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arXiv 2003.037362020-03-08cs.IRcs.CLcs.DB

DeepLENS: Deep Learning for Entity Summarization

National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）

Qingxia Liu, Gong Cheng, Yuzhong Qu

备注：: 6 pages, submitted to DL4KG 2020

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Entity summarization has been a prominent task over knowledge graphs. While existing methods are mainly unsupervised, we present DeepLENS, a simple yet effective deep learning model where we exploit textual semantics for encoding triples and we score each candidate triple based on its interdependence on other triples. DeepLENS significantly outperformed existing methods on a public benchmark.

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