AI*IA 2012 PAI Workshop OTTHO

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OTTHO (On the Tip of my THOught) is an information seeking system designed for solving a language game which demands knowledge covering a broad range of topics, such as movies, politics, literature, history, proverbs, and popular culture. OTTHO implements a knowledge infusion process in order to provide a background knowledge which allows a deeper understanding of the items it deals with. The knowledge infusion process consists of two steps: 1) extracting and modeling relationships between words extracted from several knowledge sources; 2) reasoning on the induced models in order to generate new knowledge. OTTHO extracts knowledge from several sources, such as a dictionary, news, Wikipedia, and various unstructured repositories and creates a memory of linguistic knowledge and world facts. Starting from some external stimuli (e.g. words) depending on the task to be accomplished, the reasoning mechanism allows retrieving some specific pieces of knowledge from the memory created in the previous step. OTTHO has a great potential for more practical applications besides solving a language game. It could be used for implementing an alternative paradigm for associative information retrieval, for computational advertising and recommender systems.

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AI*IA 2012 PAI Workshop OTTHO

  1. 1. Semantic 1/128 Web Access and Personalization research group http://www.di.uniba.it/~swap OTTHO: An Artificial Player for a Complex Language Game Giovanni Semeraro, Pasquale Lops, Marco de Gemmis, Pierpaolo BasilePopularize Artificial IntelligenceAI*IA Workshop and Prize forcelebrating 100th anniversary of Alan Turings birthRome, 15th June, 2012
  2. 2. Knowledge Infusion (KI): Motivation Humans typically have the linguistic and cultural experience to comprehend the meaning of a text • abstraction from words to concepts • recall associations between concepts by exploiting background knowledge (associative retrieval)
  3. 3. Knowledge Infusion  How to realize these capabilities into machines?  Knowledge Infusion (KI) = The process of providing a system with the background knowledge which allows a deeper understanding of the information it deals with • which knowledge sources? • which reasoning strategies?  KI implemented in the domain of language games • fundamental role of word meanings and reasoning capabilities  OTTHO: On the Tip of my THOught [Sem09, Sem11] • an artificial player based on KI for the Guillotine game[Sem09] G. Semeraro, P. Lops, P. Basile, and M. de Gemmis. On the Tip of my Thought: Playing the Guillotine Game.In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), 1543-1548, MorganKaufmann, 2009.[Sem11] G. Semeraro, M. de Gemmis, P. Lops, P. Basile. Knowledge Infusion from Open Knowledge Sources: anArtificial Player for a Language Game, IEEE Intelligent Systems. In Press.
  4. 4. The game SIN APPLE is the symbol of the original sin in the Book of GenesisNEWTON Isaac Newton discovered the gravity by means of an APPLEDOCTOR is a proverb TRY! “an APPLE a day takes the doctor away” PIE APPLE pie is a fruit cakeNEW YORK new york city is called “the big APPLE”
  5. 5. Knowledge Infusion: an NLP-AI task  NLP techniques process the unstructured information stored in several (open) knowledge sources • the memory of the system  Spreading Activation [And83] as the reasoning mechanism • the brain of the system Cultural and Linguistic Background Knowledge[And83] J. R. Anderson. A Spreading Activation Theory of Memory. Journal of Verbal Learning and VerbalBehavior, 22:261–295, 1983.
  6. 6. Knowledge Sources Encyclopedia: the Italian version of Wikipedia Dictionary – the De Mauro Paravia Italian on-line dictionary Movies: descriptions of Italian movies crawled from IMDb Books crawled from the web Songs crawled from the webProverbs and Aphorisms: Compound forms: groups of words that often go togetherthe Italian version of having a specific meaning, e.g. “artificial intelligence” –Wikiquote crawled from the web
  7. 7. Encoding a Knowledge Source as Cognitive Unit Repository Information in long term memory of human beings is encoded as Cognitive Units – ACT theory [And83] Cognitive Unit (CU) = textual description of a concept • HEAD = words identifying the concept represented by the CU • BODY = words describing the concept • [HEAD | BODY][And83] J. R. Anderson. A Spreading Activation Theory of Memory. Journal of Verbal Learning and Verbal Behavior,22:261–295, 1983.
  8. 8. Encoding a Knowledge Source as Cognitive Unit Repository HEAD BODYArtificial 0.77 AI 1.22 intelligence 1.10 computer 0.99Intelligence 1.22 engineering 0.65 machine 0.55 mind 0.49 … … … …
  9. 9. CU repositories can be queried Query: Machine Intelligence Relevant [artificial 0.77 intelligence 1.22 CUs | AI 1.22 intelligence 1.10 0.85 computer 0.99 0.52 relevance engineering 0.65 score machine 0.55Cognitive mind 0.49 0.46 Units . . . . . .
  10. 10. What does OTTHO know about clues? CLUE#1 CLUE#2 CLUE#3 CLUE#4 CLUE#5 KNOWLEDGE REPOSITORY ...Wikipedia Dictionary Movies Wikiquote SOL-WORD1 SOL-WORD2SPREADING … CANDIDATEACTIVATION NET SOLUTIONS LIST
  11. 11. Building the Spreading Activation Network - SANNodes represent CUs or words associated with CUsLinks labeled with weights • Link  association between CU and words • Weight  strength of the associationSAN populated by running n expansion phases starting from clues
  12. 12. SAN for 2 clues and 2 knowledge sources Newton Sin OTTHO - KNOWLEDGE REPOSITORY Wikipedia DictionaryCU14 = [isaac 1.34 newton 1.55 | gravitation 1.66 apple 1.52] 0.92CU16 = [newton 1.55 | unit 0.77 force 0.65 mechanics 0.35] 0.75 relevanceCU7 = [newton 1.87 | unit 1.02 force 0.75] 0.72 scoresCU2 = [sin 1.93 | Christianity 1.62 Genesis 1.53 apple 1.45] 0.65CU24 = [sin 1.54 | transgression 0.54 divine 0.45 law 0.44] 0.55
  13. 13. Spreading over the SAN newton sin 0.72 0.55 CU2 CU24 CU7 CU16 CU14 0.83 0.28 0.48 0.900.74 0.37 0.79 law 0.85 Christianity 0.91 unit force transgression isaac 0.86 0.18 apple 0.29 gravitation mechanics Genesis divine
  14. 14. Spreading over the SAN newton sin 0.72 0.55 CU2 CU24 CU7 CU16 CU14 0.83 0.28 0.48 0.900.74 0.37 0.79 law 0.85 Christianity 0.91 unit force transgression isaac 0.86 0.18 apple 0.29 gravitation mechanics Genesis divine
  15. 15. Spreading over the SAN newton sin 0.72 0.55 CU2 CU24 CU7 CU16 CU14 0.83 0.28 0.48 0.900.74 0.37 0.79 law 0.85 Christianity 0.91 unit force transgression isaac 0.86 0.18 apple 0.29 gravitation mechanics Genesis divine
  16. 16. Spreading over the SAN newton sin 0.72 0.55 CU2 CU24 CU7 CU16 CU14 0.83 0.28 0.48 0.900.74 0.37 0.79 Christianity law 0.91 0.85 unit force transgression isaac 0.86 0.18 apple 0.29 gravitation mechanics Genesis divine STOP Labels of the most “active” nodes included in CSL CSL = [apple, unit, gravitation, force, Christianity]
  17. 17. ConclusionKnowledge Infusion modeled as associative retrieval • knowledge representation based on Cognitive Units • reasoning process performed by Spreading ActivationTRY OTTHO during demo session!!!

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