A framework of Ontology Guided Data Linkage for evidence based knowledge extraction and information sharing
There has been a surge of interests in developing probabilistic techniques for linking semantic equivalent datasets. The key objective is to transform the structure of the induced data into a concise synopsis. Current techniques primarily focus on performing pair-wise attribute matching and pay little attention in discovering direct and weighted correlations among ontological clusters through multi-faceted classification. In this research, we introduce a novel Ontology Guided Data Linkage (OGDL) framework for self-organising and discovering schema structures through constructing a hierarchical cluster mapping trees. Furthermore, we extend our OGDL framework by introducing a novel faceted search engine for semantic interoperability of data and subsequent decision support analysis, and use it to map fast cluster browsing, user friendly querying and semantic reasoning learning needs.