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ConceptNet

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

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ConceptNet

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  1. ConceptNet

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

  3. 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

  4. 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)

  5. Open Mind Common Sense (OMCS) • 750k NL assertions from 15k contributors (Initial stage) • ConceptNet • A semantic net built from these • 20 link types

  6. 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

  7. ConceptNet Example

  8. 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

  9. Snapshot of ConceptNet

  10. ConceptNet Performance

  11. 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.

  12. 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

  13. 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

  14. Collaboartion of Three Nets

  15. 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

  16. 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

  17. Practice ConceptNetGUI.py

  18. 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

  19. 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)

  20. Practice Example: 장소 질의 확장

  21. 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

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