Comparative Study of Name Disambiguation Problem using a Scalable Blocking-based Framework

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Comparative Study of Name Disambiguation Problem
using a Scalable Blocking-based Framework

• Byung-Won On, Dongwon Lee, Jaewoo Kang, Prasenjit
  Mitra
• JCDL05

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Abstract

• They consider the problem of ambiguous author
  names in bibliographic citations.
• Scalable two-step framework
• Reduce the number of candidates via blocking
  (four methods)
• Measure the distance of two names via coauthor
  information (seven measures)

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Introduction

• Citation records are important resources for
  academic communities.
• Keeping citations correct and up-to-date proved
  to be a challenging task in a large-scale.
• We focus on the problem of ambiguous author
  names.
• It is difficult to get the complete list of the
  publications of some authors.
• John Doe published 100 articles, but DL keeps
  two separate purported author names, John Doe
  and J. D. Doe, each contains 50 citations.

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Problem

• Problem definition
• The baseline approach

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Solution

• Rather than comparing each pair of author names
  to find similar names, they advocate a scalable
  two-step name disambiguation framework.
• Partition all author-name strings into blocks
• Visit each block and compare all possible pairs
  of names within the block

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Solution Overview
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Blocking (1/3)

• The goal of step 1 is to put similar records into
  the same group by some criteria.
• They examine four representative blocking methods
• heuristics, token-based, n-gram, sampling

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Blocking (2/3)

• Spelling-based heuristics
• Group author names based on name spellings
• Heuristics iFfL, iFiL, fL, combination
• iFfL e.g. Jeffrey Ullman, J. Ullman
• Token-based
• Author names sharing at least one common token
  are grouped into the same block
• e.g., Jeffrey D. Ullman and Ullman, Jason

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Blocking (3/3)

• N-gram
• N4
• The number of author names put into the same
  block is the largest one.
• e.g. David R. Johnson, F. Barr-David
• Sampling
• Sampling-based join approximation
• Each token from all author names has an TFIDF
  weight.
• Each author name has its token weight vector.
• All pairs of names with similarity of at least ?
  can be put into the same block.

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Measuring Distances

• The goal of step 2 is, for each block, to
  identify top-k author names that are the closest.
• Supervised method
• Naïve Bayes Model, Support Vector Machine
• Unsupervised method
• String-based Distance, Vector-based Cosine
  Distance

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Supervised Methods (1)

• Naïve Bayes Model
• Training
• A collection of coauthors of x are randomly
  split, and only the half is used for training.
• They estimate each coauthors conditional
  probability P(Ajx)
• Testing

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Supervised Methods (2)

• Support Vector Machine
• All coauthor information of an author in a block
  is transformed into vector-space representation.
• Author names in a block are randomly split, 50
  is used for training, and the other 50 is used
  for testing.
• SVM creates a maximum-margin hyperplane that
  splits the YES and NO training examples.
• In testing, the SVM classifies vectors by mapping
  them via kernel trick to a high dimensional.
• Radial Basis Function kernel

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Unsupervised Methods(1)

• String-based Distance
• The distance between two author names are
  measured by the distance between their coauthor
  lists.
• Two token-based string distances
• Two edit-distance-based string distances

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Unsupervised Methods(2)

• Vector-based Cosine Distance
• They model the coauthor lists as vectors in the
  vector space and compute the distances between
  the vectors.
• They use the simple cosine distance.

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Experiment
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Data Sets

• They gathered real citation data from four
  different domains.
• DBLP, e-Print, BioMed, EconPapers
• Different disciplines appear to have slightly
  different citation policies and the conventions
  of citations also vary.
• Number of coauthors per article
• Use the initial of first name instead of full
  name

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Artificial name variants

• Given the large number of citations, it is not
  possible nor practical to find a real solution
  set.
• They pick top-100 author names from Y according
  to their number of citations, and generate 100
  corresponding new name variants artificially.
• Grzegorz Rozenberg with 344 citations and 114
  coauthors in DBLP, we create a new name like G.
  Rozenberg or Grzegorz Rozenbergg.
• Splitting the original 344 citations into halves,
  each name carries half of citations 172
• They test if the algorithm is able to find the
  corresponding artificial name variant in Y

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Artificial name variants

• Error type e.g. Ji-Woo K. Li
• Abbreviation J. K. Ki
• Name alternation Li, Ji-Woo K.
• Typo Ji-Woo K. Lee or Jee-Woo K. Li
• Contraction Jiwoo K. Li
• Omission Ji-Woo Li
• Combinations
• The quantify the effect of error types on the
  accuracy of name disambiguation is measured.

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Artificial name variants

• (1) mixed error types of abbreviation (30),
  alternation (30), typo (12 each in first/last
  name), contraction (2), omission (4), and
  combination (10)
• (2) abbreviation of the first name (85) and typo
  (15)

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Evaluation metrics

• Scalability
• Size of blocks generated in step 1
• Time it took to process both step 1 and 2
• Accuracy
• They measured the accuracy of top-k.

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Scalability

• The average of authors in each block
• Processing time for step 1 and 2

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Accuracy

• Four blocking methods combined with seven
  distance metrics for all four data set with k
  5.
• EconPapers data set is omitted.

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Conclusion

• They compared various configurations (four
  blocking in step 1, seven distance metrics via
  coauthor information in step 2), against four
  data sets.
• A combination of token-based or N-gram blocking
  (step 1) and SVM as a supervised method or cosine
  metric as a unsupervised method (step 2) gave the
  best scalability/accuracy trade-off.
• The accuracy of simple name spelling based
  heuristics were shown to be quite sensitive to
  the error types.
• Edit distance based distance metrics such as Jaro
  or Jaro-Winkler proved to be inadequate for
  large-scale name disambiguation problem for its
  slow processing time.