基于知识图谱嵌入的社会政治关系学习分析
Relational Learning Analysis of Social Politics using Knowledge Graph Embedding
摘要：
知识图谱（KGs）近年来受到学术界和工业界的广泛关注。事实上，结合图形技术和大量的各种图形数据集已经导致研究社区构建复杂的图形分析工具。因此，幼儿园的应用已经扩展到解决大量的现实生活中的问题，在不同的领域。尽管目前大量的通用知识库，有一个至关重要的需要，以构建域特定的知识库。此外，质量和信誉应在建设和扩大幼儿园的过程中吸收，特别是那些从混合质量资源（如社交媒体数据）传播的幼儿园。本文提出了一种新的基于可信域的KG嵌入框架。该框架涉及到捕获融合的数据从异构资源到一个正式的KG表示所描绘的领域本体。该方法利用各种知识库来丰富文本内容的语义，从而促进信息的互操作性。拟议的框架还包含一个可信度模块，以确保数据质量和可信度。然后，使用几种嵌入技术将构建的KG嵌入到低维语义连续空间中。构造KG及其嵌入的效用被证明和证实的链接预测，聚类和可视化任务。
英文摘要：
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.
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