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Combining Machine Learning and Semantic Web: A Systematic Mapping Study
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AI
DOI：10.1145/3586163.png)
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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 Semantic Web community-Semantic Web Machine Learning (SWeML). Due to its rapid growth and impact on several communities in the past 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.
Keyword:
Semantic Web
Machine Learning
Artificial Intelligence
knowledge graph
Knowledge Representation and Reasoning
neuro-symbolic integration
Systematic Mapping Study
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28
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2.5K
被引数:
3.5W
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