Date post: | 29-May-2015 |
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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...
Basic approaches
Visual search Clustering Natural language
VISUALISATION
Visual search engines
1980s – Graphic User Interface core system activities
mouse manipulating data entry query for processing
Cognitive aspects
symbolic and visual thinking term and conceptual thinking
short-term memory faster upload ; unconscious low cognitive activities
long-term memory conscious high cognitive activity
Information space
set of relations among items held by an information system (Ingwersen, 1996). multidimensional intuitive vector space modelling terms, documents, relations
Representative level
Book house extension of library catalogue
Hyperbolic treehyperbolic space
Visualisation lexical thesaurus data thesaurus network; hierarchical structure
Book House
Hyperbolic tree
Thesaurus structure
Visualisation mantra
1. Overview first2. Zoom and filter3. Details on demand
4. Interactivity 5. Linking
(Shneiderman, 1996)
Problems
Humans are more familiar with non-visual IR interfaces
training needed
Large data set unnoticed results representation indexing
data structure, data description
Examples
Search me new generation of visual search engine as the combination of tangent and visual approach.
Viewzi is highly designed and offers around 16 patterns of representation.
Kartoo is probably the best version of web based visual search engine. It offers a structured map of terms, topics and the document connection.
Carrot2
Carrot2
CLUSTERING
Cluster
number of similar items grouped closely together
things, persons or groups
unsupervised classification reaction to the user's query natural grouping of data-set
Clusters
Exclusive Clustering definite cluster with strict data
Overlapping Clustering each cluster belongs to two or more clusters
Hierarchical Clustering union between two nearest clusters
Probabilistic Clustering completly probabilistic approach
Figure 1
Clustering models
Distance-based clustering
two or more objects belong to the same cluster if they are “close” according to a given distance
items in the group share almost the same characteristics expresed by their position in the information space
Clustering models
Conceptual clustering not based on perfect match and similarity between
objectsconceptual likeness
Latent semantic clustering Rather than expanding queries based only a small set
of term relationsall terms potentially related to each other, and all
documents to be similarly related
Clustering models
Clustering
Model-based clustering
two different data-set position in information space – similarity to model inner mental model of reality - artificial or human selfcorrecting
Cognitive aspects
Inner mental modellingWittgenstein
Term and conceptual thinking Higher mental activitiesLearning approach
Contextuality
Problems
Positioning in information space Indexing Large data set Changeability
Examples 2
Clusty Carrot2Workbench
Carrot2
Conclusion
Combination of both approaches could serve better than solitary
It covers whole cognitive area high and low
Not just IR system, but also a learning tool Reflecting the contextuality
Thanks for your attention
www.baraika.blogspot.com
References and full version of the paper will be presented on
aforementioned blog.