Context-Driven   Semantic Multimedia        Search                       Hannover, 19/03/2013                           Dr...
Context-Driven Semantic                                                         Multimedia Search                         ...
3            Searching the Web    Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam
4    Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam
4    Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam
Google Multimedia SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, Workshop ,Corporate Semantic Web...
How does Google find Multimedia?            ‣Google Multimedia Search relies on text-based               metadata and link ...
Seach by Media ContentHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
The Ordinary Archive is a Small World...                                                                                  ...
The Ordinary Archive is a Small World...                                                                                  ...
But, wouldn‘t it be nice, if.....     ...but maybe you are also interested in        - George Melies (2 videos)        - M...
How to Search in               Multimedia Archives?Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2...
Searching a       Multimedia Archive                              Step 1: Digitization of analog media                    ...
vfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn   Jörg Waitelonis, Hasso-Plattner-Instit...
Today: Manual Annotationvfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn   Jörg Waiteloni...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
(Selected) Automated Media Analysis  text / images                                             audio-                     ...
Structural Video Analysis              • Decomposition of time-based media into meaningful media                fragments ...
Video Optical Character Recognition (OCR)                     • Video OCR is much more difficult                       than...
Video Face Detection, Tracking & Clustering                                                         • Face Detection      ...
Visual Context Detection• Adaption of traditional ,Bag of Words‘  approach from text retrieval• Image is expressed as vect...
How to Determine the Meaning of Metadata?     • Authoritative Metadata                                                    ...
Annotation of Audiovisual Data           • Multimedia data with spatiotemporal AnnotationsMetadata Extraction             ...
,Neil Armstrong‘ is more than just a character string
,Neil Armstrong‘ is more than just a character string                                  Neil Armstrong
,Neil Armstrong‘ is more than just a character string                                  Neil Armstrong                     ...
,Neil Armstrong‘ is more than just a character string                                    Neil Armstrong                   ...
,Neil Armstrong‘ is more than just a character string                                    Neil Armstrong                   ...
,Neil Armstrong‘ is more than just a character string                                    Neil Armstrong                   ...
,Neil Armstrong‘ is more than just a character string                                    Neil Armstrong                   ...
,Neil Armstrong‘ is more than just a character string                                    Neil Armstrong         Entities  ...
,Neil Armstrong‘ is more than just a character string                                    Neil Armstrong                Ent...
,Neil Armstrong‘ is more than just a character string                                         Neil Armstrong              ...
,Neil Armstrong‘ is more than just a character string                      Juri Gagarin                      is a         ...
Where does the knowledge come from...?
Where does the knowledge come from...?
Where does the knowledge come from...?
But what, if there is no trivial unique identification?   Web of Data = Linked Open Data
But what, if there is no trivial unique identification?   Web of Data = Linked Open Data                                   ...
Arms                   tron                                                                                               ...
Arms                   Arm                        tron                                             stron                  ...
Understanding requires Context  Web of Data = Linked Open Data               Armstrong
Understanding requires Context  Web of Data = Linked Open Data               Armstrong                    Moon
Understanding requires Context  Web of Data = Linked Open Data                                 Eagle               Armstro...
Understanding requires Context  Web of Data = Linked Open Data Space                           Eagle               Armstro...
24      Semantic Analysis                                                                                                 ...
24      Semantic Analysis                                                                                                 ...
24      Semantic Analysis                                                                                                 ...
24             Semantic Analysis                                                                                          ...
24             Semantic Analysis                                                                                          ...
24             Semantic Analysis                                                                                          ...
24             Semantic Analysis                                                                                          ...
24             Semantic Analysis                                                                                          ...
Semantic Analysis                                                  Consider all entities within the same context          ...
Semantic Analysis      Named Entity Recognition                                                 Entity Selection ProcessSe...
Semantic Analysis                                                                             Entity Selection Process    ...
24                                                                                                                        ...
Entity Based Search                                                                                                       ...
http://mediaglobe.yovisto.com:8080/mggui-dev2/32              search facets C. Hentschel, H. Sack, et al., Open up cultura...
Explorative Search                                                                                                 dbpedia...
Contact:                                                       Dr. Harald Sack                                            ...
Context-Driven Semantic Multimedia Search
Upcoming SlideShare
Loading in …5
×

Context-Driven Semantic Multimedia Search

1,383 views
1,205 views

Published on

Presentation at GOPORTIS 2013 Conference, March, 19, 2013, Hannover

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,383
On SlideShare
0
From Embeds
0
Number of Embeds
8
Actions
Shares
0
Downloads
25
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Context-Driven Semantic Multimedia Search

  1. 1. Context-Driven Semantic Multimedia Search Hannover, 19/03/2013 Dr. Harald Sack Hasso-Plattner-Institut for IT-Systems Engineering University of PotsdamGoportis Conference 2013 on Non-TextualInformation - Strategy and Innovation Beyond Text
  2. 2. Context-Driven Semantic Multimedia Search • Searching Multimedia Web vs. Archive • How to Open Up Multimedia Data? Automated Multimedia Analysis • How to Determine the Meaning of Metadata? Context-Driven Semantic Analysis • Some Examples of Semantic SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  3. 3. 3 Searching the Web Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam
  4. 4. 4 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam
  5. 5. 4 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam
  6. 6. Google Multimedia SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, Workshop ,Corporate Semantic Web‘, XInnovations 2011, Berlin, 19. Sep. 2011
  7. 7. How does Google find Multimedia? ‣Google Multimedia Search relies on text-based metadata and link contextHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  8. 8. Seach by Media ContentHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  9. 9. The Ordinary Archive is a Small World... Jules VerneHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  10. 10. The Ordinary Archive is a Small World... Jules VerneHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  11. 11. But, wouldn‘t it be nice, if..... ...but maybe you are also interested in - George Melies (2 videos) - Mark Twain (1 video) Jules Verne - H.G. Wells (2 videos) - science fiction (11 videos) - adventure (20 videos) - France (101 videos) - Moon (33 videos) - literature (434 videos) - art (1.205 videos)Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  12. 12. How to Search in Multimedia Archives?Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  13. 13. Searching a Multimedia Archive Step 1: Digitization of analog media Step 2: Annotation with (text-based) metadata Step 3: Content-based retrieval based on available metadatavfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn Jörg Waitelonis, Hasso-Plattner-Institut Potsdam
  14. 14. vfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn Jörg Waitelonis, Hasso-Plattner-Institut Potsdam
  15. 15. Today: Manual Annotationvfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn Jörg Waitelonis, Hasso-Plattner-Institut Potsdam
  16. 16. (Selected) Automated Media Analysis text / images audio- visualHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  17. 17. (Selected) Automated Media Analysis text / images audio- visualHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011 image
  18. 18. (Selected) Automated Media Analysis text / images audio- visual Visual AnalysisHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011 image
  19. 19. (Selected) Automated Media Analysis text / images audio- visual Visual Analysis Text RecognitionHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011 image
  20. 20. (Selected) Automated Media Analysis text / images audio- visual Visual Concept Detection Visual Analysis Text RecognitionHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011 image
  21. 21. (Selected) Automated Media Analysis text / images audio- visual Visual Concept Logo Detection Detection Visual Analysis Text RecognitionHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011 image
  22. 22. (Selected) Automated Media Analysis text / images audio- visual Visual Face Concept Logo Detection Detection Face Detection Detection Visual Analysis Text RecognitionHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011 image
  23. 23. (Selected) Automated Media Analysis text / images audio- visual Audio-Mining audio structural Automated audio event analysis Speech detection Recognition Visual Face Concept Logo Detection Detection Face Detection Detection Visual Analysis Text RecognitionHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011 image
  24. 24. Structural Video Analysis • Decomposition of time-based media into meaningful media fragments of coherent content that can be used as basic element for indexing and classification videoscenes shotssubshotsframeskeyframes
  25. 25. Video Optical Character Recognition (OCR) • Video OCR is much more difficult than traditional print OCR • fast detection/filtering of text candidates • verification of text candidates • script separation from background • visual quality enhancement • application of standard OCR software • spell correction w.r.t. context and temporal redundancy
  26. 26. Video Face Detection, Tracking & Clustering • Face Detection Detect candidate image regions in a video frame that depict a human face • Face Tracking Track a detected face in video over consecutive frames within shot boundaries • Face Clustering Group faces detected and tracked in videos into visually similar sets within a single videoperson not a person • Face Recognition/Identificationfrontal face:90% Reliable identification of detected person faces profile face:70%
  27. 27. Visual Context Detection• Adaption of traditional ,Bag of Words‘ approach from text retrieval• Image is expressed as vector (histogram) of dictionary codeword frequencies• classification via machine learning (Support Vector Machines)
  28. 28. How to Determine the Meaning of Metadata? • Authoritative Metadata level of abstraction • structured data accura cy • semi-structured data • natural language text re liability • Non-authoritative Metadata Semantic • (free) user tags and comments Analysis • restricted vocabularies context • (Media) Analysis Metadata agm atics pr • low level features location • high level features dependency • etc. time dependencyHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011
  29. 29. Annotation of Audiovisual Data • Multimedia data with spatiotemporal AnnotationsMetadata Extraction Metadata (e.g. MPEG-7) ... <SpatialDecomposition> <TextAnnotation> <KeywordAnnotation> <Keyword>Astronaut</Keyword> </KeywordAnnotation> </TextAnnotation> <SpatialMask> <SubRegion> <Polygon> <Coords> 480 150 620 480 </Coords> </Polygon> </SubRegion> ong </SpatialMask> Neil Armstr ... </SpatialDecomposition> ...
  30. 30. ,Neil Armstrong‘ is more than just a character string
  31. 31. ,Neil Armstrong‘ is more than just a character string Neil Armstrong
  32. 32. ,Neil Armstrong‘ is more than just a character string Neil Armstrong is a Astronaut
  33. 33. ,Neil Armstrong‘ is more than just a character string Neil Armstrong is a Astronaut subClassOf Science Occupation
  34. 34. ,Neil Armstrong‘ is more than just a character string Neil Armstrong is a Astronaut subClassOf Science Occupation subClassOf Employment
  35. 35. ,Neil Armstrong‘ is more than just a character string Neil Armstrong is a is a Astronaut Person subClassOf Science Occupation subClassOf Employment
  36. 36. ,Neil Armstrong‘ is more than just a character string Neil Armstrong is a is a Astronaut Person subClassOf Science Occupation subClassOf has an Employment
  37. 37. ,Neil Armstrong‘ is more than just a character string Neil Armstrong Entities is a is a Ontologies Astronaut Person subClassOf Science Occupation subClassOf has an Employment
  38. 38. ,Neil Armstrong‘ is more than just a character string Neil Armstrong Entities is a is a Ontologies Astronaut Person subClassOf is NOT a Science Occupation subClassOf has an Employment
  39. 39. ,Neil Armstrong‘ is more than just a character string Neil Armstrong Entities is a is a Ontologies same as Kosmonaut Astronaut Person subClassOf is NOT a Science Occupation subClassOf has an Employment
  40. 40. ,Neil Armstrong‘ is more than just a character string Juri Gagarin is a Neil Armstrong Entities is a is a Ontologies same as Kosmonaut Astronaut Person subClassOf is NOT a Science Occupation subClassOf has an Employment
  41. 41. Where does the knowledge come from...?
  42. 42. Where does the knowledge come from...?
  43. 43. Where does the knowledge come from...?
  44. 44. But what, if there is no trivial unique identification? Web of Data = Linked Open Data
  45. 45. But what, if there is no trivial unique identification? Web of Data = Linked Open Data Armstrong user tag
  46. 46. Arms tron gSemantic Web Technologies , Dr. Harald Sack, Hasso Plattner Institute, University of Potsdam
  47. 47. Arms Arm tron stron + gg MoonSemantic Web Technologies , Dr. Harald Sack, Hasso Plattner Institute, University of Potsdam
  48. 48. Understanding requires Context Web of Data = Linked Open Data Armstrong
  49. 49. Understanding requires Context Web of Data = Linked Open Data Armstrong Moon
  50. 50. Understanding requires Context Web of Data = Linked Open Data Eagle Armstrong Moon
  51. 51. Understanding requires Context Web of Data = Linked Open Data Space Eagle Armstrong Moon
  52. 52. 24 Semantic Analysis 24 24 2 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model24 2 Context Item N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  53. 53. 24 Semantic Analysis 24 24 2 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model24 2 Context Item Context Dimensions Temporal Spatial Provenance Context Context Context N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  54. 54. 24 Semantic Analysis 24 24 2 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model24 2 Context Item Context Dimensions Temporal Spatial Provenance Context Context Context determines Relevance N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  55. 55. 24 Semantic Analysis 24 2 24 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model 24 2 Context Item Contextual Description Context Dimensions Class Level of Temporal Spatial ProvenanceDiversity Structure Context Context Context determines Relevance N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  56. 56. 24 Semantic Analysis 24 2 24 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model 24 2 Context Item Contextual Description Context Dimensions Class Level of Temporal Spatial ProvenanceDiversity Structure Context Context Context influences determines Ambiguity Relevance N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  57. 57. 24 Semantic Analysis 24 2 24 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model 24 2 Context Item Contextual Description Context Dimensions Class Level of Source Source Temporal Spatial ProvenanceDiversity Structure Reliability Diversity Context Context Context influences determines Ambiguity Relevance N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  58. 58. 24 Semantic Analysis 24 2 24 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model 24 2 Context Item Contextual Description Context Dimensions Class Level of Source Source Temporal Spatial ProvenanceDiversity Structure Reliability Diversity Context Context Context influences influences determines Ambiguity Accuracy Relevance N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  59. 59. 24 Semantic Analysis 24 2 24 24 Semantics is determined by Context 4 24 24 4 24 24 SEMEX Multimedia Context Model 24 2 Context Item Contextual Description Context Dimensions Class Level of Source Source Temporal Spatial ProvenanceDiversity Structure Reliability Diversity Context Context Context influences influences determines Ambiguity Accuracy Relevance Text „Armstrong landed the Eagle on the Moon.“ N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  60. 60. Semantic Analysis Consider all entities within the same context Named Entity Mapping „Armstrong landed the Eagle on the Moon.“ Armstrong Eagle Moon 448 entities 95 entities 156 entities Man on the Moon (film) George Armstrong Custer Eagle (Bird) Moon (song) Neil Armstrong Eagle (heraldry) Moon Son-Ri The Armstrong Twins USCGC Eagle Moon 44 C Moon Armstrong, Florida The Eagle (2011 film) Eagle (comic) The Moon (Tarot card) Craig Armstrong Armstrong, Ontario Man on the Moon (soundtrack) Eagle (song) Moon Armstrong (Moon Crater) Eagle (lunar module) Armstrong Gun The Eagle (newspaper) Man on the Moon (musical) Armstrong‘s Theorem War Eagle Mr. Moon (song) Eagle (Moon Crater) Louis Armstrong International Airport Moon (Band) The Eagle (Pub) Armstrong County, Texass Moon OS Eagle TV Eagle Falls (Washington) Moon 83 Joe Armstrong Lottie Moon Ian Armstrong Eagle (racehorse) Edgar Moon Armstrong Tunnel Armstrong Tunnel Armstrong Automobile John H. Eagle Darvin Moon Sir Thomas Armstrong Eagle (typeface) Gary Moon William Moon Louis Armstrong Angela Eagle Francis Moon Armstrong (British Columbia) Linda Eagle Robert Charles Moon Karen Armstrong Allan Moon Curtis Armstrong James Philipp Eagle Fly me to the Moon (song)Hilary Armstrong Black Moon Ban-Ki Moon Gillian Armstrong William L. Armstrong
  61. 61. Semantic Analysis Named Entity Recognition Entity Selection ProcessSelect matching entities from all possible candidate entities:• Popularity based strategies • reference text corpus (wikipedia)• Linguistical strategies • link graph (wikipedia)• Statistical strategies • semantic graph• Semantic based strategies (dbpedia)General Approach1. Make an assumption2. Do the strategies support or contradict your assumption3. Make decision according to logical and probabilistic rules/constraints N. Ludwig, H. Sack, “Named entity recognition for user-generated tags,TIR 2011
  62. 62. Semantic Analysis Entity Selection Process (Semantic) Graph Analysis Named Entity Recognition „Armstrong landed the Eagle on the Moon.“ Armstrong Eagle Moon 448 entities 95 entities 156 entities Man on the Moon (film) George Armstrong Custer Eagle (Bird) Moon (song) Neil Armstrong Eagle (heraldry) Moon Son-Ri The Armstrong Twins USCGC Eagle Moon 44 C Moon Armstrong, Florida The Eagle (2011 film) Eagle (comic) The Moon (Tarot card) Craig Armstrong Armstrong, Ontario Moon Man on the Moon (soundtrack) Eagle (song) Armstrong (Moon Crater) Eagle (lunar module) Armstrong Gun The Eagle (newspaper) Man on the Moon (musical) Armstrong‘s Theorem War Eagle Mr. Moon (song) Eagle (Moon Crater) Louis Armstrong International Airport Moon (Band) The Eagle (Pub) Armstrong County, Texass Moon OS Eagle TV Eagle Falls (Washington) Moon 83 Joe Armstrong Lottie Moon Ian Armstrong Eagle (racehorse) Edgar Moon Armstrong Tunnel Armstrong Tunnel Armstrong Automobile John H. Eagle Darvin Moon Sir Thomas Armstrong Eagle (typeface) Gary Moon William Moon Louis Armstrong Angela Eagle Francis Moon Armstrong (British Columbia) Linda Eagle Robert Charles Moon Karen Armstrong Allan Moon Curtis Armstrong James Philipp Eagle Fly me to the Moon (song)Hilary Armstrong Black Moon Ban-Ki Moon Gillian Armstrong William L. Armstrong N. Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013
  63. 63. 24 24 Semantically Annotated 2 Multimedia 24 4 24 24 24 4 24 24 24 230 Video Analysis / time Metadata Extraction metadata metadata metadata metadata Entity Recognition/ metadata Mapping e.g., person xy location yz N. Ludwig, H. Sack: Named Entity Recognition for User- event abc Generated Tags. In Proc. of the 8th Int. Workshop on Text-based Information Retrieval, IEEE CS Press, 2011 e.g., bibliographical data, geographical data, encyclopedic data, ..vfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn Jörg Waitelonis, Hasso-Plattner-Institut Potsdam
  64. 64. Entity Based Search http://www.yovisto.com/labs/autosuggestion/31• Query string refinement / extension • linguistic ambiguities of traditional keyword based• entity auto-suggestion search can be avoided• interpretation of natural language queries • enables high precision and high recall retrieval Vorlesung Semantic Waitelonis,Harald Sack, Hasso-Plattner-Institut, Universität Potsdam a rich and yet immediate Starting Point for Exploratory Search, IVDW 2012 J. Osterhoff, J. Web, Dr. H. Sack, Widen the Peepholes! Entity-Based Auto-Suggestion as
  65. 65. http://mediaglobe.yovisto.com:8080/mggui-dev2/32 search facets C. Hentschel, H. Sack, et al., Open up cultural heritage in video archives with mediaglobe, I2CS 2012 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam
  66. 66. Explorative Search dbpedia:Michael_Collins34 dbpedia-owl:mission dbpedia:Apollo_11 dbpedia-owl:mission dcterms:subject dbpedia-owl:mission dbpedia:Neil_Armstrong dbpedia:Buzz_Collins dcterms:subject category:Apollo_program dbpedia:Apollo_13 rdf:type dbpedia:Space_Shuttle_Challenger yago:Space_accidents_and_incidents rdf:type http://mediaglobe.yovisto.com:8080/ Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam J. Waitelonis, H. Sack: Towards exploratory video search using linked data, MTAP Volume 59, Number 2 (2012), 645-672
  67. 67. Contact: Dr. Harald Sack Hasso-Plattner-Institut für Softwaresystemtechnik Universität Potsdam Prof.-Dr.-Helmert-Str. 2-3 D-14482 Potsdam Homepage: ttp://www.hpi.uni-potsdam.de/meinel/team/sack.html h Blog: http://yovisto.blogspot.com/ E-Mail: harald.sack@hpi.uni-potsdam.de Twitter: lysander07 / biblionomicon / yovisto Slides can be found at http://slideshare.com/lysander07/ mu ch ou v ery h ank y n! T at ten tio yo ur f orHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011

×