1 code implementation • 7 Jun 2023 • Shuwen Liu, Bernardo Cuenca Grau, Ian Horrocks, Egor V. Kostylev
A key feature of Machine Learning approaches for KG completion is their ability to learn inference patterns, so that the predicted facts are the results of applying these patterns to the KG.
no code implementations • 29 May 2023 • David Tena Cucala, Bernardo Cuenca Grau, Boris Motik, Egor V. Kostylev
Although there has been significant interest in applying machine learning techniques to structured data, the expressivity (i. e., a description of what can be learned) of such techniques is still poorly understood.
no code implementations • 22 Sep 2022 • Dongzhuoran Zhou, Baifan Zhou, Jieying Chen, Gong Cheng, Egor V. Kostylev, Evgeny Kharlamov
One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology.
no code implementations • ICLR 2022 • David Jaime Tena Cucala, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik
In this paper, we propose a new family of GNN-based transformations of graph data that can be trained effectively, but where all predictions can be explained symbolically as logical inferences in Datalog---a well-known knowledge representation formalism.
no code implementations • ICLR 2020 • Pablo Barceló, Egor V. Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, Juan Pablo Silva
We show that this class of GNNs is too weak to capture all FOC2 classifiers, and provide a syntactic characterization of the largest subclass of FOC2 classifiers that can be captured by AC-GNNs.
no code implementations • 25 Apr 2018 • Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks
There has recently been an increasing interest in declarative data analysis, where analytic tasks are specified using a logical language, and their implementation and optimisation are delegated to a general-purpose query engine.
no code implementations • 19 May 2017 • Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks
Motivated by applications in declarative data analysis, we study $\mathit{Datalog}_{\mathbb{Z}}$---an extension of positive Datalog with arithmetic functions over integers.
no code implementations • 19 May 2017 • Charalampos Nikolaou, Egor V. Kostylev, George Konstantinidis, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks
The ontology is linked to the sources using mappings, which assign views over the data to ontology predicates.
no code implementations • 24 Apr 2015 • Bernardo Cuenca Grau, Evgeny Kharlamov, Egor V. Kostylev, Dmitriy Zheleznyakov
We study confidentiality enforcement in ontologies under the Controlled Query Evaluation framework, where a policy specifies the sensitive information and a censor ensures that query answers that may compromise the policy are not returned.