  Knowledge Graphs (KGs) have gained considerable attention recently from both
academia and industry. In fact, incorporating graph technology and the copious
of various graph datasets have led the research community to build
sophisticated graph analytics tools. Therefore, the application of KGs has
extended to tackle a plethora of real-life problems in dissimilar domains.
Despite the abundance of the currently proliferated generic KGs, there is a
vital need to construct domain-specific KGs. Further, quality and credibility
should be assimilated in the process of constructing and augmenting KGs,
particularly those propagated from mixed-quality resources such as social media
data. This paper presents a novel credibility domain-based KG Embedding
framework. This framework involves capturing a fusion of data obtained from
heterogeneous resources into a formal KG representation depicted by a domain
ontology. The proposed approach makes use of various knowledge-based
repositories to enrich the semantics of the textual contents, thereby
facilitating the interoperability of information. The proposed framework also
embodies a credibility module to ensure data quality and trustworthiness. The
constructed KG is then embedded in a low-dimension semantically-continuous
space using several embedding techniques. The utility of the constructed KG and
its embeddings is demonstrated and substantiated on link prediction,
clustering, and visualisation tasks.
