{"title":"生物数据库知识图谱的构建与检索","authors":"N. Zaki, Chandana Tennakoon, Hany Al Ashwal","doi":"10.1109/ICRIIS.2017.8002465","DOIUrl":null,"url":null,"abstract":"The number of biological databases available both in the public domain and in private keep on increasing every day. Scientists and researchers need to analyze and make use of the data stored in different databases. One limitation is that these databases are stored in diverse formats. However, semantic web methods have introduced the Resource description format (RDF) to unify heterogeneous databases. In this paper we illustrate how to construct a knowledge graph out of biological RDF databases by connecting possibly related data. We also show how the resulting knowledge graph can be made compact. We, then show how free-text search can be implemented to access nodes in the knowledge graph. Finally, we introduce a way to display and navigate the created knowledge graph.","PeriodicalId":384130,"journal":{"name":"2017 International Conference on Research and Innovation in Information Systems (ICRIIS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Knowledge graph construction and search for biological databases\",\"authors\":\"N. Zaki, Chandana Tennakoon, Hany Al Ashwal\",\"doi\":\"10.1109/ICRIIS.2017.8002465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The number of biological databases available both in the public domain and in private keep on increasing every day. Scientists and researchers need to analyze and make use of the data stored in different databases. One limitation is that these databases are stored in diverse formats. However, semantic web methods have introduced the Resource description format (RDF) to unify heterogeneous databases. In this paper we illustrate how to construct a knowledge graph out of biological RDF databases by connecting possibly related data. We also show how the resulting knowledge graph can be made compact. We, then show how free-text search can be implemented to access nodes in the knowledge graph. Finally, we introduce a way to display and navigate the created knowledge graph.\",\"PeriodicalId\":384130,\"journal\":{\"name\":\"2017 International Conference on Research and Innovation in Information Systems (ICRIIS)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Research and Innovation in Information Systems (ICRIIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRIIS.2017.8002465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Research and Innovation in Information Systems (ICRIIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRIIS.2017.8002465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Knowledge graph construction and search for biological databases
The number of biological databases available both in the public domain and in private keep on increasing every day. Scientists and researchers need to analyze and make use of the data stored in different databases. One limitation is that these databases are stored in diverse formats. However, semantic web methods have introduced the Resource description format (RDF) to unify heterogeneous databases. In this paper we illustrate how to construct a knowledge graph out of biological RDF databases by connecting possibly related data. We also show how the resulting knowledge graph can be made compact. We, then show how free-text search can be implemented to access nodes in the knowledge graph. Finally, we introduce a way to display and navigate the created knowledge graph.