CSKG: The CommonSense Knowledge Graph

Commonsense Knowledge Representation I

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...

Commonsense reasoning, commonsense knowledge, and the SP theory of intelligence

This paper describes how the SP theory of intelligence, outlined in an Appendix, may throw light on aspects of commonsense reasoning (CSR) and commonsense knowledge (CSK) (together shortened to CSRK), as discussed in another paper by Ernest Davis and Gary Marcus (DM). The SP system has the generality needed for CSRK: Turing equivalence; the generality of information compression as the foundatio...

Commonsense Knowledge Representation II

Early attempts to implement systems that understand commonsense knowledge did so for very restricted domains. For example, the Planes system [Waltz, 1978] knew real world facts about a fleet of airplanes and could answer questions about them put to it in English. It had, however, no behaviors, could not interpret the facts, draw inferences from them or solve problems, other than those that have...

Commonsense Knowledge Base Completion

We enrich a curated resource of commonsense knowledge by formulating the problem as one of knowledge base completion (KBC). Most work in KBC focuses on knowledge bases like Freebase that relate entities drawn from a fixed set. However, the tuples in ConceptNet (Speer and Havasi, 2012) define relations between an unbounded set of phrases. We develop neural network models for scoring tuples on ar...

On Representing Commonsense Knowledge