Evaluation of Instances Assetin a Topic Maps Ontology

Petra Haluzová haluzpet@fd.cvut.cz

Department of Informatics and TelecommunicationsFaculty of Transportation Sciences

Czech Technical University in Prague

TMRA 2010

Information asset of instance (topic)

Expresses the richness of topic description in the ontology The richness of surrounding topics description are taken into account Association types between topics are also considered

Example: Topic Type – dog 1) Name: Barney 2) Colour: black

?Breed: dachshund

Photo: http://...

Associations – owner, vaccination,…

Weights assignment: Partial weight of topic ci

Partial weight of topic c = {1, 2,...}; c N Expresses the richness of description

of each individual topic Is equal to the sum of attribute weights The topic name is an attribute as well Default setting: 1 for all attribute weights,

user can change this setting

List of attributes andtheir weights:

Article: 1Audio recording: 2Bibliography: 2Biographical article: 1Date of birth: 1Date of death: 1Description: 1Editorial guidelines: 1 Gallery: 1Illustration: 1Libretto: 2Note: 1Poster: 1Premiere date:1Sound clip: 2Synopsis: 1Video recording: 2Web page: 2Web site: 3

Weights assignment:Total topic weight wi

Total topic weight wi for each individual topic i

The partial weight c is calculated for each individual topic in the ontology The next part of the total topic weight w is derived from associations with

surrounding topics j Coefficient k influences the importance of surrounding topics After calculating the total topic weights the results are normalized to

interval (0,1

j

jijii cakcw

Weights assignment: Weights of associations categories aij

Association weight a 0, 1; a R Three categories: hierarchical, defining, contextual Weights setting of all categories at 1 advantages topics which have

great number of associations (independently of association types)

k = 0.2weights of all attributes: 1

Weights assignment: Influence of coefficient k

Coefficient k 0, 1; k R Influences the importance of partial weights of surrounding topics The less coefficient value the greater importance is assigned to the

central topic

weights of all attributes: 1weights of all categories: 1.0

Parameters settingResults

Better results are achieved with more specific setting of attribute weights and category weights than with default setting

Example:

Weights of attributes in the list above.

k = 0.2

Weights of categories of associations:defining – 1.0hierarchical – 0.5contextual – 0.2

Summary(repetition is the mother of wisdom)

The user assigns the weights to all attributes which can occur within a topic Default: weight of all attributes 1

The user divides associations into three categories and assigns the weights to these categories Default: weight of all associations 1

The total topic weight will be calculated for each individual topic in the ontology

These total weights values will be normalized in accordance with the maximal value

Application

Information asset points to potential usefulness of information contained in the topic for the user The user’s insight is taken into consideration, if desired

Topics found during a search in the ontology may be ranked according to their information asset The total topic weights are calculated just once

The quantification of information asset is used as measure and statistical data

Scheme of topics interconnectionMatrix notation

Algorithm

Thank you for your attention