Ulli Waltinger, Dan G. Tecuci, Mihaela Olteanu, Vlad Mocanu, S. Sullivan
{"title":"USI Answers: Natural Language Question Answering Over (Semi-) Structured Industry Data","authors":"Ulli Waltinger, Dan G. Tecuci, Mihaela Olteanu, Vlad Mocanu, S. Sullivan","doi":"10.1609/aaai.v27i2.18985","DOIUrl":null,"url":null,"abstract":"This paper describes USI Answers a natural language question answering system for semi-structured industry data. The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system’s interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference on Innovative Applications of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aaai.v27i2.18985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
This paper describes USI Answers a natural language question answering system for semi-structured industry data. The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system’s interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.