Combining Machine Learning and Semantic Web: A Systematic Mapping Study

doi:10.1145/3586163

Combining Machine Learning and Semantic Web: A Systematic Mapping Study

2023-07-17

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DOI：10.1145/3586163

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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.

Keywords:

Semantic Web

Machine Learning

Artificial Intelligence

knowledge graph

Knowledge Representation and Reasoning

neuro-symbolic integration

Systematic Mapping Study

Combining Machine Learning and Semantic Web: A Systematic Mapping Study

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