WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

By Luyu Wang, Yujia Li, Ozlem Aslan, Oriol Vinyals. 2021

33 citations recorded by the source corpus.

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets. We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph -> text generation, graph -> text retrieval and text -> graph retrieval. We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement.

Subfields: Artificial Intelligence, Information Retrieval & Search, Graph Learning

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “Thus, several studies explore modifying LM training objectives to incorporate factual information using either knowledge bases (KBs) or graphs (Yu et al., 2022): each token prediction is scored not only on its likelihood given the previously generated context, but also on whether the generation is g”
Relational Memory-Augmented Language Models (2022, methodology): “Our model is closely related to previous work on grounding autoregressive language models with knowledge graphs (Ahn et al., 2016; Logan et al., 2019; Hayashi et al., 2020; Wang et al., 2021a).”
A Survey on Vision--Language--Action Models for Embodied AI (2024, unknown): “Sentence-level graphs can be useful in dialog tracking [331], fact checking [332], etc. Document-level graphs include knowledge graphs [333], Table VIII: Vision-language models.”