TY - GEN
T1 - The Microsoft Academic Search dataset and KDD Cup 2013
AU - Roy, Senjuti Basu
AU - De Cock, Martine
AU - Mandava, Vani
AU - Savanna, Swapna
AU - Dalessandro, Brian
AU - Perlich, Claudia
AU - Cukierski, William
AU - Hamner, Ben
PY - 2013
Y1 - 2013
N2 - KDD Cup 2013 challenged participants to tackle the problem of author name ambiguity in a digital library of scientific publications. The competition consisted of two tracks, which were based on large-scale datasets from a snapshot of Microsoft Academic Search, taken in January 2013 and including 250K authors and 2.5M papers. Participants were asked to determine which papers in an author profile are truly written by a given author (track 1), as well as to identify duplicate author profiles (track 2). Track 1 and track 2 were launched respectively on April 18 and April 20, 2013, with a common final submission deadline on June 12, 2013. For track 1 a training dataset with correct labels was diclosed at the start of the competition. This track was the most popular one, attracting submissions of 561 different teams. Track 2, which was formulated as an unsupervised learning task, received submissions from 241 participants. This paper presents details about the problem definitions, the datasets, the evaluation metrics and the results.
AB - KDD Cup 2013 challenged participants to tackle the problem of author name ambiguity in a digital library of scientific publications. The competition consisted of two tracks, which were based on large-scale datasets from a snapshot of Microsoft Academic Search, taken in January 2013 and including 250K authors and 2.5M papers. Participants were asked to determine which papers in an author profile are truly written by a given author (track 1), as well as to identify duplicate author profiles (track 2). Track 1 and track 2 were launched respectively on April 18 and April 20, 2013, with a common final submission deadline on June 12, 2013. For track 1 a training dataset with correct labels was diclosed at the start of the competition. This track was the most popular one, attracting submissions of 561 different teams. Track 2, which was formulated as an unsupervised learning task, received submissions from 241 participants. This paper presents details about the problem definitions, the datasets, the evaluation metrics and the results.
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U2 - 10.1145/2517288.2517299
DO - 10.1145/2517288.2517299
M3 - Conference contribution
AN - SCOPUS:85146685284
SN - 9781450324953
T3 - Proceedings of the 2013 KDD Cup 2013 Workshop
BT - Proceedings of the 2013 KDD Cup 2013 Workshop
PB - Association for Computing Machinery
T2 - 2013 KDD Cup 2013 Workshop
Y2 - 11 August 2013 through 14 August 2013
ER -