Publications (6)
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…
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…
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…
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…
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
Tim Schwabe, Maribel Acosta
Cardinality Estimation over Knowledge Graphs (KG) is crucial for query optimization, yet remains a challenging task due to the semi-structured nature and complex correlations of ty…
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…