arXiv:2003.03736
공식 코드 · Papers with Code
DeepLENS: Deep Learning for Entity Summarization
원문 초록
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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Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
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