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Found 23 papers, 5 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.
Seminaive Materialisation in DatalogMTL
DatalogMTL is an extension of Datalog with metric temporal operators that has found applications in temporal ontology-based data access and query answering, as well as in stream reasoning.
An Empirical Study of Retrieval-enhanced Graph Neural Networks
Graph Neural Networks (GNNs) are effective tools for graph representation learning.
Cardinality-Minimal Explanations for Monotonic Neural Networks
In recent years, there has been increasing interest in explanation methods for neural model predictions that offer precise formal guarantees.
MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years.
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.
Double-descent curves in neural networks: a new perspective using Gaussian processes
Double-descent curves in neural networks describe the phenomenon that the generalisation error initially descends with increasing parameters, then grows after reaching an optimal number of parameters which is less than the number of data points, but then descends again in the overparameterized regime.
The Window Validity Problem in Rule-Based Stream Reasoning
Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data.
Consequence-based Reasoning for Description Logics with Disjunction, Inverse Roles, Number Restrictions, and Nominals
We present a consequence-based calculus for concept subsumption and classification in the description logic ALCHOIQ, which extends ALC with role hierarchies, inverse roles, number restrictions, and nominals.
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.
Stream Reasoning in Temporal Datalog
In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities.
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.
Extending Consequence-Based Reasoning to SRIQ
Consequence-based calculi are a family of reasoning algorithms for description logics (DLs), and they combine hypertableau and resolution in a way that often achieves excellent performance in practice.
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.
Computing Horn Rewritings of Description Logics Ontologies
We study the problem of rewriting an ontology O1 expressed in a DL L1 into an ontology O2 in a Horn DL L2 such that O1 and O2 are equisatisfiable when extended with an arbitrary dataset.
Ontology Module Extraction via Datalog Reasoning
Module extraction - the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S - has found many applications in recent years.
Datalog Rewritability of Disjunctive Datalog Programs and its Applications to Ontology Reasoning
We study the problem of rewriting a disjunctive datalog program into plain datalog.
Acyclicity Notions for Existential Rules and Their Application to Query Answering in Ontologies
Existential rules are closely related to the Horn fragments of the OWL 2 ontology language; furthermore, several prominent OWL 2 reasoners implement CQ answering by using the chase to materialise all relevant facts.
Reasoning over Ontologies with Hidden Content: The Import-by-Query Approach
There is currently a growing interest in techniques for hiding parts of the signature of an ontology Kh that is being reused by another ontology Kv.
Completeness Guarantees for Incomplete Ontology Reasoners: Theory and Practice
Since ontologies and typical queries are often fixed at application design time, our approach allows application developers to check whether a reasoner known to be incomplete in general is actually complete for the kinds of input relevant for the application.
Computing Datalog Rewritings beyond Horn Ontologies
Rewriting-based approaches for answering queries over an OWL 2 DL ontology have so far been developed mainly for Horn fragments of OWL 2 DL.
