The document discusses using ontologies and natural language analysis to understand chat conversations. It proposes a socio-cultural paradigm where knowledge is constructed through dialogue within communities. An approach called "polyphony" is presented for identifying topics, discourse, and implicit links in chat logs using ontologies, wordnets and text mining. Graphical representations are generated to visualize the conversation structure and contributions of participants.

1. Ontology-Based Analyze of Chat Conversations. An Urban Development Case Stefan Trausan-Matu “ Politehnica" University of Bucharest and Romanian Academy Research Institute for Artificial Intelligence Bucharest, Romania stefan.trausan@cs.pub.ro http://www.racai.ro/~trausan

2. ontologies in use form social groups social groups form ontologies in use (Chris Tweed) 10 March 2009 Towntology Final Conference, Liege

3. Philosophical paradigms in knowledge construction Cognitive science : “ knowledge is in the mind of individual persons ” (Cyc, WordNet, FrameNet, Mikrokosmos, Sowa … ) - ontologies Socio-cultural : “ knowledge is social, is in communities where people enter in dialogs ” (Vygotsky, Engestr ö m , Stahl … ) – folksonomies – social groups 10 March 2009 Towntology Final Conference, Liege

4. Examples of paradigm change From Intelligent Tutoring Systems to Computer Supported Collaborative Learning Web2.0 is the Social Web, not the Semantic Web 10 March 2009 Towntology Final Conference, Liege

5. Why a socio-cultural paradigm? Cognitive science and artificial intelligence problems Natural language understanding Considering socio-cultural issues (including urbanism) Supporting dialogism Group knowledge construction Conflict resolution Reaching common meaning through dialog A theoretical foundation for the Social Web 10 March 2009 Towntology Final Conference, Liege

6. Natural language understanding Rhetorics – the systematic usage of synonyms Ex. (WordNet): car, auto, automobile, machine, motorcar or closely related words Ex. (WordNet): car => cruiser, police cruiser, patrol car, police car, prowl car, squad car Ambiguity Ex. (WordNet): car, railcar, railway car, railroad car Word senses depend on context, evolve in time and differ geographically Metaphors – “stocks are very sensitive creatures” 10 March 2009 Towntology Final Conference, Liege

7. The Social Web Folksonomies Social Networks Discussion forums Chat conferences 10 March 2009 Towntology Final Conference, Liege

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9. Knowledge building in (small) groups Start from a common ground: General ontology Domain ontology Linguistic practices Rhetoric Pragmatics Debate, negotiation – dialogue New concepts are build 10 March 2009 Towntology Final Conference, Liege

10. Dialogism – Mikhail Bakhtin “… Any true understanding is dialogic in nature” (Voloshinov-Bakhtin, 1973) Real life dialog should be the considered, not only written text (as Saussure recommended) Utterances (not sentences) should be the unit of analysis Carnivalesque Speech genres Inter-animation of voices Polyphony 10 March 2009 Towntology Final Conference, Liege

11. Polyphony – a system for the analysis of chat logs Used in several CSCL projects: Virtual Math Teams – NSF project, Drexel University, US (Trausan-Matu & Rebedea, 2009) K-Teams – Romanian CNSIS project LTfLL – FP7 IST project It may be used for any domain 10 March 2009 Towntology Final Conference, Liege

12. Language Technology for Lifelong Learning (LTfLL) EU FP7 Project, 2008-2011 Netherlands, France, United Kingdom, Germany, Romania, Bulgaria Technologies considered: Chat (conversation) analysis Latent Semantic Analysis Ontologies (semantics) Folksonomies Semantic Social Networks Corpus linguistics 10 March 2009 Towntology Final Conference, Liege

13. Polyphony Topic identification Based on WordNet+domain ontology New concepts may be added in the domain ontology Discourse identification – polyphonic model (Trausan-Matu, Stahl & Sarmiento, 2006) Graphical visualization of the chat Evaluation of the contributions of the participants 10 March 2009 Towntology Final Conference, Liege

14. Identification of Chat Topics XML or HTML chat logs Tokenization Stop-words, emoticons and usual abbreviations ( :) , :D , brb, thx, …) are eliminated Semantic distances identified using WordNet and the domain ontology Pattern (cue phrases) analysis 10 March 2009 Towntology Final Conference, Liege

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16. Addition of new concepts in the domain ontology 10 March 2009 Towntology Final Conference, Liege

17. Discourse identification 10 March 2009 Towntology Final Conference, Liege

18. Implicit Links Discovering Text mining techniques: Pattern (cue phrases) analysis Co - reference analysis Lexical chains Heuristics 10 March 2009 Towntology Final Conference, Liege

19. Graphical Representation of the Conversation For each participant in the chat, there is a separate horizontal line in the representation Each utterance is placed in the line corresponding to the issuer of that utterance, according to the emission time The explicit references among utterances are depicted using blue connecting lines The implicit references (deduced by the system) are represented using other colour (red or green). T he strength of each utterance is represented as a b a r chart. 10 March 2009 Towntology Final Conference, Liege

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22. Identification of participants’ contributions Oy axis – Value of contributions Ox axis – The number of the utterance 10 March 2009 Towntology Final Conference, Liege

23. Conclusions and future directions There are a need and a possibility for the integration of ontologies with social knowledge building The importance of context and negotiation: Usage of perspectives in ontologies 10 March 2009 Towntology Final Conference, Liege