Artificial Intelligent systems can benefit from incorporating commonsense knowledge as background, such as ice is cold (HASPROPERTY), chewing is a sub-event of eating (HASSUBEVENT), chair and table are typically found near each other (LOCATEDNEAR), etc. This kind of commonsense facts have been utilized in many downstream tasks, such as textual entailment [4, 1] and visual recognition tasks [29]...

Commonsense is of primary interest to AI research since the inception of the field. Traditionally, commonsense knowledge is gathered by using humans to create and insert it in knowledge bases. Automating the collection of commonsense from text that is freely available can reduce the cost and effort of creating large knowledge bases and can enable systems that dynamically adapt to current releva...

This special issue consists largely of expanded and revised versions of selected papers of the Fifth International Symposium on Logical Formalizations of Commonsense Reasoning (Common Sense 2001), held at New York University in May 2001.1,2 The Common Sense Symposia, first organized in 1991 by John McCarthy and held roughly biannually since, are dedicated to exploring the development of formal ...

This paper presents a semantically grounded method for extracting commonsense knowledge. First, commonsense rules are identified, e.g., one cannot see imaginary objects. Second, those rules are combined with a basic semantic representation in order to infer commonsense knowledge facts, e.g. one cannot see a flying carpet. Further combinations of semantic relations with inferred commonsense fact...

Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can expl...

Collecting commonsense knowledge from freely available text can reduce the cost and effort of creating large knowledge bases. For the acquired knowledge to be useful, we must ensure that it is correct, and that it carries information about its relevance and about the context in which it can be considered commonsense. In this paper, we design, and evaluate an online game that classifies, using t...

Imparting commonsense knowledge to computers enables a new class of intelligent applications better equipped to make sense of the everyday world and assist people with everyday tasks. While previous attempts have been made to acquire and structure commonsense knowledge, they have either been inadequate in capturing the breadth of knowledge needed for the enterprise, or their complicated represe...

Significant advances in artificial intelligence, including machines that play master level chess, or make medical diagnoses, highlight an intriguing paradox. While systems can compete with highly qualified experts in many fields, there has been much less progress in constructing machines that exhibit simple commonsense, the kind expected of any normally intelligent child. As a result, commonsen...

The objective of the TACIT (Toward Annotating Commonsense Inferences in Text) project is to identify all or most of the commonsense inferences needed to understand a small collection of short narrative texts; to characterize those inferences in terms of features in different dimensions; and to characterize the commonsense knowledge that underlies those inferences. The primary purpose of this an...