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Real-Time Generation of Topic Maps from Speech Streams

TMRA'05 Internatioal Workshop on Topic Maps Research and Applications 06.10.2005 Karsten Böhm, Lutz Maicher University of Leipzig boehm|maicher@informatik.uni-leipzig.de. Real-Time Generation of Topic Maps from Speech Streams. Introduction. Topic Maps are means for

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Real-Time Generation of Topic Maps from Speech Streams

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  1. TMRA'05 Internatioal Workshop on Topic Maps Research and Applications06.10.2005 Karsten Böhm, Lutz Maicher University of Leipzig boehm|maicher@informatik.uni-leipzig.de Real-Time Generation of Topic Maps from Speech Streams

  2. Introduction • Topic Maps are means for • representing (powerful) indexes • of any information collection • for semantic information integration • Our goal: • real-time generation of conceptual indexes of speech streams, • represented as Topic Maps • for integration with other information systems

  3. How to create Topic Maps • Topic Maps are a semantic technology ... ...only in the perspective of information integration • holding the Co-location objective always true • „Subject Proxies indicating identical Subjects has to be viewed as merged ones“ • Subject Equality Decision Approach • Subject Viewing Approach • We have to represent the created indexes to hold the Co-location objective true in the perspective of the creator .... ... and therefore we need a theoretic fundament.

  4. 1. World without any sensory system 2. Sensory Systems come to stage, catching Subject Stages Are there any elgs? Subject Identity Decision about Subject Identity is a perspective dependent process under uncertainty whether Subject Stages caught at different occassions belong to the same Subject. Subject Equality Decision Chain From the child's perspective: („Elgs are sweet.“) I caught always the same Subject, an elg. From the zoologist's perspective: („Elgs are loners.“) I caught two deers and three elgs. From the ranger's perspective: („Bernd needs a cow“) I caught Lisa, Ud (fighting), and Bernd (in summer, in winter and as calf).

  5. 1. World without any sensory system Are there any elgs? Subject Equality Decision Chain 2. Sensory Systems come to stage, catching Subject Stages 3. Documenting the impressions (from the rangers perspective) 4. Subject Equality is decided according to the governing SMD (1) Subjectness: I'm only interested in Lisa, Ud, and Bernd not in snow, trees. (2) Creating Subject Proxies for the current Subject Stages of Lisa, Ud and Bernd (3) Try to document the decision about the Subject Identity of the current Subject Stage by the given means of the governing SMD ontology, TMV ontology and TMV vocabulary. Subject Identity of Subject Stages is mapped to Subject Indication of the Subject Proxy (4) Document all further information observed about the Subject Stage. (Documenting = modelling = loosing information)

  6. Subject Equality Decision Chain Co-Location Objective: Subject Proxies indicating identical Subjects • World without any sensory system • How to make a qualified assertion about the very nature of Subjects? • Sensory systems come on stage, catching Subject Stages • Never Subjects, only Subject Stages(see Quine) are observed • Subject Identity = Subject Stages caught at different occassions belong to the same Subject (see Vatants hubjects) • perspective dependent (see Biezunsky) • decision process under uncertainty • Documenting the impressions from a perspective • Subjectness in the current perspective • observations are documented restricted by the available vocabulary (SMD Ontology, TMV ontology, TMV vocabulary) • Decision about Subject Identity is documented according to the governing Subject Indication Approach • Subject Equality is decided according to a SMD

  7. The Observation Principle .. or how to create Topic Maps from digital domains? (1.) Observe the information collections in interest (texts, video streams, etc.) and detect Subject Stages of Subjects in interest from the current perspective. (2.) Decide about the Subject Identity of the observed Subject Stages. (3.) Create a Subject Proxy for each Subject Stage in interest. (4.) Document the decision about the Subject Identity of the current Subject Stage by the given means of the governing SMD ontology, TMV ontology and TMV vocabulary. ( ... and with respect to all expected Subject Equality Decision Approaches applied later to this Subject Proxy) (5.) Document all further information observed about the Subject Stage by the given means of the governing SMD ontology, TMV ontology and TMV vocabulary.

  8. The Semantic Talk System • Focusses on the support of group oriented conversation • Implementation of a “minimal invasive” IT-solution • Application for interviewing scenarios, innovation processes and early stages of product development • Semantic Talk creates powerful, conceptual indexes of Speech Streams in real-time • Combines speech recognition (LinguaTec’s VoicePro) with Text Mining algorithms • Provides dynamic visualization (extended Version of TouchGraph) • Networked application with multiple clients • Provides a generic RDF-export • Cooperation with University Duisburg-Essen, ISA Informationssysteme GmbH

  9. SemantikTalk: Speech recognition and text Mining Overview window (birds eye view) Window for add. Information (documents, pictures) Sliders for configuration parameters (zooms) local context window

  10. abc cdf foo topic3 xyz Visualization component Speech recognition 1 (VoicePro) Speech recognition n Background Knowledge with Semantic Relations Topic & Association Extraction Integration und Serialization The Semantic Talk System

  11. Semantic Talk creates indexes of speech streams we have to represent them as Topic Maps and use them for semantic information integration

  12. From RDF-output to LTM ST did observe a noticeable usage of the term "Fisichella" in the speech stream ... <st:node rdf:ID="node_Fisichella"> <st:ID>160615</st:ID> <st:label>Fisichella</st:label> <st:nodelevel>1</st:nodelevel> <st:ref_wort_nr rdf:resource="http://www.tt.de/dtd/st/pap#node_160615"/> <st:variant st:index="3" st:type="4" st:weight="0.3176"/> </st:node> Semantic Mapping between RDF-output and Topic Map using the Omnigator ... [id7406 : id7276 = "Fisichella" @"http://www.texttech.de/dtd/st/pap#node_160615" @"http://www.texttech.de/dtd/st/pap#node_Fisichella"] {id7406, id3670, [[1]]} {id7406, id7650, [[160615]]} id7549( id7406 : id463, id464 : id2195 ) [id464] {id464, id1636, [[0.31766722453166335]]} {id464, id4378, [[3]]} {id464, id787, [[4]]} ... and this 'noticeable usage of the term Fisichella' becomes the Subject in the Topic Map. (Subject Identity => the same algorithms observes the 'noticable usage' twice)

  13. [id @"http://www.formula1-fansite.org/Fisichella " @"http://www.texttech.de/dtd/st/pap#node_Fisichella"] from the mapping perspective the same Subject is caught, - if Semantic Talk observes a noticeable usage of the term 'Fisichella' - if the motor-sport Topic Map caught a person with the same name. Integration with other Topic Maps ... Starting point: Integration with an other Topic Map created by the observation principle (for example a motor-sport Topic Map) - a mapping Topic Map is needed (which should be created under the observation principle, too) ... to allow more accurate mapping decisions, it seems to be necessary that the creation process of a Topic Map needs to be documented, too.

  14. Discussion

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