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Combining Machine Learning and Semantic Web: A Systematic Mapping Study.

Authors :
BREIT, ANNA
WALTERSDORFER, LAURA
EKAPUTRA, FAJAR J.
SABOU, MARTA
EKELHART, ANDREAS
IANA, ANDREEA
PAULHEIM, HEIKO
PORTISCH, JAN
REVENKO, ARTEM
TEIJE, ANNETTE TEN
VAN HARMELEN, FRANK
Source :
ACM Computing Surveys. 2023 Suppl14s, Vol. 55, p1-41. 41p.
Publication Year :
2023

Abstract

In 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 SemanticWeb community--SemanticWebMachine 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03600300
Volume :
55
Database :
Academic Search Index
Journal :
ACM Computing Surveys
Publication Type :
Academic Journal
Accession number :
169992789
Full Text :
https://doi.org/10.1145/3586163