KeyGraph-based BBS for Online Chance Discovery

Seo, Yuta; Iwase, Yoshihiro; Takama, Yasufumi

doi:10.1109/icsmc.2006.384973

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…This technology is also adopted in relevant research and studies, including identifying the potential domestic consumption needs to offer suggestions on improving the quality of meals (Ohsawa & Fukuda, 2002); analyzing seismic records to predict risks of earthquakes (Ohsawa, 2002); getting information from recipe-sharing websites, interpreting user preferences and seeking new business opportunities (Emoto, 2015;Shin & Seo, 2017); formulating more accurate and personalized recommendation system based on chance discovery theory (Kong et al, 2018); conducting chance discovery process according to the content of online discussion etc. (Seo, Iwase, & Takama, 2006).…”

Section: Related Work Chance Discoverymentioning

confidence: 99%

Investigating Effects of Education for Sustainable Development in Junior High School in Central Java

2019

Int. j. bus. econ. aff.

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Patents are a valuable source of information in technical innovation, market direction, and competitive intelligence. Discovering emerging chances from the patent data helps to improve planning for business decisions in further investments accordingly. This paper aims to explore and identify commercial opportunities about the financial technologies that are used in investment management in China. We focus on a method using KeyGraph, a text data mining tool for extraction and visualization of preference from information to assist the process of chance discovery, to reveal the structure of the patents which have CPC G06Q40/06, the classification label that covers technologies related to financial investment. Consequently, through conducting both cluster and association analysis on the patent data, Y10S707/99935(Query augmenting and refining) has been found as a chance that connect advance investment analysis system and the basic information management system, and the result shows that the presented method is promising to generate new opportunities from the chance nodes obtained from the KeyGraph.

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