Abstract: Reported is the methodology of the semantic transformation of an initial user’s search query in the form of key words or key phrases to the resulting query composed of the relevant concepts of the domain ontology. Transformation methodology is based on incremental user profiling. The mapping of a user’s keywords to the concepts of the domain ontology is built according to the presented transformation rules. These rules are based on the usage of the rich set of the semantic relationships comprising subsumption, synonymy, instantiation and meronymy provided as the DAML+OIL ontology. ACM research papers domain is chosen for the methodology evaluation. Transformation algorithm is implemented in the research prototype as the combined capability of the query transformation agent and the ontology agent of the intelligent multi-agent information retrieval mediator 1. 1.

Ontologies offer a flexible and expressive layer of abstraction, very useful for capturing the semantics of information repositories and facilitating their retrieval either by the user or by the system to support user tasks. This work presents an ontology-based user profiler, in the context of a Personal Interaction Management System (PIMS). The profiler, based on an ontology of the users’ domain, enables them to create their personal ontology by initially choosing one of the available template ontologies as a starting point, which they subsequently populate and customize. The profiler employs a web interface which allows users to populate their personal ontology through forms, hiding ontology complexities and peculiarities. Forms are dynamically generated through ontology views, which are specified by ontology designers. Author Keywords User profile, ontology, web-based profiler.

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. Index Terms—user profile, ontology, user modeling, context I.

Recent research in the domain of Personal Information Management has recognized the need for a paradigm shift towards a more activity-oriented system. Ontologies, as semantic networks with a structure not dissimilar to the one used by the human brain for storing long-term knowledge, may be very useful as the basis of such a system. This work proposes the use of spreading activation over ontologies in order to provide to a task-based system and its associated tools with methods to record semantics related to documents and tasks and to support user context inference. Author Keywords Personal ontology, spreading activation, context inference.

We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms using either direct human relevance judgments or indirect judgments obtained from click-through data from millions of users. The rankings are thus optimized to this generic population of users, not to any specific user. We propose a generative model of relevance which can be used to infer the relevance of a document to a specific user for a search query. The user-specific parameters of this generative model constitute a compact user profile. We show how to learn these profiles from a user’s long-term search history. Our algorithm for computing the personalized ranking is simple and has little computational overhead. We evaluate our personalization approach using historical search data from thousands of users of a major Web search engine. Our findings demonstrate gains in retrieval performance for queries with high ambiguity, with particularly large improvements for acronym queries.