@inbook{937663b6da474b85aaf3465b6dba6abb,
title = "Identifying and propagating contextually appropriate deep-topics amongst collaborating web-users",
abstract = "This paper describes a method for discovering URLs with contextually relevant deep-topics, and then propagating such information to collaborating users lacking such information. When a user is knowledgeable about a subject, their reasons for frequently browsing a URL extend beyond the fact that it is merely related to said subject. This paper's method includes an algorithm for discovering the surface-topic of a URL, and the underlying deep-topic that a user is truly interested in with respect to a given URL. The deep-topic extraction process works by using URLs linked together through a user's behavioral browsing patterns in order to discover the surface or group-topic of surrounding URLs, and then subtracting those topics to discover hidden deeper topics. This paper describes the three parts of the method: Information Extraction, Propagation, and Verification & Integration, which together form a method with high levels of parallelism due to its distributed and independent nature. This paper also discusses concrete usage-scenarios for the included method, and data structures which would support the implementation of this paper's method.",
keywords = "collaboration, data mining, knowledge discovery, knowledge sharing, web behavior",
author = "Jeremy Hall and Yasushi Kiyoki",
year = "2014",
month = mar,
day = "3",
doi = "10.3233/978-1-61499-361-2-146",
language = "English",
isbn = "9781614993605",
series = "Frontiers in Artificial Intelligence and Applications",
pages = "146--157",
editor = "Takehiro Tokuda and Yasushi Kiyoki and Hannu Jaakkola and Naofumi Yoshida",
booktitle = "Information Modelling and Knowledge Bases XXV",
}