Your browser does not support JavaScript!

Home    Collections    Type of Work    Post-graduate theses  

Post-graduate theses

Current Record: 16 of 5442

Back to Results Previous page
Next page
Add to Basket
[Add to Basket]
Identifier 000443666
Title Knowledge graph embedding methods for entity alignment
Alternative Title Μέθοδοι διανυμαστικής αναπαράστασης γράφων γνώσης για αντιστοίχιση οντοτήτων
Author Φανουράκης, Νικόλαος Ε.
Thesis advisor Χριστοφίδης, Βασίλης
Reviewer Κομοδάκης, Νίκος
Πλεξουσάκης, Δημήτρης
Kotzinos, Dimitris
Abstract In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A frequent task when integrating knowledge from different KGs is to find which subgraphs refer to the same real-world entity. Recently, neural networks and deep learning techniques have been proposed for entity alignment tasks, that learn a vector-space representation (i.e., embedding) of entities which preserves their similarity in the original KGs. A wide variety of supervised, unsupervised, and semi-supervised embedding methods have been proposed in the literature that exploit the structural information (relation based) of entities in the KGs, or their attribute values (attribute based). Still, a quantitative assessment of their strengths and weaknesses on real-world KGs using different performance metrics and KG characteristics is missing. In this thesis, we conduct a meta-level analysis of popular embedding methods for entity alignment based on a statistically sound methodology. Our analysis reveals interesting statistically significant correlations of different embedding methods with various meta-features extracted by KGs. Finally, we are able to rank in a statistically significant way their performance across all real-world KGs of our testbed, while also to demonstrate their trade-offs in terms of effectiveness and efficiency. To the best of our knowledge, there are no experimental studies that provide such a type of meta-level analysis.
Language English
Issue date 2021-11-26
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link Bookmark and Share
Views 8

Digital Documents
No preview available

No permission to view document.
It won't be available until: 2022-11-26