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Cuvillier Verlag

30 Jahre Kompetenz im wissenschaftlichen Publizieren
Internationaler Fachverlag für Wissenschaft und Wirtschaft

Cuvillier Verlag

 Factorization Models for Multi-Relational Data

Printausgabe
EUR 26,20 EUR 24,89

E-Book
EUR 18,34

Factorization Models for Multi-Relational Data

Lucas Drumond (Autor)

Vorschau

Inhaltsverzeichnis, PDF (45 KB)
Leseprobe, PDF (97 KB)

ISBN-13 (Printausgabe) 9783954047345
ISBN-13 (E-Book) 9783736947344
Sprache Englisch
Seitenanzahl 136
Umschlagkaschierung matt
Auflage 1. Aufl.
Erscheinungsort Göttingen
Promotionsort Hildesheim
Erscheinungsdatum 20.06.2014
Allgemeine Einordnung Dissertation
Fachbereiche Geisteswissenschaften
Informatik
Schlagwörter Factorization Models, Relational Learning
Beschreibung

Mining multi-relational data has gained relevance in the last years and found applications in a number of tasks like recommender systems, link prediction, RDF mining, natural language processing, protein-interaction prediction and social network analysis just to cite a few. Appropriate machine learning models for such tasks must not only be able to operate on large scale scenarios, but also deal with noise, partial inconsistencies, ambiguities, or duplicate entries in the data. In recent years there has been a growing interest on multi-relational factorization models since they have shown to be a scalable and effective approach for multi-relational learning. This thesis formalizes the relational learning problem and investigates open issues in the state-of-the-art factorization models for multi-relational data. Specifically it studies how to deal with the open world assumption present in many real world relational datasets and how to optimize models for multiple target relations.