Extracting triples from Vietnamese text to create knowledge graph

Huong Duong To, P. Do
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引用次数: 2

Abstract

Knowledge graph (KG) plays an increasingly important role in the current technology era. It is very useful in many fields such as searching for information, supporting question answering systems and in other AI applications, etc. Besides the private Knowledge Graphs like Google's "Knowledge graph", we also have Open Knowledge graphs as DBpedia, YAGO, ... But generally, these Open Knowledge graphs contain very little data in Vietnamese. Due to this practice, our team proposed a way to create Vietnamese Knowledge graph by automatically scratching the Vietnamese text on the website as input, then using Named-entity recognition (NER) to recognize entities as nouns and combined with POS tag identifies words as verbs to extract triple in the simple sentences of the paragraph. The triple was then loaded into Neo4j to visualize the Knowledge graph.
从越南文文本中提取三元组,创建知识图谱
知识图谱在当今科技时代发挥着越来越重要的作用。它在许多领域非常有用,如搜索信息,支持问答系统和其他人工智能应用等。除了私有的知识图谱,如谷歌的“知识图谱”,我们也有开放的知识图谱,如DBpedia, YAGO,…但总的来说,这些开放知识图表包含的越南语数据很少。针对这一实践,我们的团队提出了一种通过自动抓取网站上的越南语文本作为输入,然后使用命名实体识别(NER)将实体识别为名词,并结合POS标签将单词识别为动词来提取段落简单句中的三联体来创建越南语知识图谱的方法。然后将这个三元组加载到Neo4j中以可视化知识图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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