From the 1 of 5 linked papers with an AI index.
5 papers
Fully Inductive Cardinality Estimation
Tim Schwabe, Lukas Ketzer, Maribel Acosta
The paper introduces FICE, a graph neural network‑based estimator that can predict the cardinalities of SPARQL Basic Graph Pattern queries on knowledge graphs it has never seen bef…
Gradient-Based Join Ordering
Tim Schwabe, Maribel Acosta
Join ordering is the NP-hard problem of selecting the most efficient order in which to evaluate joins (conjunctive, binary operators) in a database query. Because query execution p…
Effects of Distributional Biases on Gradient-Based Causal Discovery in the Bivariate Categorical Case
Tim Schwabe, Moritz Lange, Laurenz Wiskott +1
Gradient-based causal discovery shows great potential for deducing causal structure from data in an efficient and scalable way. Those approaches however can be susceptible to distr…
Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering
Tim Schwabe, Louisa Siebel, Patrik Valach +1
Question answering (QA) requires accurately aligning user questions with structured queries, a process often limited by the scarcity of high-quality query-natural language (Q-NL) p…
Neuro-Symbolic Query Optimization in Knowledge Graphs
Maribel Acosta, Chang Qin, Tim Schwabe
This chapter delves into the emerging field of neuro-symbolic query optimization for knowledge graphs (KGs), presenting a comprehensive exploration of how neural and symbolic techn…