Academic research related to the Open Directory Project. The listed research papers may quote ODP as an example for a large web directory, they may describe studies based on ODP data, tests for which ODP data were used or they may focus on ODP itself.
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By A. Maguitman, F. Menczer, F. Erdinc, H. Roinestad and A. Vespignani, Indiana University. In: World Wide Web, Volume 9, Issue 4, 2006. An information-theoretic measure of semantic similarity between pages exploiting both hierarchical and non-hierarchical ODP structure improves on taxonomy-based approaches.
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By Inderjit S. Dhillon, Subramanyam Mallela and Rahul Kumar, University of Texas, Austin, USA. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002. The authors propose a new information-theoretic divisive algorithm for word clustering applied to text classification. Experimental results are based on a 20 Newsgroups data set and a 3-level hierarchy of HTML documents collected from ODP´s Science toplevel.
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By Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen and Wei-Ying Ma. In: Proceedings of the 28th Annual International ACM SIGIR Conference, August 2005. The authors propose a ranking scheme named Affinity Ranking (AR). Yahoo, ODP and newsgroup data are used for the experiments.
By Adam L. Berger, Carnegie Mellon University, and Vibhu O. Mittal, Just Research, Pittsburgh, USA. In: Proceedings of the 23rd Annual International ACM SIGIR Conference, 2000. Probabilistic models are used to select and order words into a gist. The paper describes a technique for learning these models automatically from a collection of human-summarized web pages, the authors used ODP data for this purpose.
By Jian-Tao Sun, Dou Shen, HuaJun Zeng, Qiang Yang, Yuchang Lu and Zheng Chen. In: Proceedings of the 28th Annual International ACM SIGIR Conference, August 2005. The authors propose two adapted summarization methods that take advantage of the relationships discovered from clickthrough data. For those pages not covered by clickthrough data, they put forward a thematic lexicon approach to generate implicit knowledge. The methods are evaluated on a relatively small dataset consisting of manually annotated pages as well as a large dataset crawled from ODP.
By A. Maguitman, F. Menczer, F. Erdinc, H. Roinestad and A. Vespignani, Indiana University. In: World Wide Web, Volume 9, Issue 4, 2006. An information-theoretic measure of semantic similarity between pages exploiting both hierarchical and non-hierarchical ODP structure improves on taxonomy-based approaches.
[PDF]
By Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen and Wei-Ying Ma. In: Proceedings of the 28th Annual International ACM SIGIR Conference, August 2005. The authors propose a ranking scheme named Affinity Ranking (AR). Yahoo, ODP and newsgroup data are used for the experiments.
By Jian-Tao Sun, Dou Shen, HuaJun Zeng, Qiang Yang, Yuchang Lu and Zheng Chen. In: Proceedings of the 28th Annual International ACM SIGIR Conference, August 2005. The authors propose two adapted summarization methods that take advantage of the relationships discovered from clickthrough data. For those pages not covered by clickthrough data, they put forward a thematic lexicon approach to generate implicit knowledge. The methods are evaluated on a relatively small dataset consisting of manually annotated pages as well as a large dataset crawled from ODP.
By Inderjit S. Dhillon, Subramanyam Mallela and Rahul Kumar, University of Texas, Austin, USA. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002. The authors propose a new information-theoretic divisive algorithm for word clustering applied to text classification. Experimental results are based on a 20 Newsgroups data set and a 3-level hierarchy of HTML documents collected from ODP´s Science toplevel.
[PDF]
By Adam L. Berger, Carnegie Mellon University, and Vibhu O. Mittal, Just Research, Pittsburgh, USA. In: Proceedings of the 23rd Annual International ACM SIGIR Conference, 2000. Probabilistic models are used to select and order words into a gist. The paper describes a technique for learning these models automatically from a collection of human-summarized web pages, the authors used ODP data for this purpose.
