We discuss the architecture and trade-offs of these systems, present preliminary results, and highlight open challenges for future research.
KW - neural networks
KW - neuro-symbolic AI
KW - query optimization
UR - https://www.scopus.com/pages/publications/105017378533
U2 - 10.1007/978-3-032-05727-3_18
DO - 10.1007/978-3-032-05727-3_18
M3 - Conference contribution
AN - SCOPUS:105017378533
SN - 9783032057266
T3 - Communications in Computer and Information Science
SP - 183
EP - 191
BT - New Trends in Database and Information Systems - ADBIS 2025 Short Papers, Workshops, Doctoral Consortium and Tutorials, 2025, Proceedings
A2 - Chrysanthis, Panos K.
A2 - Nørvåg, Kjetil
A2 - Stefanidis, Kostas
A2 - Zhang, Zheying
A2 - Quintarelli, Elisa
A2 - Zumpano, Ester
PB - Springer Science and Business Media Deutschland GmbH
T2 - Short papers, Doctoral Consortium and workshop papers which were presented at the 29th European Conference on New Trends in Databases and Information Systems, ADBIS 2025
Y2 - 23 September 2025 through 26 September 2025
ER -