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

stefan.trausan@cs.pub.ro 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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