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However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500\u00a0000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of ~8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application.<\/jats:p>","DOI":"10.1093\/bib\/bbaa344","type":"journal-article","created":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T06:03:36Z","timestamp":1607753016000},"source":"Crossref","is-referenced-by-count":144,"title":["PharmKG: a dedicated knowledge graph benchmark for bomedical data mining"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9747-4285","authenticated-orcid":false,"given":"Shuangjia","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Data and Computer Science at the Sun Yat-Sen University"}]},{"given":"Jiahua","family":"Rao","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science at the Sun Yat-Sen University"}]},{"given":"Ying","family":"Song","sequence":"additional","affiliation":[{"name":"School of Systems Science and Engineering at the Sun Yat-Sen University"}]},{"given":"Jixian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aladdin Healthcare Technologies Ltd"}]},{"given":"Xianglu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Aladdin Healthcare Technologies Ltd"}]},{"given":"Evandro Fei","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, L\u00f8renskog, Norway"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science and the National Super Computer Center at Guangzhou, Sun Yat-sen University, China"}]},{"given":"Zhangming","family":"Niu","sequence":"additional","affiliation":[{"name":"Aladdin Healthcare Technologies Ltd"}]}],"member":"286","published-online":{"date-parts":[[2020,12,21]]},"reference":[{"key":"2021072117024353800_ref1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1093\/bib\/bbv020","article-title":"A survey of current trends in computational drug repositioning","volume":"17","author":"Li","year":"2016","journal-title":"Brief Bioinform"},{"key":"2021072117024353800_ref2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.websem.2017.06.002","article-title":"Large-scale structural and textual similarity-based mining of knowledge graph to predict drug\u2013drug interactions","volume":"44","author":"Abdelaziz","year":"2017","journal-title":"J Web Semant"},{"key":"2021072117024353800_ref3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/415141a","article-title":"Functional organization of the yeast proteome by systematic analysis of protein complexes","volume":"415","author":"Gavin","year":"2002","journal-title":"Nature"},{"key":"2021072117024353800_ref4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1038\/nrg1272","article-title":"Network biology: understanding the cell's functional organization","volume":"5","author":"Barabasi","year":"2004","journal-title":"Nat Rev Genet"},{"key":"2021072117024353800_ref5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1037\/0033-295X.99.1.45","article-title":"Context, cortex, and dopamine: a connectionist approach to behavior and biology in schizophrenia","volume":"99","author":"Cohen","year":"1992","journal-title":"Psychol Rev"},{"key":"2021072117024353800_ref6","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1093\/bfgp\/els037","article-title":"Biological function through network topology: a survey of the human diseasome","volume":"11","author":"Janji\u0107","year":"2012","journal-title":"Brief Funct Genomics"},{"key":"2021072117024353800_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"key":"2021072117024353800_ref8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s41421-020-0153-3","article-title":"Network-based drug repurposing for novel coronavirus 2019-nCoV\/SARS-CoV-2","volume":"6","author":"Zhou","year":"2020","journal-title":"Cell Discov"},{"key":"2021072117024353800_ref9","article-title":"Translating embeddings for modeling multi-relational data","author":"Bordes"},{"key":"2021072117024353800_ref10","article-title":"Convolutional 2d knowledge graph embeddings. 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