A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
[PDF][PDF] 基于领域知识图谱的生命医学学科知识发现探析
胡正银， 刘蕾蕾， 代冰， 覃筱楚 - 数据分析与知识发现, 2020 - researchgate.net
基于领域知识图谱的生命医学学科知识发现探析* A Study on Knowledge Graph-based Subject
Knowledge Discovery i Page 1 基于领域知识图谱的生命医学学科知识发 现探析* 胡正银1,2,刘 …
Knowledge Discovery i Page 1 基于领域知识图谱的生命医学学科知识发 现探析* 胡正银1,2,刘 …
Resolving the imbalance issue in hierarchical disciplinary topic inference via llm-based data augmentation
In addressing the imbalanced issue of data within the realm of Natural Language
Processing, text data augmentation methods have emerged as pivotal solutions. This data …
Processing, text data augmentation methods have emerged as pivotal solutions. This data …
Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents
Q Zhang, H Chen, Y Cai, W Dong, P Liu - Applied Sciences, 2023 - mdpi.com
The existing methods for document-level event extraction mainly face two challenges. The
first challenge is effectively capturing event information that spans across sentences. The …
first challenge is effectively capturing event information that spans across sentences. The …
Discovering subject knowledge in life and medical sciences with knowledge graph
H Zhengyin, L Leilei, B Dai… - Data Analysis and …, 2020 - manu44.magtech.com.cn
[Objective] This paper explores new methods for deep subject knowledge discovery using
multi-source heterogeneous data.[Methods] First, we constructed a SPO semantic network of …
multi-source heterogeneous data.[Methods] First, we constructed a SPO semantic network of …
An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships
Geoscience knowledge graph (GKG) can organize various geoscience knowledge into a
machine understandable and computable semantic network and is an effective way to …
machine understandable and computable semantic network and is an effective way to …
RDKG: A Reinforcement Learning Framework for Disease Diagnosis on Knowledge Graph
Automatic disease diagnosis from symptoms has attracted much attention in medical
practices. It can assist doctors and medical practitioners in narrowing down disease …
practices. It can assist doctors and medical practitioners in narrowing down disease …
An Improved DDPG and Its Application in Spacecraft Fault Knowledge Graph
X Xing, S Wang, W Liu - Sensors, 2023 - mdpi.com
We construct a spacecraft performance-fault relationship graph of the control system, which
can help space robots locate and repair spacecraft faults quickly. In order to improve the …
can help space robots locate and repair spacecraft faults quickly. In order to improve the …
A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph
The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a
challenge for knowledge engineering to construct a high-quality domain knowledge graph …
challenge for knowledge engineering to construct a high-quality domain knowledge graph …