The general task of work in knowledge representation and reasoning (KRR) is, trivially, the representation of knowledge. The domain for which the representation task has been undertaken is, often, secondary to the description of the overall task as an exercise in knowledge representation. Thus, the task of representing and using mathematical, common sense, visual, language-based, and logical kn...

The pre-training models such as BERT have achieved great results in various natural language processing problems. However, a large number of parameters need significant amounts memory and the consumption inference time, which makes it difficult to deploy them on edge devices. In this work, we propose knowledge distillation method LRC-BERT based contrastive learning fit output intermediate layer...

One way to solve the knowledge acquisition bottleneck is to have ways to translate natural language sentences and discourses to a formal knowledge representation language, especially ones that are appropriate to express domain knowledge in sciences, such as Biology. While there have been several proposals, including by Montague (1970), to give model theoretic semantics for natural language and ...

studies related to the second language acquisition have frequently focused on the role of mother tongue. since the learner’s knowledge in the first language provides a basis to compare it with that of second language, it is assumed that being aware of the learner’s prior knowledge is affective in the syllabus design which itself is a crucial element in teaching. in this study personal pronouns ...

This document provides a set of general requirements for a knowledge representation system, and explores some of them in more detail. By knowledge representation system, we mean the complete package of: • a knowledge representation language, with clearly defined syntax and semantics. • an inference engine which can perform reasoning with a particular representation encoded in that language, to ...