Commonsense knowledge is a broad and challenging area of research which investigates our understanding the world as well human assumptions about reality. Deriving directly from subjective perception external world, it intrinsically intertwined with embodied cognition. reasoning linked to sense-making, pattern recognition framing abilities. This work presents new resource that formalizes cogniti...

Contextual knowledge is essential in answering questions given specific observations. While recent approaches to building commonsense knowledge bases via text mining and/or crowdsourcing are successful, contextual knowledge is largely missing. To address this gap, this paper presents SocialExplain, a novel approach to acquiring contextual commonsense knowledge from explanations of social conten...

A fundamental ability of humans is to utilize commonsense knowledge in language understanding and question answering. In recent years, many knowledge-enhanced Commonsense Question Answering (CQA) approaches have been proposed. However, it remains unclear: (1) How far can we get by exploiting external for CQA? (2) much potential has exploited current CQA models? (3) Which are the most promising ...

Unlike people, household robots cannot rely on commonsense knowledge when accomplishing everyday tasks. We believe that this is one of the reasons why they perform poorly in comparison to humans. By integrating extensive collections of commonsense knowledge into mobile robot’s knowledge bases, the work proposed in this paper enables robots to flexibly infer control decisions under changing envi...

Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impa...

Commonsense knowledge is useful for making Web search, local search, and mobile assistance behave in a way that the user perceives as “smart”. Most machine-readable knowledge bases, however, lack basic commonsense facts about the world, e.g. the property of ice cream being cold. This paper proposes a graph-based Markov chain approach to extract common-sense knowledge from Web-scale language mod...

The KNEXT system produces a large volume of factoids from text, expressing possibilistic general claims such as that ‘A PERSON MAY HAVE A HEAD’ or ‘PEOPLE MAY SAY SOMETHING’. We present a rule-based method to sharpen certain classes of factoids into stronger, quantified claims such as ‘ALL OR MOST PERSONS HAVE A HEAD’ or ‘ALL OR MOST PERSONS AT LEAST OCCASIONALLY SAY SOMETHING’ – statements str...

It is argued that set theory provides a powerful addition to commonsense reasoning, facilitating expression of meta-knowledge, names, and self-reference. Difficulties in establishing a suitable language to include sets for such purposes are discussed, as well as what appear to be promising solutions. Ackermann’s set theory as well as a more recent theory involving universal sets are discussed i...

Existing knowledge graph (KG) models for commonsense question answering present two challenges: (i) existing methods retrieve entities related to questions from the graph, which may extract noise and irrelevant nodes, (ii) there is a lack of interaction representation between entities. However, current mainly focus on retrieving relevant with some noisy nodes. In this paper, we propose novel re...