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TODE: An Ontology-Based Model for the Dynamic Population of Web Directories
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Author(s): Sofia Stamou (Patras University, Greece), Alexandros Ntoulas (University of California Los Angeles (UCLA), USA)and Dimitris Christodoulakis (Patras University, Greece)
Copyright: 2008
Pages: 17
Source title:
Data Mining with Ontologies: Implementations, Findings, and Frameworks
Source Author(s)/Editor(s): Hector Oscar Nigro (Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina), Sandra Elizabeth Gonzalez Cisaro (Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina)and Daniel Hugo Xodo (Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina)
DOI: 10.4018/978-1-59904-618-1.ch001
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Abstract
In this paper we study how we can organize the continuously proliferating Web content into topical cate-gories, also known as Web directories. In this respect, we have implemented a system, named TODE that uses a Topical Ontology for Directories’ Editing. First, we describe the process for building our ontol-ogy of Web topics, which are treated in TODE as directories’ topics. Then, we present how TODE inter-acts with the ontology in order to categorize Web pages into the ontology’s topics and we experimentally study our system’s efficiency in grouping Web pages thematically. We evaluate TODE’s performance by comparing its resulting categorization for a number of pages to the categorization the same pages dis-play in Google Directory as well as to the categorizations delivered for the same set of pages and topics by a Bayesian classifier. Results indicate that our model has a noticeable potential in reducing the hu-man-effort overheads associated with populating Web directories. Furthermore, experimental results im-ply that the use of a rich topical ontology increases significantly classification accuracy for dynamic con-tents.
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