Consolidating Commonsense Knowledge

06/10/2020
by   Filip Ilievski, et al.
0

Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities. At present, a number of valuable commonsense knowledge sources exist, with different foci, strengths, and weaknesses. In this paper, we list representative sources and their properties. Based on this survey, we propose principles and a representation model in order to consolidate them into a Common Sense Knowledge Graph (CSKG). We apply this approach to consolidate seven separate sources into a first integrated CSKG. We present statistics of CSKG, present initial investigations of its utility on four QA datasets, and list learned lessons.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/21/2020

CSKG: The CommonSense Knowledge Graph

Sources of commonsense knowledge aim to support applications in natural ...
research
06/08/2022

Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning

Commonsense reasoning systems should be able to generalize to diverse re...
research
08/18/2020

Commonsense Knowledge in Wikidata

Wikidata and Wikipedia have been proven useful for reason-ing in natural...
research
01/12/2021

Dimensions of Commonsense Knowledge

Commonsense knowledge is essential for many AI applications, including t...
research
09/20/2021

Commonsense Knowledge in Word Associations and ConceptNet

Humans use countless basic, shared facts about the world to efficiently ...
research
12/30/2019

Using ConceptNet to Teach Common Sense to an Automated Theorem Prover

The CoRg system is a system to solve commonsense reasoning problems. The...
research
03/27/2013

On Implementing Usual Values

In many cases commonsense knowledge consists of knowledge of what is usu...

Please sign up or login with your details

Forgot password? Click here to reset