Konferenzpaper | 2024 | Peer reviewed

Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks

Autor(en)

Blum, Moritz ; Nolano, Gennaro ; Ell, Basil

; Cimiano, Philipp

Titel des Konferenzbandes

Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING

Name der Konferenz

Deep Learning and Linguistic Linked Data Workshop at LREC-COLING

URL

https://pub.uni-bielefeld.de/record/2988690

Weitere Details

Sprache

englisch

Darstellungsform

Text

Graph Neural Networks (GNNs) have been applied successfully to various NLP tasks, particularly Relation Extraction (RE). Even though most of these approaches rely on the syntactic dependency tree of a sentence to derive a graph representation, the impact of this choice compared to other possible graph representations has not been evaluated. We examine the effect of representing text though a graph of different graph representations for GNNs that are applied to RE, considering, e.g., a fully connected graph of tokens, of semantic role structures, and combinations thereof. We further examine the impact of background knowledge injection from Knowledge Graphs (KGs) into the graph representation to achieve enhanced graph representations. Our results show that combining multiple graph representations can improve the model's predictions. Moreover, the integration of background knowledge positively impacts scores, as enhancing the text graphs with Wikidata features or WordNet features can lead to an improvement of close to 0.1 in F1.

Zitierstile

Blum M, Nolano G, Ell B, Cimiano P. Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks. In: Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING. 2024.

In die Zwischenablage einfügen

Blum, M., Nolano, G., Ell, B., & Cimiano, P. (2024). Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks. Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING

In die Zwischenablage einfügen

Blum, Moritz, Nolano, Gennaro, Ell, Basil, and Cimiano, Philipp. 2024. “Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks”. In Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING.

In die Zwischenablage einfügen

Blum, M., Nolano, G., Ell, B., and Cimiano, P. (2024). “Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks” in Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING.

In die Zwischenablage einfügen

Blum, M., et al., 2024. Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks. In Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING.

In die Zwischenablage einfügen

M. Blum, et al., “Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks”, Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING, 2024.

In die Zwischenablage einfügen

Blum, M., Nolano, G., Ell, B., Cimiano, P.: Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks. Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING. (2024).

In die Zwischenablage einfügen

Blum, Moritz, Nolano, Gennaro, Ell, Basil, and Cimiano, Philipp. “Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks”. Proceedings of the Deep Learning and Linguistic Linked Data Workshop at LREC-COLING. 2024.

AMA

APA (6th ed.)

Chicago

Frontiers

Harvard

IEEE

LNCS

MLA