[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123585-en":3,"doc-seo-123585-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123585,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG - Paper abstract","Growing interest in neuro-symbolic AI in the Semantic Web community motivates systems that combine Semantic Web resources with Machine Learning components (SWeMLS). Progress is constrained by missing standardized system descriptions and an annotated corpus. A large-scale systematic mapping of 470 SWeMLS papers is used to formalize one resource containing the SWeMLS ontology, a pattern library for 45 recurring workflows, and the SWEMLS-KG knowledge graph with machine-actionable metadata, supporting both field assessment and adoption of machine-processable documentation.","arXiv :2303 . 15113v1 [ cs .AI] 27 Mar 2023  \nDescribing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG  \nFajar J. Ekaputra 1 ;2 , Majlinda Llugiqi 1 , Marta Sabou 1 , Andreas Ekelhart3 ;4 , Heiko Paulheim5 , Anna Breit6 , Artem Revenko6 , Laura Waltersdorfer2 ,  \nKheir Eddine Farfar7 , and Sören Auer7 ;8  \n1 WU (Vienna University of Economics and Business) [first.last@wu.ac.at](first.last@wu.ac.at)  \n2 TU Wien [first.last@tuwien.ac.at](first.last@tuwien.ac.at)  \n3 University of Vienna [first.last@univie.ac.at](first.last@univie.ac.at)  \n4 SBA Research [first.last@sba-research.org](first.last@sba-research.org)  \n5 University [of Mannheim](of Mannheim first.last@uni-mannheim.de)[ first.last@uni-mannheim.de](of Mannheim first.last@uni-mannheim.de)  \n6 Semantic Web Company [first.last@semantic-web.com](first.last@semantic-web.com)  \n7 TIB Leibniz Information Centre for Science and Society [first.last@tib.eu](first.last@tib.eu)  \n8 L3S Research Center, Leibniz University of Hannover [auer@l3s.de](auer@l3s.de)  \nAbstract. The overall AI trend of creating neuro-symbolic systems is reflected in the Semantic Web community with an increased interest in the development of systems that rely on both Semantic Web resources and Machine Learning components (SWeMLS, for short) . However, understanding trends and best practices in this rapidly growing field is hampered by a lack of standardized descriptions of these systems and an annotated corpus of such systems. To address these gaps, we leverage the results of a large-scale systematic mapping study collecting information about 470 SWeMLS papers and formalize these into one resource containing: (i) the SWeMLS ontology, (ii) the SWeMLS pattern library containing machine-actionable descriptions of 45 frequently occurring SWeMLS workflows, and (iii) SWEMLS-KG, a knowledge graph including machine-actionable metadata of the papers in terms of the SWeMLS ontology. This resource provides the first framework for semantically describing and organizing SWeMLS thus making a key impact in  \n(1) understanding the status quo of the field based on the published paper corpus and (2) enticing the uptake of machine-processable system documentation in the SWeMLS area.  \nKeywords: Neuro-symbolic System, Semantic Web, Machine Learning, Knowledge Graphs  \nResource type: Knowledge Graph  \nLicense: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nDOI: [https://doi.org/10.5281/zenodo.7445917](https://doi.org/10.5281/zenodo.7445917)  \nURL: [https://w3id.org/semsys/sites/swemls-kg/](https://w3id.org/semsys/sites/swemls-kg/)  \n1 Introduction  \nThe field of Artificial Intelligence (AI) is currently witnessing a great interest in (more closely) integrating and bridging between symbolic and sub-symbolic  \n2 F.J. Ekaputra et al.  \nFig. 1: Schematic representation of a SWeMLS workflow for art classification [13] .  \n(AI) [7] techniques. This substantial trend led to the establishment of the new sub-research field of neuro-symbolic systems 9 [6,12], which focuses on the theoretical and practical aspects of creating such complex systems. Against this backdrop, it is not surprising that this AI trend is also reflected in the Semantic Web (SW) research community which has popularized AI-based knowledge representation techniques and resources in the last two decades [17] . There is increased interest in neuro-symbolic integration in the context of the Semantic Web [18], such as the development of systems that rely on both Semantic Web resources and Machine Learning components. We coined the term Semantic Weband Machine Learning System (SWeMLS) to refer to such systems [8] .  \nFor example, in [13] authors propose a system for automatic art analysis that can be classified as an SWeMLS. To that end, they augment a deep learning based system that classifies artistic images purely based on visual features with contextual art information in a form of a knowledge graph about p","cbCaidegsawcElw7","https://ap.wps.com/l/cbCaidegsawcElw7","pdf",832668,1,18,"English","en",105,"# Introduction\n## Motivation and challenges\n## Example SWeMLS workflow and impact","[{\"question\":\"What does SWeMLS refer to in this work?\",\"answer\":\"SWeMLS denotes Semantic Web and Machine Learning Systems that combine Semantic Web resources with Machine Learning components.\"},{\"question\":\"What gaps does the study address?\",\"answer\":\"It addresses the lack of standardized descriptions for SWeMLS and the lack of an annotated corpus representing such systems for trend analysis.\"},{\"question\":\"What are the main components of the proposed resource?\",\"answer\":\"The resource includes the SWeMLS ontology, a pattern library with machine-actionable descriptions of 45 workflows, and SWEMLS-KG, a knowledge graph providing machine-actionable metadata for the collected papers.\"}]","Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG - 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