conceptnet n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
ConceptNet PowerPoint Presentation
Download Presentation
ConceptNet

Loading in 2 Seconds...

play fullscreen
1 / 25

ConceptNet - PowerPoint PPT Presentation


  • 193 Views
  • Uploaded on

ConceptNet. Outline. What is commonsense? Representative research on commonsense Open Mind Common Sense (OMCS) ConceptNet LiftNet StoryNet Practice Summary. What is commonsense?.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'ConceptNet' - edmund


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
outline
Outline
  • What is commonsense?
  • Representative research on commonsense
  • Open Mind Common Sense (OMCS)
  • ConceptNet
  • LiftNet
  • StoryNet
  • Practice
  • Summary
what is commonsense
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 beset 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, including:
      • 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
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 items
    • Used to generate ConceptNet, very large semantic network.
  • Other such projects
    • HowNet (Chinese Academy of Science)
    • FrameNet (Berkley)
open mind common sense omcs
Open Mind Common Sense (OMCS)
  • 750k NL assertions from 15k contributors (Initial stage)
  • ConceptNet
    • A semantic net built from these
    • 20 link types
conceptnet1
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
conceptnet function
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
conceptnet application
ConceptNet Application
  • Commonsense ARIA
    • Observes a user writing an e-mail and proactively suggests photos relevant to the user’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
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
  • 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
demos
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 recognition accuracy
  • http://web.media.mit.edu/~lieber/Lieberary/Lieberary.html
installation

Practice

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
conceptnetxmlrpcserver py

Practice

ConceptNetXMLRPCServer.py

import sys

import ConceptNetDB

import DocXMLRPCServer

pred_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_filename

c =ConceptNetDB.ConceptNetDB(None,pred_filename)

print "Starting XML-RPC Server"

port = 8000

xmlrpc = 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
wrapper modules

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_concepts, get_context, get_all_projections, project_affective, project_consequences, project_details, project_spatial
  • C++: Pipe 연결 (XML RPC는 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: Python과 C++ 직접 연동해서 사용하기 (문의: 송인지)
summary
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