@inproceedings{blum-etal-2024-investigating,
    title = "Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks",
    author = "Blum, Moritz  and
      Nolano, Gennaro  and
      Ell, Basil  and
      Cimiano, Philipp",
    editor = "S{\'e}rasset, Gilles  and
      Oliveira, Hugo Gon{\c{c}}alo  and
      Oleskeviciene, Giedre Valunaite",
    booktitle = "Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.dlnld-1.1",
    pages = "1--13",
    abstract = "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 points in F1.",
}

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 points in F1.
Blum, Moritz Nolano, Gennaro Ell, Basil Cimiano, Philipp
Investigating the Impact of Different Graph Representations for Relation Extraction with Graph Neural Networks