activity
20192022
most citedSpace4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network

34 citations · 80 across the 13 of their papers we have counts for

collaborators

17 papers

cs.IR2022

Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property

Yuxin Liu, Yawen Li, Yingxia Shao +1

In the age of big data, the demand for hidden information mining in technological intellectual property is increasing in discrete countries. Definitely, a considerable number of gr…

cs.IR2022

A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents

Runze Fang, Junping Du, Yingxia Shao +1

The relation triples extraction method based on table filling can address the issues of relation overlap and bias propagation. However, most of them only establish separate table f…

cs.IR20224 cited

Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings

Shitao Xiao, Zheng Liu, Weihao Han +10

Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and Product Quantization (PQ), have been widely applied to embedding based document retrieval thanks…

cs.CL2022

Scientific and Technological Text Knowledge Extraction Method of based on Word Mixing and GRU

Suyu Ouyang, Yingxia Shao, Junping Du +1

The knowledge extraction task is to extract triple relations (head entity-relation-tail entity) from unstructured text data. The existing knowledge extraction methods are divided i…

cs.DB2022

An I/O-Efficient Disk-based Graph System for Scalable Second-Order Random Walk of Large Graphs

Hongzheng Li, Yingxia Shao, Junping Du +2

Random walk is widely used in many graph analysis tasks, especially the first-order random walk. However, as a simplification of real-world problems, the first-order random walk is…

cs.CL2022

An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information

Yuhui Wang, Junping Du, Yingxia Shao

Patent texts contain a large amount of entity information. Through named entity recognition, intellectual property entity information containing key information can be extracted fr…