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, which has extended the application of KGs 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. For example, the amount of the political discourses in social media is overwhelming yet can be hijacked and misused by spammers to spread misinformation and false news. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data related to politics domain and obtained from heterogeneous resources into a formal KG representation depicted by a politics 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 domain-based social credibility module to ensure data quality and trustworthiness. The utility of the proposed framework is verified by means of experiments conducted on two constructed KGs. The KGs are then embedded in low-dimensional semantically-continuous space using several embedding techniques. The effectiveness of embedding techniques and social credibility module is further demonstrated and substantiated on link prediction, clustering, and visualisation tasks.

知识图谱（KGs）最近受到了学术界和工业界的广泛关注。实际上，结合图技术和大量的各种图数据集已导致研究社区构建复杂的图分析工具，从而扩展了KG的应用范围，以解决不同领域中的大量现实问题。尽管目前泛滥的通用KG数量很多，但构建特定领域的KG仍是至关重要的。此外，在构建和增强KG的过程中，尤其是从混合质量资源（例如社交媒体数据）传播的KG时，质量和信誉应被吸收。例如，社交媒体中的政治话语数量巨大，但垃圾邮件发送者可能会劫持和滥用这些信息，以散布错误信息和虚假新闻。本文提出了一种基于信誉域的新型KG嵌入框架。该框架涉及捕获与政治领域相关的数据的融合，这些数据是从异构资源中获得的，并融合到由政治领域本体描述的正式KG表示中。所提出的方法利用各种基于知识的存储库来丰富文本内容的语义，从而促进信息的互操作性。提议的框架还体现了基于域的社会可信度模块，以确保数据质量和可信赖性。通过对两个构建的KG进行的实验验证了所提出框架的实用性。然后，使用几种嵌入技术将KG嵌入到低维语义连续的空间中。