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EKAPUTRA, Vienna University of Economics and Business (WU) and TU Wien MARTA SABOU, Vienna University of Economics and Business (WU)  \nANDREAS EKELHART, University of Vienna and SBA Research  \nANDREEA IANA, HEIKO PAULHEIM, and JAN PORTISCH, University of Mannheim ARTEM REVENKO, Semantic Web Company  \nANNETTE TEN TEIJE and FRANK VAN HARMELEN, Vrije Universiteit (VU) Amsterdam  \nIn line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the Semantic Web community—Semantic Web Machine Learning (SWeML) . Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology.  \nThis work was supported in part by the research project OBARIS, which received funding from the Austrian Research Promotion Agency (FFG) under grant 877389 . SBA Research (SBA-K1) is a COMET Centre within the framework of COMET—Competence Centers for Excellent Technologies Programme and funded by BMK, BMDW, and the federal state of Vienna; COMET is managed by FFG. Moreover, this work was supported by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development. M. Sabou was funded through the FWF HOnEst project (V 754-N) .  \nAuthors’ addresses: A. Breit and A. Revenko, Semantic Web Company, Neubaugasse 1/8, 1070 Vienna, Austria; emails:{anna.breit, [artem.revenko}@semantic-web.com](artem.revenko}@semantic-web.com); L. Waltersdorfer, TU Wien, Favoritenstrasse 9-11/194-01, 1040 Vienna, Austria; email: [laura.waltersdorfer@tuwien.ac.at](laura.waltersdorfer@tuwien.ac.at); F. J. Ekaputra, Vienna University of Economics and Business (WU), Institute for Data Process, and Knowledge Management (DPKM) . Building D2/C, 2nd floor, Welthandelsplatz 1, 1020 Vienna, Austria; email: [fajar.ekaputra@wu.ac.at](fajar.ekaputra@wu.ac.at); M. Sabou, University of Vienna, Kolingasse 14-16, 5. OG, 1090 Vienna, Austria; [email: marta.sabou@wu.ac.at](email: marta.sabou@wu.ac.at); A. Ekelhart, University of Vienna and SBA Research, Floragasse 7, 1040 Vienna, Austria; email: [andreas.ekelhart@univie.ac.at](andreas.ekelhart@univie.ac.at); A. Iana, H. Paulheim, and J. Portisch, University of Mannheim, L 1, 1, 68131 Mannheim, Germany; emails: [andreea.iana@uni-mannheim.de](andreea.iana@uni-mannheim.de), {heiko, [jan}@informatik.uni-mannheim.de](jan}@informatik.uni-mannheim.de); A. ten Teije  \nand F. van Harmelen, Vrije Universiteit (VU) Amsterdam, Dept. of Computer Science, De Boelelaan 1111, 1085HV Amsterdam, Netherlands; emails: {annette.ten.teije, [frank.van.harmelen}@vu.nl](frank.van.harmelen}@vu.nl).  \nAuthor’s current addresses: J. Portisch, SAP SE, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany; email: [jan.portisch@sap.com](jan.portisch@sap.com).  \nThis work is licensed under a Creative Commons Attribution Inte","cbCaisPyUem1xhEp","https://ap.wps.com/l/cbCaisPyUem1xhEp","pdf",5358439,41,"English","# Introduction\n## Intelligent systems and research motivation\n## SWeML scope and research gap\n# Systematic mapping study approach\n## Paper corpus and inclusion focus\n## Architectural and application feature evaluation\n# Findings and implications\n## Growth trends and domain impact\n## Drivers: deep learning and knowledge graphs\n# Classification contribution\n## Ontology-based taxonomy","[{\"question\":\"What is Semantic Web Machine Learning (SWeML)?\",\"answer\":\"SWeML combines machine learning components with techniques developed by the Semantic Web community.\"},{\"question\":\"What research gap does the systematic mapping study address?\",\"answer\":\"Surveys that use principled and unbiased approaches for understanding SWeML systems are missing.\"},{\"question\":\"How many papers were analyzed and what did the study evaluate?\",\"answer\":\"Nearly 500 papers from the past decade were analyzed, focusing on architectural and application-specific features.\"}]","Combining Machine Learning and Semantic Web - A Systematic Mapping Study | PDF",103]