TY - GEN
T1 - Query by document
AU - Yang, Yin
AU - Bansal, Nilesh
AU - Dakka, Wisam
AU - Ipeirotis, Panagiotis
AU - Koudas, Nick
AU - Papadias, Dimitris
PY - 2009
Y1 - 2009
N2 - We are experiencing an unprecedented increase of content contributed by users in forums such as blogs, social networking sites and micro-blogging services. Such abundance of content complements content on web sites and traditional media forums such as news papers, news and financial streams, and so on. Given such plethora of information there is a pressing need to cross reference information across textual services. For example, commonly we read a news item and we wonder if there are any blogs reporting related content or vice versa. In this paper, we present techniques to automate the process of cross referencing online information content. We introduce methodologies to extract phrases from a given "query document" to be used as queries to search interfaces with the goal to retrieve content related to the query document. In particular, we consider two techniques to extract and score key phrases. We also consider techniques to complement extracted phrases with information present in external sources such as Wikipedia and introduce an algorithm called RelevanceRank for this purpose. We discuss both these techniques in detail and provide an experimental study utilizing a large number of human judges from Amazons's Mechanical Turk service. Detailed experiments demonstrate the effectiveness and efficiency of the proposed techniques for the task of automating retrieval of documents related to a query document.
AB - We are experiencing an unprecedented increase of content contributed by users in forums such as blogs, social networking sites and micro-blogging services. Such abundance of content complements content on web sites and traditional media forums such as news papers, news and financial streams, and so on. Given such plethora of information there is a pressing need to cross reference information across textual services. For example, commonly we read a news item and we wonder if there are any blogs reporting related content or vice versa. In this paper, we present techniques to automate the process of cross referencing online information content. We introduce methodologies to extract phrases from a given "query document" to be used as queries to search interfaces with the goal to retrieve content related to the query document. In particular, we consider two techniques to extract and score key phrases. We also consider techniques to complement extracted phrases with information present in external sources such as Wikipedia and introduce an algorithm called RelevanceRank for this purpose. We discuss both these techniques in detail and provide an experimental study utilizing a large number of human judges from Amazons's Mechanical Turk service. Detailed experiments demonstrate the effectiveness and efficiency of the proposed techniques for the task of automating retrieval of documents related to a query document.
KW - Blog
KW - Similarity matching
KW - Web 2.0
KW - Wikipedia
UR - http://www.scopus.com/inward/record.url?scp=70349111073&partnerID=8YFLogxK
U2 - 10.1145/1498759.1498806
DO - 10.1145/1498759.1498806
M3 - Conference contribution
AN - SCOPUS:70349111073
SN - 9781605583907
T3 - Proceedings of the 2nd ACM International Conference on Web Search and Data Mining, WSDM'09
SP - 34
EP - 43
BT - Proceedings of the 2nd ACM International Conference on Web Search and Data Mining, WSDM'09
T2 - 2nd ACM International Conference on Web Search and Data Mining, WSDM'09
Y2 - 9 February 2009 through 12 February 2009
ER -