关联数据的数据挖掘:过去、现在和未来

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI:10.1109/ICCMC.2018.8487861

Rohit Beniwal, Vikas Gupta, Manish Rawat, Rishabh Aggarwal

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引用次数: 6

摘要

关联数据已经成为表示结构化数据的一种流行方法。主要目标之一是将今天的文档网络转换为数据网络，其中的数据既可由机器读取，又可由机器处理。本文主要研究用于挖掘原始数据的数据挖掘技术。然而，这些技术很麻烦，可以使用关联数据进行优化。因此，我们讨论了关联数据的数据挖掘技术，这些技术可能在未来从非结构化或半结构化数据中提取有意义的信息方面发挥关键作用。

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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Data Mining with Linked Data: Past, Present, and Future

Linked Data has emerged as a popular method for representing structured data. One of the prime aims is to convert today’s web of documents into a web of data where the data is machine-readable as well as processable. This research paper focuses on the data mining techniques used for mining the raw data. However, these techniques are cumbersome and can be optimized using Linked Data. Hence, we discuss the data mining techniques with Linked Data that may play a pivotal role in future in extracting meaningful information from unstructured or semi-structured data.

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来源期刊

2018 Second International Conference on Computing Methodologies and Communication (ICCMC)

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