@conference {KatiforiGVLH07,
	title = {Creating an Ontology for the User Profile: Method and Applications},
	booktitle = {RCIS},
	year = {2007},
	month = {4/2007},
	pages = {407{\textendash}412},
	abstract = {User profiling is commonly employed nowadays to enhance usability as well as to support personalization, adaptivity and other user-centric features. Insofar, application designers model user profiles mainly in an ad-hoc manner, hindering thus application interoperability at the user profile level, increasing the amount of work to be done and the possibility of errors or omissions in the profile model. This work aims at creating a user profile ontology that incorporates concepts and properties used to model the user profile. Existing literature, applications and ontologies related to the domain of user context and profiling have been taken into account in order to create a general, comprehensive and extensible user model. This ontology can be used as a reference model, in order to alleviate the aforementioned issues. The model, available for download, is exemplified through its application in two different areas, personal information management and adaptive visualization.},
	author = {Akrivi Katifori and M. Golemati and Costas Vassilakis and Giorgos Lepouras and C. Halatsis}
}
@conference {Golemati200662,
	title = {A context-based adaptive visualization environment},
	booktitle = {Proceedings of the International Conference on Information Visualisation},
	year = {2006},
	note = {cited By (since 1996) 0},
	month = {7},
	pages = {62-67},
	abstract = {Digital libraries and historical archives are increasingly employing visualization systems to facilitate the information retrieval and knowledge extraction tasks of their users. Typically, each organization employs a single visualization system, which may not suit best the needs of certain user groups, specific tasks, or properties of document collections to be visualized. In this paper we present a context-based adaptive visualization environment, which embeds a set of visualization methods into a visualization library, from which the most appropriate one is selected for presenting information to the user. Methods are selected by examining parameters related to the user profile, system configuration and the set of data to be visualized, and employing a set of rules to assess the suitability of each method. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria. Β{\textcopyright} 2006 IEEE.},
	keywords = {(PL) properties, Adaptive systems, Adaptive visualization, Behavioral research, Digital libraries, document collections, Extraction, Information analysis, Information science, Information services, Information systems, Information visualization, Knowledge extraction, Libraries, Retrieval (MIR), Search engines, Selection criterion, Set of rules, Solvent extraction, Specific tasks, System configurations, User behaviors, User groups, user profiling, Visualization, visualization methods, Visualization systems},
	doi = {10.1109/IV.2006.5},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-45149121827\&partnerID=40\&md5=ec45ad555b132faaf0bd2ca53e3292dc},
	author = {M. Golemati and C. Halatsis and Costas Vassilakis and Akrivi Katifori and Giorgos Lepouras}
}
