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2021 | OriginalPaper | Chapter

SemAI: A Novel Approach for Achieving Enhanced Semantic Interoperability in Public Policies

Authors : George Manias, Argyro Mavrogiorgou, Athanasios Kiourtis, Dimosthenis Kyriazis

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer International Publishing

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Abstract

This chapter introduces SemAI, a novel approach designed to enhance semantic interoperability in public policies. It addresses the challenges posed by the increasing volume and complexity of data, particularly unstructured data, and the need for meaningful data integration across diverse datasets and domains. By leveraging AI and NLP techniques such as Neural Machine Translation (NMT) and Named Entity Recognition (NER), and integrating them with Semantic Web technologies like RDF and JSON-LD, SemAI aims to create a robust, multi-layered mechanism for data interoperability. This approach not only facilitates the aggregation, correlation, and transformation of data but also supports the creation of machine-readable knowledge graphs. The proposed methodology is designed to be language-independent, making it particularly valuable in multilingual environments. The chapter outlines the architecture and components of SemAI, including NMT, Semantic & Syntactic Analysis with NLP, and Ontology Mapping, and discusses its potential application in the PolicyCLOUD project. The innovative combination of AI, NLP, and Semantic Web technologies presented in this chapter offers a promising solution for enhancing data-driven policy-making and improving the overall efficiency and effectiveness of public policy systems.

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Metadata
Title
SemAI: A Novel Approach for Achieving Enhanced Semantic Interoperability in Public Policies
Authors
George Manias
Argyro Mavrogiorgou
Athanasios Kiourtis
Dimosthenis Kyriazis
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-79150-6_54

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