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Factorization Models for Multi-Relational Data

Lucas Drumond
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Mining multi-relational data has gained relevance in the last years and found applications in a number of tasks like recommender ...

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Description

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.

Informations sur le produit

Titre:
Factorization Models for Multi-Relational Data
Auteur:
Lucas Drumond
EAN:
9783736947344
ISBN:
978-3-7369-4734-4
Format:
eBook (pdf)
Producteur:
Cuvillier Verlag
Editeur:
Cuvillier Verlag
Genre:
Sciences humaines en général
Parution:
20.06.2014
Protection contre la copie numérique:
filigrane numérique
Taille de fichier:
1.34 MB
Nombre de pages:
136
Année:
2014

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