{"title":"工业生产环境中的信息检索","authors":"Stefan Windmann, O. Niggemann","doi":"10.1109/ETFA.2018.8502506","DOIUrl":null,"url":null,"abstract":"The complexity of industrial production systems is steadily growing. Hence, the plant stuff has to search in an increasing number of documents within the daily work routine, e.g. in manuals, commissioning instructions, service notes, shift books, process data, repair instructions, data sheets, R/I flow charts, CAD drawings etc. To support the plant stuff, an intelligent search engine for industrial production environments is proposed in this paper. Characteristics of the developed search engine with respect to the domain of industrial production environments, e.g. tailored synonym replacements and document classifications, are outlined. Particularly, two methods for document classifications, a k-nearest-neighbor classifier and a Naive Bayes classifier, are evaluated with documents from industrial production environments.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"29 1","pages":"1205-1208"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Information Retrieval in Industrial Production Environments\",\"authors\":\"Stefan Windmann, O. Niggemann\",\"doi\":\"10.1109/ETFA.2018.8502506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The complexity of industrial production systems is steadily growing. Hence, the plant stuff has to search in an increasing number of documents within the daily work routine, e.g. in manuals, commissioning instructions, service notes, shift books, process data, repair instructions, data sheets, R/I flow charts, CAD drawings etc. To support the plant stuff, an intelligent search engine for industrial production environments is proposed in this paper. Characteristics of the developed search engine with respect to the domain of industrial production environments, e.g. tailored synonym replacements and document classifications, are outlined. Particularly, two methods for document classifications, a k-nearest-neighbor classifier and a Naive Bayes classifier, are evaluated with documents from industrial production environments.\",\"PeriodicalId\":6566,\"journal\":{\"name\":\"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"volume\":\"29 1\",\"pages\":\"1205-1208\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETFA.2018.8502506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2018.8502506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Information Retrieval in Industrial Production Environments
The complexity of industrial production systems is steadily growing. Hence, the plant stuff has to search in an increasing number of documents within the daily work routine, e.g. in manuals, commissioning instructions, service notes, shift books, process data, repair instructions, data sheets, R/I flow charts, CAD drawings etc. To support the plant stuff, an intelligent search engine for industrial production environments is proposed in this paper. Characteristics of the developed search engine with respect to the domain of industrial production environments, e.g. tailored synonym replacements and document classifications, are outlined. Particularly, two methods for document classifications, a k-nearest-neighbor classifier and a Naive Bayes classifier, are evaluated with documents from industrial production environments.