Systems and Users in Intelligent Information Retrieval:

Who does What?

prof. dr. L. Schomaker

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Symposium 3/2/2003, Delft

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Overview

Who?

Intelligent Information Retrieval and Presentation (I2RP): The Challenge

The future

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Persons & Institutes

Supervisors: – prof. Lynda Hardman (CWI)– prof. Jaap van den Herik (UM)– prof. Gerard Kempen/Crit Cremers (UL)– dr. N. Taatgen (RuG)

Coordinator– prof. Lambert Schomaker (RuG)

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Persons & Institutes (continued…)

Researchers: – Stefano Bocconi (oio,CWI)– Yulia Bachvarova (oio,CWI)– Boban Arsenijevic (oio,UL)– Floris Wiesman (postdoc, UM)– Judith Grob (oio,RuG)

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Intelligent Information Retrieval and Presentation (I2RP): The Challenge

Observations: CPU power is ever increasing, but… “Current systems in Information Retrieval

are violating the essential rules for an intelligent dialogue”

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A mutually cooperative dialogue?

Grice (1975): the rules for a mutually cooperative dialogue are:

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Grice (1975)

Maxims of quantity:

– Make your contribution as informative as required

– Do not make your contribution

more informative than required

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Grice (1975)

Maxims of quality:

– Do not say what you believe to be false

– Do not say that for which you lack evidence

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Maxims of Grice (1975)

Maxim of relation:

– Be relevant

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Maxims of Grice (1975)

Maxim of manner:

– Avoid obscure expressions– Avoid ambiguity– Be orderly– Be brief

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Example: Quantity

“when did Napoleon die?”

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Example: Quantity

“when did Napoleon die?”

72800 documents found

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How to design systems that obey the Maxims of Grice?

Use Knowledge!

Use the User!

Use language!

Starting point: The “user in context”

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(1) Knowledge

Use Knowledge!

What Knowledge?

Who specifies it?

How to relate knowledge from heterogeneous

data bases?

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(2) The User

Use the User!

Will they be motivated?

What type of user? Skilled / newbie?

What does the user WANT?

Can we predict user actions?

How to reason like the current user?

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Example: Relevance Feedback in Image Search

Machine Learning may give us a free ride on Moore’s Law ( fcpu increases each year)

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Example: Relevance Feedback in Image Search

Machine Learning may give us a free ride on Moore’s Law ( fcpu increases each year)

But: Pattern classification needs examples (ground truth values) given by users

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Example: Relevance Feedback in Image Search

Machine Learning may give us a free ride on Moore’s Law ( fcpu increases each year)

But: Pattern classification needs examples (ground truth values) given by users

In Information Retrieval, this is implemented as “relevance feedback”, given by the user,

on quality of items in a hit list

Relevance Feedback in Image Search …

Relevance Feedback in Image Search

Relevance Feedback in Image Search

users are lazy, especially if the perceived benefits are low…

The machinemay findstructure…

(Kohonenself-organizedmap ofscannedhandwrittencharacters)

The machinemay findstructure…

Buthumangroundtruth labelsare stillnecessary!

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… the user …

Knowledge on user skill development is essential. What is annoying at start may be easy later (and vice versa).

What is the user’s goal? How do users maintain their “goal stack”?

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(3) Language

Use language! Can the system parse input sentences?

Can the system generate text answers from

non-linguistic data and knowledge bases?

How to generate a narrative from a sequence

of facts?

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Project: Floris Wiesman (UM)“Instance vs Term-based Ontology Mappings”

Given two ontologies from the cultural

heritage, how can the knowledge be shared? (manual translation? X)

Try to find correspondences in naming schemes, as if the mapping problem were an Information Retrieval problem in itself.

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Ontology Mapping (Wiesman)

Try to find correspondences in naming schemes, as if the mapping problem were an Information Retrieval problem in itself.

Example:

Ontology “museum-A”:

Document->Book->Author->Name

Ontology “library-B”:

Document->Book->Writer->Name

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Ontology Mapping (Wiesman POSTER)

Try to find correspondences in naming schemes, as if the mapping problem were an Information Retrieval problem in itself.

Example:

Ontology “museum-A”:

Document->Book->Author->Name

Ontology “library-B”:

Document->Book->Writer->Name

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Projects: Stefano Bocconi (CWI)

How to develop discourse models: system response user’s question

– Narrative (“Tell me about…”)– Description (“What is …?”)– Explanation (“Why is…?”)– Argument (“Why should …?”)

Experimental environment:

“Rembrandt’s World” Ontology

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Projects: Stefano Bocconi (CWI)

How to develop discourse models: system response user’s question

– Narrative (“Tell me about…”)– Description (“What is …?”)– Explanation (“Why is…?”)– Argument (“Why should …?”)

Experimental environment:

“Rembrandt’s World” Ontology

POSTER

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Projects: Boban Arsenijevic (UL)

How to Parse & Generate using an intermediate semantic processing stage?

Input sentence parsing

“Aggregate Semantic Material

generator

Output sentences

Goal: explore how different phrasings still pertain to the semantic core

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Projects: Boban Arsenijevic (UL)

How to Parse & Generate using an intermediate semantic processing stage?

Input sentence parsing

“Aggregate Semantic Material

generator

Output sentences

Goal: explore how different phrasings still pertain to the semantic core

POSTER + demo:parser/generator

for Dutch

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Projects: Judith Grob (RuG)

How to develop an active user agent that learns from the user and behaves in a way which is acceptable and useful?

Cognitive modeling (ACT-R), skill development and concept learning by humans

“Instance-based” learning schemes are a method to find analogies between patterns

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Projects: Judith Grob (RuG)

(just started). An initial model concerns the modeling of learning a simple task with a quantitative target variable (“Sugar Factory”)

ACT-R appears to be able to mimic human learning and ‘transfer’

A similar goal-oriented task in information retrieval will be developed

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Projects: Judith Grob (RuG)

(just started). An initial model concerns the modeling of learning a simple task with a quantitative target variable (“Sugar Factory”)

ACT-R appears to be able to mimic human learning and ‘transfer’

A similar goal-oriented task in information retrieval will be developed

POSTER

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Future developments

Partial overlap between projects is noted and exploited:– “Rembrandt’s World” is a useful example ontology– Goal: interoperability over the network– First: develop bilateral cooperation between

partners cooperation yields co-publication and

software combination

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Conclusion

I2RP represents a multi-faceted view on system and user in an information-retrieval context

Multi-disciplinarity: CS,AI,Cognition,Language

Still: a common ground starts to develop!