{"title":"从越南文文本中提取三元组,创建知识图谱","authors":"Huong Duong To, P. Do","doi":"10.1109/KSE50997.2020.9287471","DOIUrl":null,"url":null,"abstract":"Knowledge graph (KG) plays an increasingly important role in the current technology era. It is very useful in many fields such as searching for information, supporting question answering systems and in other AI applications, etc. Besides the private Knowledge Graphs like Google's \"Knowledge graph\", we also have Open Knowledge graphs as DBpedia, YAGO, ... But generally, these Open Knowledge graphs contain very little data in Vietnamese. Due to this practice, our team proposed a way to create Vietnamese Knowledge graph by automatically scratching the Vietnamese text on the website as input, then using Named-entity recognition (NER) to recognize entities as nouns and combined with POS tag identifies words as verbs to extract triple in the simple sentences of the paragraph. The triple was then loaded into Neo4j to visualize the Knowledge graph.","PeriodicalId":275683,"journal":{"name":"2020 12th International Conference on Knowledge and Systems Engineering (KSE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Extracting triples from Vietnamese text to create knowledge graph\",\"authors\":\"Huong Duong To, P. Do\",\"doi\":\"10.1109/KSE50997.2020.9287471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Knowledge graph (KG) plays an increasingly important role in the current technology era. It is very useful in many fields such as searching for information, supporting question answering systems and in other AI applications, etc. Besides the private Knowledge Graphs like Google's \\\"Knowledge graph\\\", we also have Open Knowledge graphs as DBpedia, YAGO, ... But generally, these Open Knowledge graphs contain very little data in Vietnamese. Due to this practice, our team proposed a way to create Vietnamese Knowledge graph by automatically scratching the Vietnamese text on the website as input, then using Named-entity recognition (NER) to recognize entities as nouns and combined with POS tag identifies words as verbs to extract triple in the simple sentences of the paragraph. The triple was then loaded into Neo4j to visualize the Knowledge graph.\",\"PeriodicalId\":275683,\"journal\":{\"name\":\"2020 12th International Conference on Knowledge and Systems Engineering (KSE)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 12th International Conference on Knowledge and Systems Engineering (KSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KSE50997.2020.9287471\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 12th International Conference on Knowledge and Systems Engineering (KSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KSE50997.2020.9287471","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extracting triples from Vietnamese text to create knowledge graph
Knowledge graph (KG) plays an increasingly important role in the current technology era. It is very useful in many fields such as searching for information, supporting question answering systems and in other AI applications, etc. Besides the private Knowledge Graphs like Google's "Knowledge graph", we also have Open Knowledge graphs as DBpedia, YAGO, ... But generally, these Open Knowledge graphs contain very little data in Vietnamese. Due to this practice, our team proposed a way to create Vietnamese Knowledge graph by automatically scratching the Vietnamese text on the website as input, then using Named-entity recognition (NER) to recognize entities as nouns and combined with POS tag identifies words as verbs to extract triple in the simple sentences of the paragraph. The triple was then loaded into Neo4j to visualize the Knowledge graph.