{"title":"The code lists case: Identifying and linking the key parts of fiscal datasets","authors":"Panagiotis-Marios Filippidis, Sotirios Karampatakis, Kleanthis Koupidis, Lazaros Ioannidis, Charalampos Bratsas","doi":"10.1109/SMAP.2016.7753404","DOIUrl":null,"url":null,"abstract":"Today, there is a growing need within citizens, journalists and many public bodies to explore and search for valuable information and knowledge in budget data, that are widely published by governments and municipalities across Europe. A significant element of these fiscal datasets are code lists, which serve not only for the coding and the simplicity of representation of budget concepts, but can also act as linking entities between budget datasets of different countries, bodies, years and structure, in order to facilitate their comparison. To this end, we made an extensive survey of code lists that refer or are related to fiscal or other concepts that are included in budget datasets, recording totally 239 international and national classifications. We selected some of these classifications to represent them semantically with SKOS and then we examined their linking possibilities. While manual and automated methods are insufficient for linking large code lists, tools using semi-automated methods, like Alignment seem to be more suitable for this specific task.","PeriodicalId":247696,"journal":{"name":"2016 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMAP.2016.7753404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Today, there is a growing need within citizens, journalists and many public bodies to explore and search for valuable information and knowledge in budget data, that are widely published by governments and municipalities across Europe. A significant element of these fiscal datasets are code lists, which serve not only for the coding and the simplicity of representation of budget concepts, but can also act as linking entities between budget datasets of different countries, bodies, years and structure, in order to facilitate their comparison. To this end, we made an extensive survey of code lists that refer or are related to fiscal or other concepts that are included in budget datasets, recording totally 239 international and national classifications. We selected some of these classifications to represent them semantically with SKOS and then we examined their linking possibilities. While manual and automated methods are insufficient for linking large code lists, tools using semi-automated methods, like Alignment seem to be more suitable for this specific task.