trends in search engines

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.