3 citations · 6 across the 4 of their papers we have counts for
5 papers
Neural Entity Summarization with Joint Encoding and Weak Supervision
Junyou Li, Gong Cheng, Qingxia Liu +4
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions…
DeepLENS: Deep Learning for Entity Summarization
Qingxia Liu, Gong Cheng, Yuzhong Qu
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 mode…
ESBM: An Entity Summarization BenchMark
Qingxia Liu, Gong Cheng, Kalpa Gunaratna +1
Entity summarization is the problem of computing an optimal compact summary for an entity by selecting a size-constrained subset of triples from RDF data. Entity summarization supp…
Entity Summarization: State of the Art and Future Challenges
Qingxia Liu, Gong Cheng, Kalpa Gunaratna +1
The increasing availability of semantic data has substantially enhanced Web applications. Semantic data such as RDF data is commonly represented as entity-property-value triples. T…
Graph-based Ontology Summarization: A Survey
Seyedamin Pouriyeh, Mehdi Allahyari, Qingxia Liu +4
Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of…