Bøger af Gerd Stumme

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the "e;noise"e; that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

Finding knowledge - or meaning - in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data ("e;Web mining"e;). This is due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.

We are pleased to bring you this collection of papers for the Ninth International Conference on Conceptual Structures (ICCS), representing continued excellence in conceptual structures research. We have adopted the title \Broadening the Base,"e; acknowledging the importance of contributions from scholars in many research areas. The rst ICCS meetings focused primarily on Sowa's conceptual graphs; in recent years, however, the ICCS conference series has intentionally widened its scope to stimulate research across domain boundaries. We hope that this stimulation is further enhanced by ICCS 2001 continuing the long tradition of lively conferences about Conceptual Structures. We wish to express our appreciation to all the authors of submitted papers, to the general chair, to the members of the editorial board and the program committee, and to the additional reviewers for making ICCS 2001 a valuable contribution to the knowledge processing research eld. We would also like to acknowledge the leadership of Guy Mineau and Bernhard Ganter in providing a solid framework for an open and e ective reviewing process. Very special thanks go to the local organizers for making the conference possible and, furthermore, an enjoyable and inspiring event. We are grateful to the University of Alabama in Huntsville, and the University of Karlsruhe for their generous support.

Dieses Buch stellt Methoden der Begrifflichen Wissensverarbeitung vor und prasentiert Anwendungen aus unterschiedlichen Praxisfeldern. Im Methodenteil wird in moderne Techniken der Begrifflichen Datenanalyse und Wissensverarbeitung eingefuhrt. Hierbei werden die mathematischen Grundlagen abgehandelt und durch zahlreiche Beispiele anschaulich gemacht. Der zweite Teil des Buches richtet sich verstarkt an potentielle Anwender. An ausgewahlten Anwendungen wird die Vorgehensweise bei der Datenanalyse und dem Information Retrieval mit den Methoden der Begrifflichen Wissensverarbeitung vorgestellt und ihr Potential aufgezeigt.