M. Herold. Albert-Ludwigs-University, Freiburg, Germany, Diploma Thesis, (Dezember 2005)
Ontologies specify the knowledge about a domain of interest using formal semantics, for example the products offered by an online-shop as well as a taxonomy of product categories organizing the products for better browsing and searching or an information portal like a digital library organizing books in a hierarchical category system. Personalized semantic applications allow users to have a personal copy of the ontology and to tailor it to their needs: e.g., they can select to see only a subset of all the categories available, merge existing categories, or introduce completely new categories. In large information portals one can try to support users in maintaining their personal ontology by recommending them changes based on ontologies of other users. For example, if a user has many articles about "Java" and "C++" in his personal bibliography but all of them in a common category "programming languages", but other users have a better organization using two subcategories for "Java" and "C++", respectively, one would like to recommend the user to add these two subcategories and assign the papers accordingly. Methods from Machine Learning, especially from Recommender Systems and Collaborative Filtering can be applied to learn such recommendations. The task of this topic is, to implement different strategies for recommending categories, their super- und subcategories and the assignment of products to the categories: very simple strategies that take into account other users' ontologies only in a summary way, e.g., always recommend the most often used concept first, as well as personalized recommenders based on collaborative filtering methods. The strategies should be evaluated on several synthetic datasets.
