A new ontological-content-based method for ranking the relevancy of items in the electronic newspapers domain is proposed. The method is being implemented in ePaper, a personalized electronic newspaper research project. The content-based part of the filtering method of ePaper utilizes a hierarchical ontology of news items. The method considers common and "close" ontology concepts appearing in the user's profile and in the item's profile, measuring the hierarchical distance between concepts in the two profiles. Based on the number of common and related concepts, and their distances from each other, the filtering algorithm computes the similarity between items and users, and rank-orders the news items according to their relevancy to each user, thus providing a personalized newspaper. We have conducted evaluations of the filtering method, examining various parameters. A group of subjects, each having defined an initial content-based profile using the news ontology concepts, read news items from a certain electronic newspaper and expressed the relevancy of each item to them. In different runs of the algorithm on the same data, we changed several parameters of the algorithm, and compared the results with the users' ratings. We discovered that the filtering method, which considers not only common concepts but also hierarchically related concepts, yields significantly better quality of filtering compared to using only common concepts. Moreover, we were able to find optimal values of similarity scores according to the hierarchical distance between related concepts.