Lorenz Bühmann, Jens Lehmann, Patrick Westphal, Simon Bin
{"title":"DL-Learner Structured Machine Learning on Semantic Web Data","authors":"Lorenz Bühmann, Jens Lehmann, Patrick Westphal, Simon Bin","doi":"10.1145/3184558.3186235","DOIUrl":null,"url":null,"abstract":"The following paper is an extended summary of the journal paper \"DL-Learner A framework for inductive learning on the Semantic Web\". In this system paper, we describe the DL-Learner framework. It is beneficial in various data and schema analytic tasks with applications in different standard machine learning scenarios, e.g. life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion Proceedings of the The Web Conference 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3184558.3186235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27
Abstract
The following paper is an extended summary of the journal paper "DL-Learner A framework for inductive learning on the Semantic Web". In this system paper, we describe the DL-Learner framework. It is beneficial in various data and schema analytic tasks with applications in different standard machine learning scenarios, e.g. life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework.