Identifying and propagating contextually appropriate deep-topics amongst collaborating web-users

Jeremy Hall, Yasushi Kiyoki

研究成果: Chapter

1 被引用数 (Scopus)


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.

ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXV
編集者Takehiro Tokuda, Yasushi Kiyoki, Hannu Jaakkola, Naofumi Yoshida
出版ステータスPublished - 2014 3月 3


名前Frontiers in Artificial Intelligence and Applications

ASJC Scopus subject areas

  • 人工知能


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