In a speech community, people utilize their communicative competence which they have acquired from their society as part of their distinctive sociolinguistic identity. They negotiate and share meanings, because they have commonsense knowledge about the world, and have universal practical reasoning. Their commonsense knowledge is embodied in their language. Thus, not only does social life depend...

in a speech community, people utilize their communicative competence which they have acquired from their society as part of their distinctive sociolinguistic identity. they negotiate and share meanings, because they have commonsense knowledge about the world, and have universal practical reasoning. their commonsense knowledge is embodied in their language. thus, not only does social life depend...

Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual and planning. Consequently, sources that include commonsense have been designed constructed over the past decades. Recently, focus has on large text-based sources, which facilitate easier integration with neural (language) models application to textual tasks, typically at expense ...

Large-scale commonsense knowledge bases empower a broad range of AI applications, where the automatic extraction (CKE) is fundamental and challenging problem. CKE from text known for suffering inherent sparsity reporting bias in text. Visual perception, on other hand, contains rich about real-world entities, e.g., (person, can_hold, bottle), which can serve as promising sources acquiring ground...

In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic from FrameNet, which schematically describes situations, objects and events. (2) world YAGO, Freebase, DBpedia Wikidata, provides explicit about specific instances. (3) commonsense ConceptNet, implicit general facts. To model these different consistently, introduce three-lev...

Sources of commonsense knowledge support applications in natural language understanding, computer vision, and graphs. Given their complementarity, integration is desired. Yet, different foci, modeling approaches, sparse overlap make difficult. In this paper, we consolidate by following five principles, which apply to combine seven key sources into a first integrated CommonSense Knowledge Graph ...

Question generation (QG) is to generate natural and grammatical questions that can be answered by a specific answer for given context. Previous sequence-to-sequence models suffer from problem asking high-quality requires commonsense knowledge as backgrounds, which in most cases not learned directly training data, resulting unsatisfactory deprived of knowledge. In this paper, we propose multi-ta...