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Aggregate queries on knowledge graphs: Fast approximation with semantic-aware sampling
A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a
schema-flexible manner. Aggregate query is a fundamental query over KGs, eg,“what is the …
schema-flexible manner. Aggregate query is a fundamental query over KGs, eg,“what is the …
ChatEL: Entity linking with chatbots
Y Ding, Q Zeng, T Weninger - ar**, current traffic management systems have
become inadequate to meet the requirements of intelligent supervision. In particular, with …
become inadequate to meet the requirements of intelligent supervision. In particular, with …
Adaptive deep learning for entity disambiguation via knowledge-based risk analysis
The state-of-the-art performance on entity disambiguation has been reached by deep neural
networks. However, the task remains very challenging due to the complexity of natural …
networks. However, the task remains very challenging due to the complexity of natural …
[HTML][HTML] An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance
B Jia, C Wang, H Zhao, L Shi - Symmetry, 2022 - mdpi.com
In the era of big data, a large amount of unstructured text data springs up every day. Entity
linking involves relating the mentions found in the texts to the corresponding entities, which …
linking involves relating the mentions found in the texts to the corresponding entities, which …
[HTML][HTML] Entity Linking Model Based on Cascading Attention and Dynamic Graph
H Li, C Li, Z Sun, H Zhu - Electronics, 2024 - mdpi.com
The purpose of entity linking is to connect entity mentions in text to real entities in the
knowledge base. Existing methods focus on using the text topic, entity type, linking order …
knowledge base. Existing methods focus on using the text topic, entity type, linking order …