Abstract
We present the first results of a research aimed at generating useradapted image descriptions from annotated knowledge sources. This system employs a User Model and several knowledge sources to select the image attributes to include in the description and the level of detail. Both ‘individual’ and ‘comparative-descriptions’ may be generated, by taking an appropriate ‘reference’ image according to the context and to an ontology of concepts in the domain to which the image refers; the comparison strategy is suited to the User background and to the interaction history. All data employed in the generation of these descriptions (the image, the discourse) are annotated by a XML-like language. Results obtained in the description of radiological images are presented, and the advantage of annotating knowledge sources are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cawsey, A. Presenting tailoed resource descriptions: Will XSLT do the job? In Proceedings of the 9th International WWW Conference, 2000.
Cawsey A., Bental D., Bruce E. and McAndrew, P.: Generating resource descriptions from metadata to support relevance assessments in retrieval. Proceedings of RIAO 2000.
De Carolis, B., de Rosis, F., Andreoli, C., Cavallo, V. and De Cicco, M.L.: The dynamic Generation of Hypertext Presentations of Medical Guidelines. The New Review of Hypermedia and Multimedia, 67–88 (1998).
De Carolis, B., de Rosis, F., Pizzutilo, S.: Generating User-Adapted Hypermedia from Discourse Plans. Fifth Congress of the Italian Association of Artificial Intelligence (AI*IA 97), Roma, (1997).
B. De Carolis, C. Pelachaud, I. Poggi, Verbal and non verbal discourse planning. Workshop on Achieving Human-like Behaviors. Autonomous Agents 2000. ACM Press.
de Rosis, F., De Carolis, B., Pizzutilo, S.: Automated Generation of Agent’s Behavior from Formal Models of Interaction. To appear in proceedings of AVI 2000, Palermo, Italy (2000).
Hammond, N. and Allinson, L.: Extending Hypertext for Learning: an Investigation of Access and Guidance Tools. People and Computers V, HCI 89, Cambridge University Press (1989).
Inote: Image Annotation Tool. http://jefferson.village.edu/iath/inote.html.
Markman., A.B. and Gentner., D.: Commmonalities and Differences in Similarity Comparisons. Memory and Cognition, 24, 2 (1996).
Moore, J., D. Participating in Explanatory Dialogues. Interpreting and Responding to Question in Context. ACL-MIT Press series in NLP, (1995).W3C: eXtensible Markup Language (XML). http://www.w3.org/xml/
Marcu, D.: Extending a Formal and Computational Model of Rhetorical Structure Theory with Intentional Structures à la Grosz and Sidner. The 18th International Conference on Computational Linguistics COLING’2000, Luxembourg, July 31-August 4, 2000.
M. Perkowitz, and O. Etzioni. Towards Adaptive Web Sites: Conceptual Framework and Case Study. Artificial Intelligence, 118(1–2), 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cassotta, M.T., De Carolis, B., de Rosis, F., Andreoli, C., De Cicco, M.L. (2001). User-Adapted Image Descriptions from Annotated Knowledge Sources. In: Esposito, F. (eds) AI*IA 2001: Advances in Artificial Intelligence. AI*IA 2001. Lecture Notes in Computer Science(), vol 2175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45411-X_29
Download citation
DOI: https://doi.org/10.1007/3-540-45411-X_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42601-1
Online ISBN: 978-3-540-45411-3
eBook Packages: Springer Book Archive