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Learning Latent Features using Stochastic Neural Networks on Graph Structured Data

Weller, Tobias

Abstract (englisch):

Graph structured data are ubiquitous data structures, used to model relationships between entities. Graphs have become an important foundation to represent interactions between users in social networks, items in recommender systems, and interactions between drugs in bioinformatics. The main research problems in these areas include node clustering, node classification and link prediction. Especially the link prediction task is in bioinformatics of special interest toward the identification and development of new uses of existing or abandoned drugs since drug development is currently very time consuming and expensive. In the context of knowledge graphs, link prediction is also of special interest to automatically complete missing information to derive further knowledge. Likewise, node classification is an important research focus in the context of knowledge graphs, e.g. to automatically classify new entities according to their class affiliation and to complete missing class affiliation for existing entities.
In recent years, network embeddings are often trained for encoding the entities of graph structured data into a low-dimensional space whilst preserving the graph structure. ... mehr

Volltext §
DOI: 10.5445/IR/1000130825
Veröffentlicht am 25.03.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Hochschulschrift
Publikationsdatum 25.03.2021
Sprache Englisch
Identifikator KITopen-ID: 1000130825
Verlag Karlsruher Institut für Technologie (KIT)
Umfang vi, 139 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Prüfungsdatum 11.02.2021
Schlagwörter Machine Learning, Knowledge Graph, Embedding
Referent/Betreuer Sure-Vetter, Y.
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