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<p>The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers.<br>The broad theme of this year's summer school was “<em>Declarative Artificial Intelligence: Knowledge, Rules, Logic.</em>"</br></p><p>The following eight lectures were presented during the school: Declarative AI for Industry: Methods, Applications, Trends; Ontologies vs Constraints; Termination of Reasoning; Compact Query Rewritings for Ontology Based Query Answering; Graph Queries and Description Logics; Controlled Query Evaluation in Description Logic Ontologies; Learning from Neural Networks with Queries and Counter Examples; and Proof-Theoretic Approaches in Logical Argumentation.</p>

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Reasoning Web. Declarative Artificial Intelligence: Knowledge, Rules, Logic

  • 教材

  • 发表日期：

    2025年

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摘要

The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers.
The broad theme of this year's summer school was “Declarative Artificial Intelligence: Knowledge, Rules, Logic."

The following eight lectures were presented during the school: Declarative AI for Industry: Methods, Applications, Trends; Ontologies vs Constraints; Termination of Reasoning; Compact Query Rewritings for Ontology Based Query Answering; Graph Queries and Description Logics; Controlled Query Evaluation in Description Logic Ontologies; Learning from Neural Networks with Queries and Counter Examples; and Proof-Theoretic Approaches in Logical Argumentation.