Combining Machine Learning and Semantic Web: A Systematic Mapping Study
The systematic mapping study on combining Machine Learning and Semantic Web has been accepted by ACM Computing Surveys. This was an international joint effort of Semantic Web Company (SWC), Vienna University of Technology, WU (Vienna University of Economics and Business), University of Vienna, SBA Research, University of Mannheim, and Vrije Universiteit Amsterdam (VU Amsterdam).
Titel
Combining Machine Learning and Semantic Web: A Systematic Mapping Study
Authors
Anna Breit, Laura Waltersdorfer, Fajar J. Ekaputra, Marta Sabou, Andreas Ekelhart, Andreea Iana, Heiko Paulheim, Jan Portisch, Artem Revenko, Annete ten Teije, Frank van Harmelen
Report
ACM Computing Surveys
Abstract
In line with the general trend in artiicial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community ś Semantic Web Machine Learning (SWeML for short). Due to its rapid growth and impact on several communities in the last 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 ill this gap, we performed a systematic study and analyzed nearly 500 papers published in the last decade in this area, where we focused on evaluating architectural, and application-speciic features. Our analysis identiied 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 paper is a classiication system for SWeML Systems which we publish as ontology.
Link
Combining Machine Learning and Semantic Web: A Systematic Mapping Study | ACM Computing Surveys