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Found 9 papers, 1 papers with code
Revisiting Inferential Benchmarks for Knowledge Graph Completion
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.
On the Correspondence Between Monotonic Max-Sum GNNs and Datalog
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.
Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding
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.
Explainable GNN-Based Models over Knowledge Graphs
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.
The Logical Expressiveness of Graph Neural Networks
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.
Stratified Negation in Limit Datalog Programs
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.
Foundations of Declarative Data Analysis Using Limit Datalog Programs
Motivated by applications in declarative data analysis, we study $\mathit{Datalog}_{\mathbb{Z}}$---an extension of positive Datalog with arithmetic functions over integers.
The Bag Semantics of Ontology-Based Data Access
The ontology is linked to the sources using mappings, which assign views over the data to ontology predicates.
Controlled Query Evaluation for Datalog and OWL 2 Profile Ontologies
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.
