Existing works on KG-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WikiBIO , WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However  in practice, the input knowledge could be more than enough, because the output description may only want to cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such  practical scenario in KG-to-text. Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities  , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text . We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to ensemble the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO , WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However , in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG) , which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions . We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.
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Existing works on KG-to-text
Previous works on knowledge-to-text
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WikiBIO
WIKIBIO
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,
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because
since
D
want to
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a
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exploring large knowledge graphs (KG) to retrieve
retrieving
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from a large knowledge graph (KG)
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suffered
suffer
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description text
descriptions
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ensemble
extract