Ontology-Based AnalyzeOntology-Based Analyzeof Chat Conversations. of Chat Conversations. An Urban Development An Urban Development
CaseCase
Stefan Trausan-Matu“Politehnica" University of Bucharest
andRomanian Academy Research Institute for Artificial Intelligence
Bucharest, Romania
[email protected] http://www.racai.ro/~trausan
ontologies in use form social groups
social groups form ontologies in use
(Chris Tweed)
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Philosophical paradigms in Philosophical paradigms in knowledge constructionknowledge constructionCognitive 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, Engeström, Stahl …) – folksonomies – social groups
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Examples of paradigm Examples of paradigm changechange
From Intelligent Tutoring Systems to Computer Supported Collaborative Learning
Web2.0 is the Social Web, not the Semantic Web
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Why a socio-cultural Why a socio-cultural paradigm?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
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Natural language Natural language understandingunderstandingRhetorics – 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
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The Social WebThe Social Web
FolksonomiesSocial NetworksDiscussion forumsChat conferences
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Knowledge building in Knowledge building in (small) groups(small) groupsStart from a common ground:
◦General ontology◦Domain ontology◦Linguistic practices
Rhetoric Pragmatics
Debate, negotiation – dialogueNew concepts are build
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Dialogism – Mikhail BakhtinDialogism – Mikhail Bakhtin
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• “… 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 genresInter-animation of voicesPolyphony
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Polyphony – a system for the Polyphony – a system for the analysis of chat logsanalysis 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
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Language Technology for Language Technology for Lifelong Learning (LTfLL)Lifelong Learning (LTfLL)EU FP7 Project, 2008-2011Netherlands, France, United Kingdom, Germany,
Romania, BulgariaTechnologies considered:
◦ Chat (conversation) analysis◦ Latent Semantic Analysis◦ Ontologies (semantics)◦ Folksonomies◦ Semantic Social Networks◦ Corpus linguistics
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Polyphony Polyphony Topic identificationBased on WordNet+domain ontologyNew concepts may be added in the
domain ontologyDiscourse identification – polyphonic
model (Trausan-Matu, Stahl & Sarmiento, 2006)
Graphical visualization of the chatEvaluation of the contributions of the
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Identification of Chat TopicsIdentification of Chat Topics
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XML or HTML chat logsTokenizationStop-words, emoticons and usual
abbreviations ( :) , :D , brb, thx, …) are eliminated
Semantic distances identified using WordNet and the domain ontology
Pattern (cue phrases) analysis
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Addition of new concepts in Addition of new concepts in the domain ontologythe domain ontology
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Discourse identificationDiscourse identification
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Implicit Links DiscoveringImplicit Links Discovering
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Text mining techniques:◦Pattern (cue phrases) analysis◦Co-reference analysis◦Lexical chains◦Heuristics
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Graphical Representation of the Graphical Representation of the ConversationConversation
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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). The strength of each utterance is
represented as a bar chart.
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Identification of participants’ Identification of participants’ contributionscontributions
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Oy axis – Value of contributions Ox axis – The number of the utterance
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Conclusions and future Conclusions and future directionsdirectionsThere 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
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