International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 08 | Aug 2024
p-ISSN: 2395-0072
www.irjet.net
Exploring the Interplay: Semantic Web Enhancements for Machine Learning Advancement Amritha Sreedhar C, Aparna Mohan PG Student, Dept. of Computer Science, Malabar College of Engineering and Technology, Kerala, India Asst. Professor, Dept. of Computer Science, Malabar College of Engineering and Technology, Kerala, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper presents a thorough examination of
light on the transformative potential of this interdisciplinary field.[10]
the evolving field of Semantic Web Enhancements for Machine Learning (SWeML). Through a systematic study we identify trends and characteristics, emphasizing architectural and application-specific features. Our analysis underscores the rapid growth of SWeML systems, driven by the fusion of deep learning and knowledge graph technologies. [7] Additionally, we introduce a novel classification system for SWeML systems, enhancing understanding in this domain.
Over the past two decades, the landscape of artificial intelligence research has witnessed a paradigm shift towards the integration of learning and symbolic components. [2] This shift has given rise to SWeML, a sub-area dedicated to marrying the principles of Semantic Web with the power of ML techniques. The resultant synergy has fueled a rapid proliferation of SWeML systems, each poised to address complex challenges across a spectrum of application domains.
We advocate for Semantic Web architectures compatible with existing languages such as RDF and OWL, emphasizing their role in fostering development while accommodating closedworld assumptions and negation as failure.
At the heart of SWeML lies a quest for understanding and harnessing the rich semantics inherent in data and knowledge representations. By leveraging Semantic Web frameworks such as RDF and OWL, researchers aim to imbue ML models with a deeper understanding of context and meaning. This, in turn, enables more intelligent decisionmaking and reasoning capabilities, laying the foundation for next-generation intelligent systems.[4]
Semantic Web Services, leveraging machine learning techniques, have seen notable progress in web service discovery frameworks. We provide an extensive review of these approaches, highlighting advancements in semantic discovery through clustering, classification, and association rules. Furthermore, we explore the synergy between Machine Learning and Semantic Web technologies in enhancing explainability, crucial in high-stakes domains. By reviewing current methodologies, we identify key domains driving research and propose future directions for leveraging Semantic Web technologies to enhance explainability in Machine Learning systems.[8]
In this introductory exploration, we embark on a multidimensional journey through the realm of SWeML. We begin by surveying the landscape, analyzing recent trends, and identifying catalysts driving the rapid growth of SWeML systems. We then delve into the architectural nuances of Semantic Web frameworks, highlighting their compatibility with existing languages and their pivotal role in shaping the future of the Semantic Web.
Keywords: semantic web, SWeML, Machine Learning, Deep Learning, Clustering
Furthermore, we unravel the intricate web of Semantic Web Services, where machine learning techniques are leveraged to facilitate richer, more declarative descriptions of distributed computation elements. Through a comprehensive review of existing frameworks, we illuminate the transformative potential of SWeML in the realm of web service discovery and semantic interoperability.
1. INTRODUCTION The symbiotic relationship between Semantic Web technologies and Machine Learning (ML) has paved the way for groundbreaking advancements in various domains. This intersection, often referred to as Semantic Web Enhancements for Machine Learning (SWeML), represents a convergence of methodologies aimed at augmenting the capabilities of intelligent systems. SWeML System can improve the interpretability and comprehensibility of the ML sub-system through its incorporation of a semantic symbolic subsystem. Such explainable SWeML Systems have been used in different domains to solve a wide variety of tasks. In this introduction, we embark on a journey to explore the dynamic interplay between Semantic Web and ML, shedding
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Additionally, we explore the pressing need for explainability in ML systems, particularly in high-stakes domains such as healthcare and transportation. Here, Semantic Web technologies emerge as a beacon of hope, offering semantically interpretable tools for reasoning on knowledge bases and providing transparent insights into model decisions.
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