From RuleML Wiki
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 .