![Evaluation of Instances Asset in a Topic Maps Ontology Petra Haluzová [email_address] Department of Informatics and Telecommunications Faculty of Transportation Sciences Czech Technical University in Prague TMRA 2010](https://image.slidesharecdn.com/presentationtmra-101011111426-phpapp02/85/Evaluation-of-Instances-Asset-in-a-Topic-Maps-Based-Ontology-1-320.jpg)
Evaluation of Instances Asset in a Topic Maps-Based Ontology
- 1. Evaluation of Instances Asset in a Topic Maps Ontology Petra Haluzová [email_address] Department of Informatics and Telecommunications Faculty of Transportation Sciences Czech Technical University in Prague TMRA 2010
- 2. 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) Colo u r: black ? Breed: dachshund Photo: http://... Association s – owner, vaccination,…
- 3. Weights assignment: Partial weight of topic c i 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 and their weights: Article: 1 Audio recording: 2 Bibliography: 2 Biographical article: 1 Date of birth: 1 Date of death: 1 Description: 1 Editorial guidelines: 1 Gallery: 1 Illustration: 1 Libretto: 2 Note: 1 Poster: 1 Premiere date:1 Sound clip: 2 Synopsis: 1 Video recording: 2 Web page: 2 Web site: 3
- 4. Weights assignment: Total topic weight w i Total topic weight w i 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
- 5. Weights assignment: Weights of associations categories a ij Association weight a 0, 1 ; a R Three categories: hierarchical, defining, contextual Weights setting of all categories at 1 advantages topic s which have great number of associations (independently of association types) k = 0 . 2 weights of all attributes: 1
- 6. 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: 1 weights of all categories: 1 . 0
- 7. Parameters setting Results 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 . 0 hierarchical – 0 . 5 contextual – 0 . 2
- 8. 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
- 9. 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
- 10. Scheme of topics interconnection Matrix notation
- 11. Algorithm
- 12. Thank you for your attention