A framework for data, knowledge, and reasoning: Datalog +/-

It contains rules, facts, and queries, where the rules consist of: (1) a Datalog + fragment of the rst-order RCC relation denitions; (2) additional rules expressing part of the

This paper describes the design and implementation of the recommender applica- tion framework developed as a part of the Ontology Application Management (OAM) Framework [3], which

Moreover, we show that due to the restricted form of its axioms, the DL-Lite R language allows us to give a less involved datalog encoding in which reasoning on negative information

For future work, we are working on identifying a more general class of datalog rewritable existential rules, optimising our rewriting algorithm and implementation, and

In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Data- log has to

To overcome the obstacles mentioned above, it turns out to be appropriate to treat the KG completion problem as an unsupervised relational learning task, and exploit algorithms

The Datalog+/– languages are derived from Datalog by allowing existentially quantified variables, equality, and the falsum in rule heads, and, at the same time, by enforcing

They are used as any other class in our framework, though their particular semantics is expressed in dlgp by the two following dlgp statements that must be present in every