DeepLENS: Deep Learning for Entity Summarization
This work addresses entity summarization for knowledge graph users, presenting a novel deep learning approach that improves performance.
The authors tackled entity summarization over knowledge graphs by introducing DeepLENS, a deep learning model that uses textual semantics and triple interdependence scoring, which significantly outperformed existing methods on a public benchmark.
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.