ConceptNet

Outline

• What is commonsense?

• Representative research on commonsense

• Open Mind Common Sense (OMCS)

• ConceptNet

• LiftNet

• StoryNet

• Practice

• Summary

What is commonsense?

• Beliefs or propositions that seem, to most people, to be prudent and of sound judgment, without dependence upon esoteric knowledge

• Exhibiting native good judgment– Arrive home at a reasonable hour– Commonsense scholarship on the foibles of a genius– Unlearned and commonsensical countryfolk were capable of solving problems that b

eset the more sophisticated

• Commonsense reasoning is the branch of Artificial intelligence concerned with replicating human thinking

– Developing adequately broad and deep commonsense knowledge bases. – Developing reasoning methods that exhibit the features of human thinking, includin

g: • The ability to reason with knowledge that is true by default • The ability to reason rapidly across a broad range of domains • The ability to tolerate uncertainty in your knowledge

– Developing new kinds of cognitive architectures that support multiple reasoning methods and representations

Projects to Collect Commonsense

• Cyc– Started in 1984 by Dr. Doug Lenat– Developed by CyCorp, with 3.2 millions of assertions linking over 280.000 concepts

and using thousands of micro-theories.– Cyc-NL is still a “potential application”, knowledge representation in frames is quite

complicated and thus difficult to use.

• Open Mind Common Sense Project– Started in 2000 at MIT by Push Singh– WWW collaboration with over 20,123 registered users, who contributed 812,769 ite

ms– Used to generate ConceptNet, very large semantic network.

• Other such projects– HowNet (Chinese Academy of Science)– FrameNet (Berkley)

Open Mind Common Sense (OMCS)

• 750k NL assertions from 15k contributors (Initial stage)

• ConceptNet– A semantic net built from these– 20 link types

ConceptNet

• Common sense knowledge base with NLP capability– Much needs to be examined – Uncontrolled vocabulary, can be biased in terms of content; but seems quite reliable

knowledge

• Extracted automatically from common sense knowledge expressed in semi-structured NL sentences from OMCSNet (open mind common sense) – applying about 50 extraction rules

– ”The Effect of [falling off a bike] is [you get hurt].”– ”A lime is a very sour fruit” at OMCS is extracted into two assertions

• IsA (lime, fruit)• PropertyOf (lime, very sour)

• Commonsense knowledge covering aspects of everyday life– Spatial– Physical– Social– Temporal– psychological

Twenty Semantic Relation Types in ConceptNet (Liu and Singh, 2004)

THINGS (52,000 assertions)

IsA: (IsA "apple" "fruit") Part of: (PartOf "CPU" "computer") PropertyOf: (PropertyOf "coffee" "wet") MadeOf: (MadeOf "bread" "flour") DefinedAs: (DefinedAs "meat" "flesh of animal")

EVENTS (38,000 assertions)

PrerequisiteeventOf: (PrerequisiteEventOf "read letter" "open envelope") SubeventOf: (SubeventOf "play sport" "score goal") FirstSubeventOF: (FirstSubeventOf "start fire" "light match") LastSubeventOf: (LastSubeventOf "attend classical concert" "applaud")

AGENTS (104,000 assertions)

CapableOf: (CapableOf "dentist" "pull tooth")

SPATIAL (36,000 assertions)

LocationOf: (LocationOf "army" "in war")

TEMPORAL time & sequence

CAUSAL (17,000 assertions)

EffectOf: (EffectOf "view video" "entertainment") DesirousEffectOf: (DesirousEffectOf "sweat" "take shower")

AFFECTIONAL (mood, feeling, emotions) (34,000 assertions)

DesireOf (DesireOf "person" "not be depressed") MotivationOf (MotivationOf "play game" "compete")

FUNCTIONAL (115,000 assertions)

IsUsedFor: (UsedFor "fireplace" "burn wood") CapableOfReceivingAction: (CapableOfReceivingAction "drink" "serve")

ASSOCIATION K-LINES (1.25 million assertions)

SuperThematicKLine: (SuperThematicKLine "western civilization" "civilization") ThematicKLine: (ThematicKLine "wedding dress" "veil") ConceptuallyRelatedTo: (ConceptuallyRelatedTo "bad breath" "mint")

ConceptNet Example

ConceptNet Function

• Inference– Spreading activation: Node-activation radiating outward from an origin code

• GetContext (node)• GetAnalogousConcept (node)

– Graph traversal: • FindPathBetweenNodes (node1, node2)

• Support– Topic sensing – Query expansion– Semantic similarity of words– Lexical generalization– Thematic generalization

Snapshot of ConceptNet

ConceptNet Performance

ConceptNet Application

• Commonsense ARIA– Observes a user writing an e-mail and proactively suggests photos relevant to the us

er’s story– Bridges semantic gaps between annotations and the user’s story

• GOOSE– A goal-oriented search engine for novice users– Generate the search query

• MAKEBELIEVE– Story-generator that allows a person to interactively invent a story with the system– Generate causal projection chains to create storylines

• GloBuddy: A dynamic foreign language phrasebook

• AAA: Rrecommends products from Amazon.com by using ConceptNet to reason about a person’s goals and desires,creating a profile of their predicted tastes.

Reasoning in LifeNet

• LifeNet : A large-scale temporal graphical model expressed in terms of egocentric propositions of the form

– I am at a restaurant– I eat a sandwich– It is 3 pm– It is raining outside– I feel frightened

• Temporal reasoning– Prediction : Guess what might be true in the next moment– Elaboration : Guess what else might be true now– Explanation : Guess at what happened prior to the current event– Projection : Guess what series of events might follow– Filtering : Filter unlikely current states or events– Fixed-lag smoothing : Filter unlikely past states or events

StoryNet

• StoryNet builds on LifeNet and ConceptNet

• ConceptNet lays out the possibilities for ordering elements

– I want to drive a car– I ned gasoline– Gasoline can be found in a plane– A plane can be found in the sky

Collaboartion of Three Nets

Demos

• Video of Henry Lieberman's lecture on Applying Common Sense Reasoning in Interactive Applications: http://helix.media.mit.edu/ramgen/insite/exa/2003/lieber-2003-02-26.rm

• http://web.media.mit.edu/~lieber/Lieberary/Mondrian/Knowacq.mov– A User Interface for Knowledge Acquisition from Video

• http://agents.media.mit.edu/projects/voice/ – CS reasoning for better voice recognition– ConceptNet - to disambiguate phonetically similar words and improve overall recogni

tion accuracy

• http://web.media.mit.edu/~lieber/Lieberary/Lieberary.html

Installation

• 파이썬 설치 (python-2.4.3.msi)

• 컨셉넷 설치 (ConceptNet2.1.zip)– Montylingua: 문장 분석 모듈– ConceptNet

• 기본 실행 소스 (*.py)– ConceptNetGUI.py– ConceptNetXMLRPCServer.py – ConceptNetDB.py– ConceptNetNLTools.py

• 기본 DB 소스 (*.txt)– predicates_concise_kline.txt– predicates_concise_nonkline.txt– predicates_nonconcise_kline.txt– predicates_nonconcise_nonkline.txt

Practice

ConceptNetGUI.pyPractice

ConceptNetXMLRPCServer.py

import sysimport ConceptNetDBimport DocXMLRPCServerpred_filename = "predicates.txt"if len(sys.argv)>0 and sys.argv[-1][-1*len('.py'):].lower()!='.py': pred_filename = sys.argv[-1]print "Syntax: python ConceptNetXMLRPCServer.py [predicates_file]"print "Loading Predicates from %s..."%pred_filenamec =ConceptNetDB.ConceptNetDB(None,pred_filename)print "Starting XML-RPC Server"port = 8000xmlrpc = DocXMLRPCServer.DocXMLRPCServer(('',port))print "Now serving on localhost port %s!"%str(port)xmlrpc.register_introspection_functions()xmlrpc.register_instance(c)xmlrpc.register_instance(c.nltools)xmlrpc.serve_forever()

• ConceptNetDB.py (http:// 주소 :port), ex: http://165.132.140.237:8000

• ConceptNetNLTools.py (http:// 주소 /port), ex: http://165.132.140.237:8001

Practice

Wrapper Modules

• Python: 기본 동작 시스템– XML-RPC: 모듈간 통신 모듈

• C# – 소스

• CookComputing.XmlRpc.dll• ConceptNetEx2.exe

– 기본 제공 함수• guess_mood, guess_topic, guess_concept, summarize_document, tag, get_analogous_con

cepts, get_context, get_all_projections, project_affective, project_consequences, project_details, project_spatial

• C++: Pipe 연결 (XML RPC 는 Visual C++ 지원 안함 )– 소스 : concept_cpp_interface.h– 사용방법

• #include "concept_cpp_interface.h“• ConceptNetCPPInterface ci;• ci.setCommend(type, query);• ci.executeCN();• ci.mt, ci.tt, ci.ct, ci.at, ci.cxt, ci.pt, ci.aft, ci.cqt, ci.dt, ci.st

• 기타방법 : Python 과 C++ 직접 연동해서 사용하기 ( 문의 : 송인지 )

Practice

Example: 장소 질의 확장 Practice

Summary

• Little new work on the practical commonsense reasoning

• Building practical commonsense reasoning systems using unconventional techniques

– Representing knowledge in natural languageDistributing knowledge acquisition to non-experts via the World Wide Web

– Developing reasoning techniques that work successfully with large and imperfect knowledge bases.

• Lots of possibility with commonsense reasoning