TRENDS IN SEARCH ENGINES Bc. Poláková Barbora  December 2009 There is no study that would prefer  any of further approache...
Basic approaches  <ul><li>Visual search  </li></ul><ul><li>Clustering  </li></ul><ul><li>Natural language </li></ul>
VISUALISATION
Visual search engines <ul><li>1980s – Graphic User Interface  </li></ul><ul><li>core system activities  </li></ul><ul><ul>...
Cognitive aspects  <ul><li>symbolic and visual thinking  </li></ul><ul><li>term and conceptual thinking  </li></ul><ul><li...
Information space  <ul><li>set of relations among items held by an information system  (Ingwersen, 1996) .  </li></ul><ul>...
Representative level  <ul><li>Book house  </li></ul><ul><ul><ul><li>extension of library catalogue  </li></ul></ul></ul><u...
Book House
Hyperbolic tree
Thesaurus structure
Visualisation mantra  <ul><li>1.  Overview first </li></ul><ul><li>2. Zoom and filter </li></ul><ul><li>3. Details on dema...
Problems <ul><li>Humans are more familiar with non-visual IR interfaces </li></ul><ul><ul><ul><li>training needed </li></u...
Examples <ul><li>Search me   new generation of visual search engine as the combination of tangent and visual approach.  </...
Carrot2
CLUSTERING
Cluster  <ul><li>number of similar items  grouped closely together  </li></ul><ul><ul><ul><li>things, persons or groups </...
Clusters <ul><li>Exclusive Clustering  </li></ul><ul><ul><ul><ul><li>definite cluster with strict data  </li></ul></ul></u...
Figure 1
Clustering models <ul><li>Distance-based clustering  </li></ul><ul><ul><ul><ul><li>two or more objects belong to the same ...
Clustering models <ul><li>Conceptual clustering  </li></ul><ul><ul><ul><li>not based on perfect match and similarity betwe...
Clustering models
Clustering <ul><li>Model-based clustering  </li></ul><ul><ul><ul><li>two different data-set  </li></ul></ul></ul><ul><ul><...
Cognitive aspects <ul><li>Inner mental modelling </li></ul><ul><ul><ul><li>Wittgenstein </li></ul></ul></ul><ul><li>Term a...
Problems <ul><li>Positioning in information space  </li></ul><ul><li>Indexing  </li></ul><ul><li>Large data set  </li></ul...
Examples 2 <ul><li>Clusty   </li></ul><ul><li>Carrot2Workbench  </li></ul>
Carrot2
Conclusion  <ul><li>Combination of both approaches could serve better than solitary </li></ul><ul><li>It covers whole cogn...
Thanks for your attention  www.baraika.blogspot.com References and full version of the paper will be presented on aforemen...
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Trends In Search Engines

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Presentation of one chapter of my master thesis held on natural language in web search engines.
It offers two other approaches in search engine: visualisation and clusters

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Trends In Search Engines

  1. 1. TRENDS IN SEARCH ENGINES Bc. Poláková Barbora December 2009 There is no study that would prefer any of further approaches in information retrieval in general...
  2. 2. Basic approaches <ul><li>Visual search </li></ul><ul><li>Clustering </li></ul><ul><li>Natural language </li></ul>
  3. 3. VISUALISATION
  4. 4. Visual search engines <ul><li>1980s – Graphic User Interface </li></ul><ul><li>core system activities </li></ul><ul><ul><li>mouse manipulating </li></ul></ul><ul><ul><li>data entry </li></ul></ul><ul><ul><li>query for processing </li></ul></ul>
  5. 5. Cognitive aspects <ul><li>symbolic and visual thinking </li></ul><ul><li>term and conceptual thinking </li></ul><ul><li>short-term memory </li></ul><ul><ul><li>faster upload ; unconscious low cognitive activities </li></ul></ul><ul><li>long-term memory </li></ul><ul><ul><li>conscious high cognitive activity </li></ul></ul>
  6. 6. Information space <ul><li>set of relations among items held by an information system (Ingwersen, 1996) . </li></ul><ul><ul><li>multidimensional </li></ul></ul><ul><ul><li>intuitive </li></ul></ul><ul><ul><li>vector space modelling </li></ul></ul><ul><ul><li>terms, documents, relations </li></ul></ul>
  7. 7. Representative level <ul><li>Book house </li></ul><ul><ul><ul><li>extension of library catalogue </li></ul></ul></ul><ul><li>Hyperbolic tree </li></ul><ul><ul><ul><li>hyperbolic space </li></ul></ul></ul><ul><li>Visualisation lexical thesaurus data </li></ul><ul><ul><ul><li>thesaurus network; hierarchical structure </li></ul></ul></ul>
  8. 8. Book House
  9. 9. Hyperbolic tree
  10. 10. Thesaurus structure
  11. 11. Visualisation mantra <ul><li>1. Overview first </li></ul><ul><li>2. Zoom and filter </li></ul><ul><li>3. Details on demand </li></ul><ul><li>4. Interactivity </li></ul><ul><li>5. Linking </li></ul><ul><li>(Shneiderman, 1996) </li></ul>
  12. 12. Problems <ul><li>Humans are more familiar with non-visual IR interfaces </li></ul><ul><ul><ul><li>training needed </li></ul></ul></ul><ul><li>Large data set </li></ul><ul><ul><ul><li>unnoticed results representation </li></ul></ul></ul><ul><ul><ul><li>indexing </li></ul></ul></ul><ul><ul><ul><ul><li>data structure, data description </li></ul></ul></ul></ul>
  13. 13. Examples <ul><li>Search me new generation of visual search engine as the combination of tangent and visual approach. </li></ul><ul><li>Viewzi is highly designed and offers around 16 patterns of representation. </li></ul><ul><li>Kartoo is probably the best version of web based visual search engine. It offers a structured map of terms, topics and the document connection. </li></ul><ul><li>Carrot2 </li></ul>
  14. 14. Carrot2
  15. 15. CLUSTERING
  16. 16. Cluster <ul><li>number of similar items grouped closely together </li></ul><ul><ul><ul><li>things, persons or groups </li></ul></ul></ul><ul><li>unsupervised classification </li></ul><ul><li>reaction to the user's query </li></ul><ul><li>natural grouping of data-set </li></ul>
  17. 17. Clusters <ul><li>Exclusive Clustering </li></ul><ul><ul><ul><ul><li>definite cluster with strict data </li></ul></ul></ul></ul><ul><li>Overlapping Clustering </li></ul><ul><ul><ul><ul><li>each cluster belongs to two or more clusters </li></ul></ul></ul></ul><ul><li>Hierarchical Clustering </li></ul><ul><ul><ul><ul><li>union between two nearest clusters </li></ul></ul></ul></ul><ul><li>Probabilistic Clustering </li></ul><ul><ul><ul><ul><li>completly probabilistic approach </li></ul></ul></ul></ul>
  18. 18. Figure 1
  19. 19. Clustering models <ul><li>Distance-based clustering </li></ul><ul><ul><ul><ul><li>two or more objects belong to the same cluster if they are “close” according to a given distance </li></ul></ul></ul></ul><ul><ul><ul><ul><li>items in the group share almost the same characteristics expresed by their position in the information space </li></ul></ul></ul></ul>
  20. 20. Clustering models <ul><li>Conceptual clustering </li></ul><ul><ul><ul><li>not based on perfect match and similarity between objects </li></ul></ul></ul><ul><ul><ul><li>conceptual likeness </li></ul></ul></ul><ul><li>Latent semantic clustering </li></ul><ul><ul><ul><li>Rather than expanding queries based only a small set of term relations </li></ul></ul></ul><ul><ul><ul><li>all terms potentially related to each other, and all documents to be similarly related </li></ul></ul></ul>
  21. 21. Clustering models
  22. 22. Clustering <ul><li>Model-based clustering </li></ul><ul><ul><ul><li>two different data-set </li></ul></ul></ul><ul><ul><ul><li>position in information space – similarity to model </li></ul></ul></ul><ul><ul><ul><li>inner mental model of reality - artificial or human </li></ul></ul></ul><ul><ul><ul><li>selfcorrecting </li></ul></ul></ul>
  23. 23. Cognitive aspects <ul><li>Inner mental modelling </li></ul><ul><ul><ul><li>Wittgenstein </li></ul></ul></ul><ul><li>Term and conceptual thinking </li></ul><ul><ul><ul><li>Higher mental activities </li></ul></ul></ul><ul><ul><ul><li>Learning approach </li></ul></ul></ul><ul><li>Contextuality </li></ul>
  24. 24. Problems <ul><li>Positioning in information space </li></ul><ul><li>Indexing </li></ul><ul><li>Large data set </li></ul><ul><li>Changeability </li></ul>
  25. 25. Examples 2 <ul><li>Clusty </li></ul><ul><li>Carrot2Workbench </li></ul>
  26. 26. Carrot2
  27. 27. Conclusion <ul><li>Combination of both approaches could serve better than solitary </li></ul><ul><li>It covers whole cognitive area </li></ul><ul><ul><li>high and low </li></ul></ul><ul><li>Not just IR system, but also a learning tool </li></ul><ul><li>Reflecting the contextuality </li></ul>
  28. 28. Thanks for your attention www.baraika.blogspot.com References and full version of the paper will be presented on aforementioned blog.

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