Exploring wikipedia and DMoz as knowledge bases for engineering a user interests hierarchy for social network applications
|Exploring wikipedia and DMoz as knowledge bases for engineering a user interests hierarchy for social network applications|
|Author(s)||Haridas M., Caragea D.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Agglomerative clustering algorithm, Data mining problems, Global ontology, Knowledge basis, Latent Semantic Analysis, Mozilla, Similar Interests, Social Networks, User interests, Wikipedia, Clustering algorithms, Internet, Ontology, Semantics, Data mining)|
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Exploring wikipedia and DMoz as knowledge bases for engineering a user interests hierarchy for social network applications is a 2009 conference paper written in English by Haridas M., Caragea D. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
The outgrowth of social networks in the recent years has resulted in opportunities for interesting data mining problems, such as interest or friendship recommendations. A global ontology over the interests specified by the users of a social network is essential for accurate recommendations. We propose, evaluate and compare three approaches to engineering a hierarchical ontology over user interests. The proposed approaches make use of two popular knowledge bases, Wikipedia and Directory Mozilla, to extract interest definitions and/or relationships between interests. More precisely, the first approach uses Wikipedia to find interest definitions, the latent semantic analysis technique to measure the similarity between interests based on their definitions, and an agglomerative clustering algorithm to group similar interests into higher level concepts. The second approach uses the Wikipedia Category Graph to extract relationships between interests, while the third approach uses Directory Mozilla to extract relationships between interests. Our results show that the third approach, although the simplest, is the most effective for building a hierarchy over user interests.
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