Mapping and creating interoperable data depends on a method of providing semantic and syntactic interoperability across diverse systems, data sources and datasets. To this end, PolicyCLOUD’s Interoperability Component aims to enhance interoperability into PolicyCLOUD project based on data-driven design by the utilization of linked data technologies, such as JSON-LD, and standards-based ontologies and vocabularies, coupled with the use of powerful tasks from the domain of Natural Language Processing (NLP), in order to improve both semantic and syntactic interoperability of data and datasets. Through these coupled technologies and methods, the Interoperability Component seeks to provide a state-of-the-art approach to achieve interoperability in data-driven policy making domain. SemAI entitles this proposed hybrid approach and is a combination of commonly used semantic techniques coupled with the utilization of NLP tasks and methods. The main goal of this hybrid mechanism is to design and implement a holistic semantic layer that will address data heterogeneity. To this end, this hybrid approach aims to enhance both semantic and syntactic interoperability of data based on the aggregation, correlation, and transformation of incoming data according to the defined schemas and models. The knowledge that will be derived from these processes, shaped in a machine-readable way, can be used latter from other tools for providing Big Data analytics, i.e. Sentiment Analysis etc.
The hybrid SemAI mechanism incorporates and integrates three different subcomponents in the scope of achieving and providing enhanced data interoperability. To this end, the NMT subcomponent, the Semantic & Syntactic Analysis with NLP subcomponent, and the Ontology Mapping subcomponent will be implemented and integrated.
SemAI introduces a multi-layer and hybrid mechanism for Semantic Interoperability across diverse policy related datasets, which will facilitate Semantic Interoperability across related datasets both within a single domain and across different policy making domains. This requirement relates to local-regional public administrations and business domain, but it also goes beyond the national borders as it also seeks to invoke a language-independent hybrid mechanism. Moreover, IT systems and applications interoperability, sharing and reuse, and interlinking of information and policies, within and between domains are essential factors for the delivery of high quality, innova-tive, and seamless policies. Under this framework, SemAI and its required steps and subcomponents facilitate its adoption at data-driven policy making domain. Achieving high levels of Semantic Interoperability in the data can help organizations and businesses to turn their data into valuable information, add extra value and knowledge to them and finally achieve enhanced policy making through the combination and correlation of several data, datasets, and policies.