Guided Interactive Discovery ofGuided Interactive Discovery of
e-Government Servicese-Government Services
Giovanni Maria S...
e-Government Services for citizens represent one of the most
frequent and critical points of contact between citizens and
...
DISCOVERY of e-services
rather than plain RETRIEVAL
is a critical functionality in e-government systems
But it is managed ...
TRADITIONAL SEARCHTECHNIQUES
DO NOT WORK
Since the vast majority of information is essentially textual and
unstructured in nature
information retrieval techniques ...
1. almost 80% of relevant documents are not retrieved
2. extremely wide semantic gap between the user model
(concepts) and...
RICHSEMANTIC SCHEMATA (ONTOLOGIES)
• End-users do not understand them
• Agent mediators required: costly to implement, not...
Traditional research has focussed on
RETRIEVAL OF INFORMATION
BUT
The most common task is BROWSING:
FIND RELATIONSHIPS
THI...
Finding opportunities/services
Finding a job
Finding the laws and regulations that apply
BUT ALSO
Buying a digital camera
...
REQUIRE
A DIFFERENT INFORMATION ACCESS
PARADIGM
GUIDEDEXPLORATION
AND
INFORMATION THINNING
Dynamic Taxonomies:
the first model to fully exploit multidimensional and
faceted classifications
Sacco, G.M., “Dynamic ta...
Representation
Intension: The infobase is described by a taxonomy designed
by an expert (the schema)
Extension: Documents ...
What is a concept?
A concept is a label which identifies a set of documents
(classified under that concept)
A nominalistic...
How do concepts relate?
By subsumptions (IS-A, PART-OF)
By the Extensional Inference Rule:
Two concepts C and C’ are relat...
DYNAMIC TAXONOMIES
Concepts extensionally related to G have a yellow
background
A
B C D
E F G H I L M
a b c d e
DYNAMIC TAXONOMIES
Concepts extensionally related to G have a yellow
background
A
B C D
E F G H I L M
a b c d e
Important consequence:
Relationships among concepts need not be anticipated but can
be inferred from the actual classifica...
Putting it all together…
The browsing system
AN EXAMPLE
1. Initial step: Tree picture of the entire infobase
AN EXAMPLE
The infobase schema is used for
browsing
The initial focus...
2. Zoom on a concept and see related concepts
AN EXAMPLE
This is the central operation:
1. The new focus is ANDed with the...
3. Iterate until the number of documents is
sufficiently small
AN EXAMPLE
3 zoom operations are sufficient to select an3 z...
• Simple and familiar interface (the only new operation is
the Zoom, which is easily understood)
• The user is effectively...
• TRANSPARENCY: the user is in charge and knows
exactly what’s happening
• EXCELLENT CONVERGENCE  very few iterations
nee...
• Easy multilingual support (just translate concept labels)
• Easy to unobtrusively gather user interests
• Easy to accomm...
• Simple integration with other retrieval techniques (IR,
DB):
dynamic taxonomies as a prefilter:
they establish the conte...
CONCLUSIONS
Dynamic taxonomies provide a single and
simple access model that solves the vast
majority of the information d...
Universal Knowledge Processor
High-performance dynamic taxonomy engine
• Microsoft Windows environment
• A set of high per...
THE SYSTEM IS AVAILABLE AT
www.knowledgeprocessors.com
Thankyou!
CONCLUSIONS
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Giovanni Maria Sacco

  1. 1. Guided Interactive Discovery ofGuided Interactive Discovery of e-Government Servicese-Government Services Giovanni Maria Sacco Dipartimento di Informatica, Università di Torino Corso Svizzera 185, 10149 Torino, Italy sacco@di.unito.it Where is the knowledge we have lost in information? T.S. Eliot, The Rock
  2. 2. e-Government Services for citizens represent one of the most frequent and critical points of contact between citizens and public administrations. THE PUBLIC FACE OF GOVERNMENT e-services represent the only practical way of providing incentives and support to specific classes of citizens. THE FRIENDLIERFACE OF GOVERNMENT
  3. 3. DISCOVERY of e-services rather than plain RETRIEVAL is a critical functionality in e-government systems But it is managed by search rather than explorative technology
  4. 4. TRADITIONAL SEARCHTECHNIQUES DO NOT WORK
  5. 5. Since the vast majority of information is essentially textual and unstructured in nature information retrieval techniques are extensively used both in pull and push strategies BUT…
  6. 6. 1. almost 80% of relevant documents are not retrieved 2. extremely wide semantic gap between the user model (concepts) and the system model (words) 3. users have no or very little assistance in formulating queries 4. results are presented as a flat list with no systematic organization: browsing is difficult or impossible.
  7. 7. RICHSEMANTIC SCHEMATA (ONTOLOGIES) • End-users do not understand them • Agent mediators required: costly to implement, not transparent, hard to understand what they do • Schemata hard to design and maintain
  8. 8. Traditional research has focussed on RETRIEVAL OF INFORMATION BUT The most common task is BROWSING: FIND RELATIONSHIPS THIN ALTERNATIVES OUT
  9. 9. Finding opportunities/services Finding a job Finding the laws and regulations that apply BUT ALSO Buying a digital camera Finding a restaurant for tonight Finding the cause of a malfunction Selecting a photo Finding a suspect/missing person from a photobank ….
  10. 10. REQUIRE A DIFFERENT INFORMATION ACCESS PARADIGM GUIDEDEXPLORATION AND INFORMATION THINNING
  11. 11. Dynamic Taxonomies: the first model to fully exploit multidimensional and faceted classifications Sacco, G.M., “Dynamic taxonomies: a model for large information bases”, IEEE Trans. o n Data and Kno wle dg e Eng ine e ring , May/June 20 0 0 US Patent n. 6,763,349 (EU pending) DYNAMIC TAXONOMIES
  12. 12. Representation Intension: The infobase is described by a taxonomy designed by an expert (the schema) Extension: Documents can be classified at any level of abstraction and each document is classified under n concepts (n>1) No relationships otherthan subsumptions (IS-A, PART-OF) need to be represented in the schema. DYNAMIC TAXONOMIES
  13. 13. What is a concept? A concept is a label which identifies a set of documents (classified under that concept) A nominalistic approach: concepts are described by instances ratherthan by properties Subsumptions require that an inclusion constraint is maintained: If D(C) denotes the set of documents classified under C and C’ is a descendant of C in the hierarchy, D(C’)⊆D(C) DYNAMIC TAXONOMIES
  14. 14. How do concepts relate? By subsumptions (IS-A, PART-OF) By the Extensional Inference Rule: Two concepts C and C’ are related if there is at least a document Dwhich is classified both underC and C’ orone of theirdescendants Because of the inclusion constraint, IS-A, PART-OF relationships are a special case of the Extensional Inference Rule. DYNAMIC TAXONOMIES
  15. 15. DYNAMIC TAXONOMIES Concepts extensionally related to G have a yellow background A B C D E F G H I L M a b c d e
  16. 16. DYNAMIC TAXONOMIES Concepts extensionally related to G have a yellow background A B C D E F G H I L M a b c d e
  17. 17. Important consequence: Relationships among concepts need not be anticipated but can be inferred from the actual classification Advantages: a simpler schema adapts to new relationships (dynamic) finds unexpected relationships (discovery) DYNAMIC TAXONOMIES
  18. 18. Putting it all together… The browsing system AN EXAMPLE
  19. 19. 1. Initial step: Tree picture of the entire infobase AN EXAMPLE The infobase schema is used for browsing The initial focus is the entire infobase
  20. 20. 2. Zoom on a concept and see related concepts AN EXAMPLE This is the central operation: 1. The new focus is ANDed with the previous focus 2. The entire infobase is reduced to the documents in the current focus 3. The taxonomy is reduced in order to show all and only those concepts which are extensionally related to the selected focus (filtering)
  21. 21. 3. Iterate until the number of documents is sufficiently small AN EXAMPLE 3 zoom operations are sufficient to select an3 zoom operations are sufficient to select an average 10 documents frominfobases withaverage 10 documents frominfobases with 1,000,000 documents, described by a compact1,000,000 documents, described by a compact taxonomy with 1,000 concepts.taxonomy with 1,000 concepts.
  22. 22. • Simple and familiar interface (the only new operation is the Zoom, which is easily understood) • The user is effectively guided to reach his goal: at each stage he has a complete list of all related concepts (i.e. a complete taxonomic summary of his current focus) • Completely symmetric interaction: if A and B are related, the user will find B if he zooms on A, and A if he zooms on B (most systems are asymmetric) • Discovery of unexpected relationships BENEFITS
  23. 23. • TRANSPARENCY: the user is in charge and knows exactly what’s happening • EXCELLENT CONVERGENCE  very few iterations needed BENEFITS
  24. 24. • Easy multilingual support (just translate concept labels) • Easy to unobtrusively gather user interests • Easy to accommodate reviews, popularity, etc. • Effective push strategies  dbworldx.di.unito.it BENEFITS
  25. 25. • Simple integration with other retrieval techniques (IR, DB): dynamic taxonomies as a prefilter: they establish the context for the query dynamic taxonomies as a conceptual summary: they summarize long result lists BENEFITS
  26. 26. CONCLUSIONS Dynamic taxonomies provide a single and simple access model that solves the vast majority of the information dissemination needs of public administrations In fact they are so versatile that can be used for: laws and regulations, e-commerce, medical guidelines, human resource management, multimedia information bases…
  27. 27. Universal Knowledge Processor High-performance dynamic taxonomy engine • Microsoft Windows environment • A set of high performance multithreaded COM objects • Intension and extension in RAM even for large databases (20Mb for 1M documents) • Extremely fast operation: 327 reduced taxonomies per second on a 800K item infobase CONCLUSIONS
  28. 28. THE SYSTEM IS AVAILABLE AT www.knowledgeprocessors.com Thankyou! CONCLUSIONS
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