ConceptNet - a pratical commonsense reasoning

tool-kit

H Liu and P Singh

MIT Media Lab

Speaker: Yi-Ching(Janet) Huang

Introduction

• ConceptNet– Freely available commonsense knowledge

base– Natual-language-processing tool-kit

• It supports many practical textual-reasoning tasks over real-world documents

Outline

• Comparison of ConceptNet, Cyc, and WordNet

• History, Construction and Structure

• Various contextual reasoning tasks

• Quantitative and Qualitative Analysis

• Conclusion

Comparison

Database content

Resource Capabilities

ConceptNet

(2002)

Commonsense

OMCS (from the public)

(automatic)

Contextual inference

WordNet

(1985)

Semantic Lexicon

Expert

(manual)

Lexical categorisation & word-similarity

Cyc

(1984)

Commonsense

Expert

(manual)

Formalized logical reasoning

History of ConceptNet

Cyc OMCSCRIS/ OMCSNet ConceptNet

1984 2000 2002 2004

Building ConceptNet

• 3 phases– Extraction phase

• Extract from OMCS corpus• English sentence -> binary-relation assertion

– Normalization phase– Relaxation phase

• Produce “inferred assertion”• Improve the connectivity of the network

Structure of the ConceptNet knowledge base

• 1.6 million assertions (1.25 million are k-lines)

• twenty relation-types

Practical commonsense reasoning

• An integrated natural-language-processing engine– MontyLingua– Text document --> VSOO frames

• Reasoning capabilities– Node-level reasoning– Document-level reasoning

Node-level reasoning

• Contextual neighborhoods– Spreading activation

• Analogy-making

• Projection

Document-level reasoning

• Topic-gisting

• Disambiguation and classification

• Novel-concept identification

• Affect sensing

Characteristics and quality

• ConceptNet’s reasoning abilities hinge largely on the quality of its knowledge

Characteristics of the KB

• The histogram of nodal word-lengths

70%

Characteristic of the KB

• Average frequency an assertion is uttered of inferred

90% uttered

Characteristics of the KB

• The connectivity of nodes in ConceptNet by measuring nodal edge-density

Quality of the knowledge

• Two dimensions of quality of ConceptNet, rated by human judges

Applications of ConceptNet

ARIA

GOOSE

GloBuddy

MAKEBELIEVE

AAA OMAdventure

Emotus Ponens

Overhear

Bubble Lexicon

LifeNet

SAM

What Would They Think?

Commonsense Predictive Text Entry

Commonsense Investing

Metafor

Commonsense ARIA

• Analyize E-mail’s content and suggest the related photos

Emotus Ponens

MakeBelieve

Conclusion

• ConceptNet is presently the largest freely commonsense database

• Support many practical textual-reasoning tasks

• Goodness– Easy to use– Simple structure of WordNet– Good for practical commonsense reasoning