Zobrazeno 1 - 10
of 648
pro vyhledávání: '"GOTTLOB, GEORG"'
One of the main challenges in the area of Neuro-Symbolic AI is to perform logical reasoning in the presence of both neural and symbolic data. This requires combining heterogeneous data sources such as knowledge graphs, neural model predictions, struc
Externí odkaz:
http://arxiv.org/abs/2403.02933
The aim of this study is to investigate Machine Unlearning (MU), a burgeoning field focused on addressing concerns related to neural models inadvertently retaining personal or sensitive data. Here, a novel approach is introduced to achieve precise an
Externí odkaz:
http://arxiv.org/abs/2402.05813
Existential rules form an expressive Datalog-based language to specify ontological knowledge. The presence of existential quantification in rule-heads, however, makes the main reasoning tasks undecidable. To overcome this limitation, in the last two
Externí odkaz:
http://arxiv.org/abs/2307.12051
Over the past decade, Knowledge Graphs have received enormous interest both from industry and from academia. Research in this area has been driven, above all, by the Database (DB) community and the Semantic Web (SW) community. However, there still re
Externí odkaz:
http://arxiv.org/abs/2307.06119
Autor:
Gottlob, Georg, Lanzinger, Matthias, Longo, Davide Mario, Okulmus, Cem, Pichler, Reinhard, Selzer, Alexander
Join queries involving many relations pose a severe challenge to today's query optimisation techniques. To some extent, this is due to the fact that these techniques do not pay sufficient attention to structural properties of the query. In stark cont
Externí odkaz:
http://arxiv.org/abs/2303.02723
Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing
Externí odkaz:
http://arxiv.org/abs/2209.10375
The chase procedure, originally introduced for checking implication of database constraints, and later on used for computing data exchange solutions, has recently become a central algorithmic tool in rule-based ontological reasoning. In this context,
Externí odkaz:
http://arxiv.org/abs/2204.10584
Modern applications combine information from a great variety of sources. Oftentimes, some of these sources, like Machine-Learning systems, are not strictly binary but associated with some degree of (lack of) confidence in the observation. We propose
Externí odkaz:
http://arxiv.org/abs/2202.01718
We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP). We show that learning guarded clauses is NP-complete and thus one step below the $\sigma^P_2$-comp
Externí odkaz:
http://arxiv.org/abs/2110.03624