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Found 9 papers, 3 papers with code
Optimised Storage for Datalog Reasoning
Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts.
CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation
We develop the CloudEval-YAML benchmark with practicality in mind: the dataset consists of hand-written problems with unit tests targeting practical scenarios.
Enhancing Datalog Reasoning with Hypertree Decompositions
In this paper, we provide algorithms that exploit hypertree decompositions for the materialisation and incremental evaluation of Datalog programs.
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.
OWL2Vec*: Embedding of OWL Ontologies
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web.
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.
Datalog Reasoning over Compressed RDF Knowledge Bases
Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules.
Modular Materialisation of Datalog Programs
The semina\"ive algorithm can materialise all consequences of arbitrary datalog rules, and it also forms the basis for incremental algorithms that update a materialisation as the input facts change.
Optimised Maintenance of Datalog Materialisations
The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules 'backwards' by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries.
