融合本体与属性图的知识大图——以金融所有权网络为例

IF 0.6 4区 管理学 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
Xiaoya Tang, Wei Fu, Yan Liu
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引用次数: 1

摘要

在大数据环境下,知识规模快速增长,传统的知识组织和服务面临着语义不准确和不时效性的困境。从知识融合的角度出发,将传统本体的精确语义优势与属性图的大规模图处理能力和谓词属性表达能力相结合,提出了一种本体与属性图融合框架(OPGFF)。融合过程分为内容层融合和约束层融合。这种融合的结果,即知识表示模型称为知识大图。此外,本文将知识大图模型应用于中国金融领域的所有权网络,构建了金融所有权知识大图。在此基础上,设计并实现了金融所有权知识大图中发现矛盾数据和填补缺失数据的6种一致性推理算法,其中5种算法是完全领域不可知论的。用实际数据验证了算法的正确性和有效性。融合OPGFF框架和知识大图实现方法可为大数据知识组织与服务提供技术参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge Big Graph Fusing Ontology with Property Graph: A Case Study of Financial Ownership Network
The scale of know­ledge is growing rapidly in the big data environment, and traditional know­ledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a know­ledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the know­ledge representation model is called know­ledge big graph. In addition, this paper applies the know­ledge big graph model to the ownership network in the China’s financial field and builds a financial ownership know­ledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership know­ledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the know­ledge big graph could provide technical reference for big data know­ledge organization and services.
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来源期刊
Knowledge Organization
Knowledge Organization INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
1.40
自引率
28.60%
发文量
7
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