Developing new ideas and algorithms in the fields of graph processing and relational learning requires public datasets. While Wikidata is the largest open source knowledge graph, involving more than fifty million entities, it is larger than needed in many cases and even too large to be processed easily. Still, it is a goldmine of relevant facts and relations. Using this knowledge graph is time consuming and prone to task specific tuning which can affect reproducibility of results. Providing a unified framework to extract topic-specific subgraphs solves this problem and allows researchers to evaluate algorithms on common datasets. This paper presents various topic-specific subgraphs of Wikidata along with the generic Python code used to extract them. These datasets can help develop new methods of knowledge graph processing and relational learning.
KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）；Orange（法国电信运营商Orange）；INESC-ID（INESC-ID研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）
Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification
College of Engineering and Computer Science, VinUni-Illinois Smart Health Center, VinUniversity（工程与计算机科学学院，VinUni - 伊利诺伊智能健康中心，Vin大学）；School of Biomedical Engineering, International University, Vietnam National University HCMC（生物医学工程学院，胡志明市越南国立大学国际大学）
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
The Hospital for Sick Children (SickKids)（SickKids医院（病童医院））；SickKids Research Institute（SickKids研究所）；University of Toronto（多伦多大学）；University Hospital Augsburg（奥格斯堡大学医院）；Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）；Vector Institute（向量研究院）
LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition
XJTU-POLIMI Joint School, Xi'an Jiaotong University School of Computer Science；Technology, Xi'an Jiaotong University MIGU Video Co., Ltd., Shanghai, China
