RuleML+RR 2019, the 3rd International Joint Conference on Rules and Reasoning, is the leading event in the field of rule-based reasoning. Stemming from the synergy between the well-known RuleML and RR events, one of the main goals of this conference is to build bridges between academia and industry.
RuleML+RR 2019 aims to bring together rigorous researchers and inventive practitioners, interested in the foundations and applications of rules and reasoning in academia, industry, engineering, business, finance, healthcare and other application areas. It will provide a forum for stimulating cooperation and cross-fertilization between the many different communities focused on the research, development and applications of rule-based systems.
RuleML+RR 2019 will take place in Bolzano, Italy, on the 16-19 September 2019 and will be part of the BRAIN 2019, the Bolzano Rules and Artificial Intelligence Summit. With its special focus theme on "Beneficial AI", BRAIN 2019 brings together the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019), DecisionCAMP 2019, the Reasoning Web Summer School (RW 2019) and The 5th Global Conference on Artificial Intelligence (GCAI 2019).
- Abstract (updated): 24 May 2019
- Full paper (updated): 31 May 2019
- Conference 16-19 Sept 2019
For more information, see http://2019.ruleml-rr.org.
The W3C Workshop on Web Standardization for Graph Data - Creating Bridges: RDF, Property Graph and SQL was held, 4-6 March 2019, in Berlin, Germany. Its Session on Rules and Reasoning, moderated by Harold Boley and Dörthe Arndt, is captured in these minutes and this report. Harold presented introductory slides and Dörthe provided a proposal on Notation3 logic as a starting point to reason over RDF/LPG graphs. The discussion went, e.g., into the one-way return heuristic as a rule example in PSOA RuleML, based on the property-parameterized CONNECTION fact example in Cypher. Rules were a popular topic not only in this session but also in other sessions and throughout the workshop. In particular, participants were interested in the following kinds of rules:
- Rules for graph knowledge inference
- Rules for graph data transformation/mapping
- Rules for graph data validation
- Rules for RDF shapes, especially SHACL rules to derive RDF triples from asserted triples
- Rules for (controlled) natural language parsing and generation
For more information, see http://wiki.ruleml.org/index.php/Graph-Relational_Data,_Ontologies,_and_Rules.
RuleML+RR 2018, Luxembourg, 18-21 September, is the leading international joint conference in the field of rule-based reasoning, from foundations to technologies to applications
- Part of the Luxembourg Logic for AI Summit (LuxLogAI) "Methods and Tools for Responsible AI"
- Program can be seen at https://easychair.org/smart-program/LuxLogAI2018/RuleMLRR-program.html
- Registration is open via EasyChair (https://easychair.org/conferences/?conf=luxlogai2018) until 17 September 2018
Modern knowledge bases have matured to the extent of being capable of complex reasoning at scale. Unfortunately, wide deployment of this technology is still hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and skilled knowledge engineers are in short supply. A way around this problem could be to acquire knowledge from text. However, the current knowledge acquisition technologies for information extraction are not up to the task because logic reasoning systems are extremely sensitive to errors in the acquired knowledge, and existing techniques lack the required accuracy by too large of a margin. Because of the enormous complexity of the problem, controlled natural languages (CNLs) were proposed in the past, but even they lack high enough accuracy. Instead of tackling the general problem of text understanding, our interest is in a related, but different, area of knowledge authoring—a technology designed to enable domain experts to manually create formalized knowledge using CNL. Our KALM system approach adopts and formalizes the FrameNet methodology for representing the meaning, enables incrementally-learnable and explainable semantic parsing, and harnesses rich knowledge graphs like BabelNet in the quest to obtain unique, disambiguated meaning of CNL sentences. Our experiments show that this approach is 95.6% accurate in standardizing the semantic relations extracted from CNL sentences—far superior to alternative systems. For more information, see KALM Code , KALM short presentation .