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Media type:
Doctoral Thesis;
E-Book;
Electronic Thesis
Title:
Multiple Objective Learning for Effective Knowledge Graph Embedding
Contributor:
Sadeghi, Afshin
[Author]
imprint:
Universitäts- und Landesbibliothek Bonn, 2023-03-15
Language:
English
DOI:
https://doi.org/20.500.11811/10695
Origination:
Footnote:
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Description:
Over the past decade, knowledge graphs (KGs) have become popular for capturing structured domain knowledge. Knowledge graphs particularly allow the effortless integration of heterogeneous data into a coherent model. Besides applications in data integration, KGs are at the center of many artificial intelligence studies as an expressive data model for representation learning purposes. Knowledge graph embedding (KGE) methods produce a latent representation of KGs entities showing applicability potential for solving downstream tasks, including link prediction and node classification. KGE also supports Named Entity Resolution in NLP tasks and is applied in Question Answering Systems. Existing KGE models have achieved excellent results over simple knowledge graphs, where they contain only a few relation patterns that leak into each other. Besides, in simple knowledge graphs, the amount of entities with similar neighbors is lower, and the structure of the subgraphs is unique, so their entities are more easily distinguished. This work dives more into the study of KGE for complex knowledge graphs. In such KGs, distinct relation patterns show up significantly more, and similar substructures repeat over the network on a larger scale. Therefore, recognizing unique entities with limited knowledge about the direct neighbors and the limited recognition of relation patterns is remarkably more difficult. Complex knowledge graph embedding provides several challenges, such as understanding learning distinct relation patterns and graphical features of nodes. The lack of suitable datasets that emulate the difficulty of more complex knowledge graphs further adds to research gaps. Hence, in this thesis, we focus on the research objective of laying the foundations for the advancement of the state-of-the-art to better embed complex knowledge graphs by providing techniques to solve various challenges and resources to fill the research gaps. First, to effectively target the complex KGE challenge, we propose a multi-objective method that ...