Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
This addresses the need for accurate and readable entity descriptions in knowledge graphs, which benefits both humans and machines, though it appears incremental as it builds on existing generative methods.
The paper tackles the problem of automatically generating type descriptions for entities in knowledge graphs, where existing methods produce ungrammatical or factually incorrect text, by proposing a head-modifier template-based method that improves readability and data fidelity, achieving state-of-the-art performance on datasets.
A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template-based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two automatic metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.