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

Abstract

One of the key elements in several application domains, such as policy making, addresses the scope of achieving and dealing with the very different formats, models and languages of data. The amount of data to be processed and analyzed in modern governments, organizations and businesses are staggering, thus Big Data analysis is the mean that helps organizations to harness their data and to identify new opportunities. Big Data is characterized by divergent data coming from various and heterogeneous sources and in different types, formats, and timeframes. Data interoperability addresses the ability of modern systems and mechanisms that create, exchange and consume data to have clear, shared expectations for the context, information and value of these divergent data. To this end, interoperability appears as the mean for accomplishing the interlinking of information, systems, applications and ways of working with the wealth of data. To address this challenge, in this paper a generalized and novel Enhanced Semantic Interoperability approach is proposed, the SemAI. This approach primarily focuses on the phases of the translation, the processing, the annotation, the mapping, as well as the transformation of the collected data, which have a major impact on the successful aggregation, analysis, and exploitation of data across the whole policy making lifecycle. The presented prototype and its required subcomponents associated with this approach provide an example of the proposed hybrid and holistic mechanism, verifying its possible extensive application and adoption in various policy making scenarios.

To read more from the AIAI2021 paper you can find it here: https://www.springerprofessional.de/en/semai-a-novel-approach-for-achieving-enhanced-semantic-interoper/19280440