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P. 1 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Real-Time Generation of Topic Maps.

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Presentation on theme: "P. 1 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Real-Time Generation of Topic Maps."— Presentation transcript:

1 p. 1 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Real-Time Generation of Topic Maps from Speech Streams TMRA'05 Internatioal Workshop on Topic Maps Research and Applications Karsten Böhm, Lutz Maicher University of Leipzig

2 p. 2 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams 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 p. 3 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams 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 p. 4 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Are there any elgs? 1. World without any sensory system Subject Equality Decision Chain From the child's perspective: (Elgs are sweet.) I caught always the same Subject, an elg. From the ranger's perspective: (Bernd needs a cow) I caught Lisa, Ud (fighting), and Bernd (in summer, in winter and as calf). From the zoologist's perspective: (Elgs are loners.) I caught two deers and three elgs. Decision about Subject Identity is a perspective dependent process under uncertainty whether Subject Stages caught at different occassions belong to the same Subject. Subject Identity 2. Sensory Systems come to stage, catching Subject Stages

5 p. 5 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Are there any elgs? 1. World without any sensory system Subject Equality Decision Chain 3. Documenting the impressions (from the rangers perspective) 2. Sensory Systems come to stage, catching Subject Stages (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) 4. Subject Equality is decided according to the governing SMD

6 p. 6 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Subject Equality Decision Chain Co-Location Objective: Subject Proxies indicating identical Subjects 1.World without any sensory system - How to make a qualified assertion about the very nature of Subjects? 2.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 3.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 4.Subject Equality is decided according to a SMD

7 p. 7 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams The Observation Principle (1.) Observe the information collections in interest (texts, video streams, etc.) and detect Subject Stages of Subjects in interest from the current perspective. (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... or how to create Topic Maps from digital domains? (2.) Decide about the Subject Identity of the observed Subject Stages.

8 p. 8 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams 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 (LinguaTecs 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 p. 9 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams SemantikTalk: Speech recognition and text Mining Sliders for configuration parameters (zooms) local context window Overview window (birds eye view) Window for add. Information (documents, pictures)

10 p. 10 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams The Semantic Talk System Speech recognition 1 (VoicePro) Speech recognition 1 (VoicePro) Speech recognition n Integration und Serialization Topic & Association Extraction Background Knowledge with Semantic Relations Visualization component abc foo cdf topic3 xyz

11 p. 11 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Semantic Talk creates indexes of speech streams we have to represent them as Topic Maps and use them for semantic information integration

12 p. 12 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams From RDF-output to LTM Fisichella 1 ST did observe a noticeable usage of the term "Fisichella" in the speech stream... Semantic Mapping between RDF-output and Topic Map using the Omnigator... [id7406 : id7276 {id7406, id3670, [[1]]} {id7406, id7650, [[160615]]} id7549( id7406 : id463, id464 : id2195 ) [id464] {id464, id1636, [[ ]]} {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 p. 13 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams 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) 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.... 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 p. 14 Böhm, Maicher Real-time Generation of Topic Maps from Speech Streams Discussion


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