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On the role of a Librarian Agent in ontology-

based Knowledge Management Systems

Nenad StojanovicInstitute AIFB

WM 2003

Luzern, 2. – 4. 4. 2003

2

Agenda

• Motivation

• Architecture

• Implementation

• Conclusion

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The Management of the Searching for Information in Semantic Portals

Challenges for the ontology-based Information Retrieval:

(inherited from the Web IR)

- a user posts short queries

- a user reads only top-ten results

(inherited from the Digital Libraries)

- users’ needs vary

Query Management

Ranking Schema

Collection Management

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Approach: Simulating bricks-and-mortar environment

I need something about ontologies and

evolution 1. Do I have that in the repository

2.

Please, refine the request evolution 4.

How?5.

A lot of users search for

+Editor 6.

+Editor , but visual

7.

You got 20 results

8.

I have time to see three only. Are these the best

ones 9.To notice: one query more for ontologies, ...

10.

Librarian Agent

1000 results!

This query is so

ambiguous

3.

Collection Management Query Management

Do I have that in the repository

Please, refine the request evolution

To notice: one query more for ontologies, ...

A lot of users search for

+Editor

1000 results!

This query is so

ambiguous

Ranking

You got 20 results

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Agenda

• Motivation

• Architecture

• Implementation

• Conclusion

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query

response

query/response/action

Log file

Access mechanism

Information repository

User

Librarian Agent

Domainontology

Query Management

Collection Management

quer

y

resp

onse

quer

y

1.

ambi

guity

2.

refin

emen

ts

3.

feedback

5.changerecommendations

6.

Librarian Agent - Architecture

Ranking

4.

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1. The Ambiguity of a Query

Definition: A query is ambiguous if the information need behind that query cannot be determined uniquely

Types:

A. semantic ambiguity – regarding the used vocabulary

- incompleteness

- unclearness

- redundancy

- un-satisfiable query

B. content ambiguity – regarding the information repository

- equivalency

- generalisation

- specialisation

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1.A. Semantic Ambiguity

X:Doctor[worksIn->>KM] and X[inGroup->>KM] and X:Researcher.

Compactness:

X:Doctor or X:Researcher Researcher

Doctor

Professor Dozent

Clarity:

X:Doctor means

X:Professor or X:Dozent

Redundancy:

X[worksIn->>KM] is

redundant informationRule:

FORALL X, X[worksIn->>KM] <-X[inGroup->>KM].

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1.B. Content Ambiguity

For a Query-Answering pair: (M, O)

Structural equivalence (=):

Structural subsumption (parent-child) (<):

212211 ),(),( OOOMOM

212211 ),(),( OOOMOM

query_termsquery_objects

Parameters:

Largest equivalent query:

Smallest equivalent query

Uniquness

Covering

CoveringTerms

),(max a

QQ

ia OMQ

adiri

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1.B Content Ambiguity – Query Map

Query: “Prof. and Project and KM“ NumberOfTerms:=3NumberOfObjects:=3...MaxEquality:= 2LargestEqu:= “Prof. and Project and KM and Group“MinEquality:= 1SmallestEqu:=(“Prof. and Project“) or (“Prof. and KM“)Uniquness:=P5

Generalisation:“Lecturer and Group and KM“ NumberOfTerms:=3NumberOfObjects:=9...Covering = 90%

Generalisation:“Researcher and Project and KM“ NumberOfTerms:=3NumberOfObjects:=8...Covering = 50%

Sibling:“PhD and Project and KM“ NumberOfTerms:=3NumberOfObjects:=1...

Specialisation:“Prof. and EU-Project and KM“ NumberOfTerms:=5NumberOfObjects:=1...

Relat

ions

gen

erat

ed b

y FC

A

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2. Query Refinement

X:Doctor[worksIn->>KM] and X[inGroup->>KM] and X:Researcher;

a) the structure of the ontology

X:Researcher -> X:Doctor

+ X[project->>.]

„project“ is an appropriate classifier

b) the capacity of the knowledge repository

P1[inGroup->>KM;worksIn->>KM;course->>X; project->>Onto1]

P2[inGroup->>KM;worksIn->>KM;course->>Y; project->>Onto1]

...

P3[inGroup->>KM;worksIn->>KM;course->>W; project->>Onto2]

P4[inGroup->>KM;worksIn->>KM;course->>Z; project->>Onto2]

c) user’s behaviour (how do users refine their queries)

QueryLog:

Collection

Sessionization Query-patterns

Analyse

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Agenda

• Motivation

• Architecture

• Implementation

• Conclusion

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Librarian Agent in the “Usage Mining Loop”

Server

Browser

Ontology Manager

UsageOLAP cube

Content vs UsageOLAP cube

Ontology +Knowledge WH

Server

LogRDBMS

OntologyInformation

Content

Logfile

Ontology information

Ontology EvolutionSemantic crawlerOther Ontology Management tools

HTTP Request

Inference Engine

…

Web page+ Ontologyinformation

semantic Log file

SemiPort

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Librarian Agent in the KAON framework

Persistence, Transaction, Security

RDF API KAON RDF SERVER

KAON API

Data layer

Middleware

KAON PORTALApplications & Services Query Recommendation

Domainontology

Information repository

Map Visualizer

Vision Portal

Usageontology

Usage Log

AmbiguityMeasurement

QueryRefinement

Query Management

RankingModule

CollectionManagement

MapBuilder

UsageLogging

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Agenda

• Motivation

• Architecture

• Implementation

• Conclusion

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Conclusion

• Librarian Agent simulates the role of a shop assistant in searching in the brick and mortar environement

• Key issues:

• measuring ambiguity of a query

• ordering query space using FCA

• Very applicable in the Knowledge Portals

• Implementation in the KAON is on the way

• Planed large-scale evaluation for the MEDLINE

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Thank you for the attention !