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tfm_chester_madison.pdf (732.37 KB)

Tipus de document

Treball de fi de màster

Data de publicació

2024-06-30

Llicència de publicació

cc-by-nc-nd (c) Madison Elizabeth Chester, 2023
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/215063

Graph-based entity resolution and completion for academic knowledge graphs

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Resum

[en] This thesis explores graph-based entity resolution and completion within academic knowledge graphs, focusing on the complex relationships between authors and papers and between papers themselves using Graph Neural Networks (GNNs). Raw data sourced from the American Physical Society underwent meticulous data cleaning and entity resolution analysis to prepare it for the proposed network. Author grouping strategies and citation overlap were examined, revealing distinct clusters of researchers and insightful patterns in citation relationships. A GNN model was developed using SAGEConv layers and heterogeneous transformations to capture local graph structures for accurate link prediction. This model was optimized with mini-batch loading and edge-level splits, which contributed to its high accuracy in predicting links between authors and papers, as demonstrated in the evaluation. The findings underscore the model’s capability to uncover hidden relationships and trends within the academic graph. Future work could enhance the model by incorporating additional features, experimenting with alternative GNN architectures, and including more detailed citation contexts and collaboration networks. Overall, this thesis highlights the transformative potential of GNNs in entity resolution and completion for academic knowledge graphs.

Descripció

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2023-2024. Tutor: Dimitri Marinelli i Albert Díaz Guilera

Matèries

Teoria de grafs, Xarxes neuronals (Informàtica), Investigadors, Treballs de fi de màster, Literatura científica

Matèries (anglès)

Graph theory, Neural networks (Computer science), Research workers, Master's thesis, Scientific literature

Citació

Col·leccions

Màster Oficial - Fonaments de la Ciència de Dades

Citació

CHESTER, Madison elizabeth. Graph-based entity resolution and completion for academic knowledge graphs. [consulta: 29 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/215063]

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