Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Yudistira Bagus Pratama; Haiyudi Haiyudi

doi:10.25299/itjrd.2023.13459

Authors

Yudistira Bagus Pratama Universitas Muhammadiyah Bangka Belitung
Haiyudi Haiyudi Universitas Muhammadiyah Bangka Belitung

DOI:

https://doi.org/10.25299/itjrd.2023.13459

Keywords:

Deep Learning, Data Mining, Community Detection, Wikipedia, Knowledge Graph

Abstract

Analysis of social networks or online communities can be very difficult when working on large networks, as many measurements require expensive hardware. For example, identifying the community structure of a network is a very computationally expensive task. Embedded graph is a way to represent graphs with vectors, so that further analysis becomes easier. The purpose of this research is to analyze the knowledge graph from the wikipedia article data. This research aims to implement web scraping techniques on the wikipedia article search engine and display similar wikipedia pages and analyze them using a predetermined deep learning algorithm. Data collection in this research used scraping techniques to retrieve data from the unstructured wikipedia website and then processed it into structured data. The method used in this research is a standard cross-industry process for data mining by performing phases of data collection, data processing, proposed algorithms, testing and evaluation. The algorithm applied is deepwalk, kmeans, girvan newman. By doing this research, it is expected to provide knowledge about the deep learning approach for data representation of the wikipedia pages knowledge graph and can help users find similar wikipedia pages and enrich literacy on knowledge graph analysis.

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