{"title":"基于UNL知识基础结构的知识抽取系统(KEYS)","authors":"S. Alansary, M. Nagi","doi":"10.1109/TENCON.2015.7372957","DOIUrl":null,"url":null,"abstract":"With the exponential growth of information available on the internet pages, humans need to extract specific information has also witnessed an ever growing increase. This paper presents KEYS (Knowledge Extraction sYStem). It searches for information inside documents represented in Universal Networking Language (UNL), i.e., in semantic hyper-graphs. This allows for retrieval and extraction practices that are language-independent and semantically-oriented. It is expected to provide high-quality knowledge extraction through a shallow analysis of the source text into the UNL using a specific ontological relations then generate the resulting UNL document into several different target languages in a fully-automatic manner. This is expected to present a novel approach to the topic of identifying named entities; extracting names with all its types from a natural language texts. The Precision measurement of the system is 0.86 while recall measurement is 0.82.","PeriodicalId":22200,"journal":{"name":"TENCON 2015 - 2015 IEEE Region 10 Conference","volume":"4 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A knowledge extaction system (KEYS) based on UNL knowledge infrastructure\",\"authors\":\"S. Alansary, M. Nagi\",\"doi\":\"10.1109/TENCON.2015.7372957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the exponential growth of information available on the internet pages, humans need to extract specific information has also witnessed an ever growing increase. This paper presents KEYS (Knowledge Extraction sYStem). It searches for information inside documents represented in Universal Networking Language (UNL), i.e., in semantic hyper-graphs. This allows for retrieval and extraction practices that are language-independent and semantically-oriented. It is expected to provide high-quality knowledge extraction through a shallow analysis of the source text into the UNL using a specific ontological relations then generate the resulting UNL document into several different target languages in a fully-automatic manner. This is expected to present a novel approach to the topic of identifying named entities; extracting names with all its types from a natural language texts. The Precision measurement of the system is 0.86 while recall measurement is 0.82.\",\"PeriodicalId\":22200,\"journal\":{\"name\":\"TENCON 2015 - 2015 IEEE Region 10 Conference\",\"volume\":\"4 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TENCON 2015 - 2015 IEEE Region 10 Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENCON.2015.7372957\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TENCON 2015 - 2015 IEEE Region 10 Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON.2015.7372957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A knowledge extaction system (KEYS) based on UNL knowledge infrastructure
With the exponential growth of information available on the internet pages, humans need to extract specific information has also witnessed an ever growing increase. This paper presents KEYS (Knowledge Extraction sYStem). It searches for information inside documents represented in Universal Networking Language (UNL), i.e., in semantic hyper-graphs. This allows for retrieval and extraction practices that are language-independent and semantically-oriented. It is expected to provide high-quality knowledge extraction through a shallow analysis of the source text into the UNL using a specific ontological relations then generate the resulting UNL document into several different target languages in a fully-automatic manner. This is expected to present a novel approach to the topic of identifying named entities; extracting names with all its types from a natural language texts. The Precision measurement of the system is 0.86 while recall measurement is 0.82.