Linked science presentation 25

Clustering Citation Distributions for
Semantic Categorization and
Citation Prediction
Francesco Osbornea , Silvio Peronibc, Enrico Mottaa,
a KMi, The Open University, United Kingdom
b Department of Computer Science and Engineering, University
of Bologna, Bologna, Italy
c Institute of Cognitive Sciences and Technologies, CNR, Rome,
Italy
October 2014

Is it possible to say who will have a
bigger impact?

Can I exploit this information for semantic
expert search?

Clustering
of Citation
Distribution
Authors’
data
Clusters of authors
with similar citation
patterns
EExxttrraaccttiioonn ooff
semantic
features
RDF
BiDO Ontology
Our approach

Clustering Citation Distributions
We cluster the citation distributions by
exploiting a bottom-up hierarchical clustering
algorithm.
We thus need to define:
• A norm
• A metric to assess the quality of a set of
clusters

A B
C D

A B
C D
1. dis(A, B) = dis(C, D)

A B
C D
2. dis(A, C) > 0 , dis(B, D) > 0

A B
C D
2. dis(A, C) > 0 , dis(B, D) > 0
3. Can be computed incrementally

A simple way to satisfy these three
requirements is to use a normalized Euclidean
distance:

We want to maximize the homogeneity of the
cluster populations in the following years.

Standard deviation is not the solution…

We estimate the homogeneity by computing the
weighted average of the MAD:

MAD (Median Absolute Deviation ) is a robust
measure of statistical dispersion and it is used to
compute the variability of an univariate sample
of quantitative data.

We then compute the memberships of all authors
in our dataset with the centroids of the resulting
clusters.
!
Σ
$%'
(')*,,
$%'
(')-,,

/./0
Finally we calculate a number of statistics for
estimating the evolution of the members of each
clusters.

How can we represent this data?
Bibliometric data are subject to the simultaneous application of
different variables. In particular, one should take into account at
least:
• the temporal association of such data to entities;
• the particular agent who provided such data (e.g., Google
Scholar, Scopus, our algorithm);
• the characterisation of such data in at least two different
kinds, i.e., numeric bibliometric data (e.g., the standard
bibliometric measures such as h-index, journal impact factor,
citation count) and categorial bibliometric data (so as to
enable the description of entities, e.g., authors, according to
specific descriptive categories).

Extraction of Semantic features

:hasCurve [ a :Curve ;
:hasTrend :increasing ;

:hasTrend :increasing ; :hasAccelerationPoint :premature-deceleration ] ;

:hasSlope [ a :Slope ; :hasStrength :low ;

:hasSlope [ a :Slope ; :hasStrength :low ; :hasGrowth :logarithmic ] ;

:hasOrderOfMagnitude :[243,729) ;

:concernsResearchPeriod :5-years-beginning .

:increasing-with-premature-deceleration-and-low-logarithmic-slope-in-
[243,729)-5-years-beginning a :ResearchCareerCategory ;

:john-doe :holdsBibliometricDataInTime [
a :BibliometricDataInTime ;
tvc:atTime [ a time:Interval ; time:hasBeginning :2014-07-11 ] ;
:accordingTo [ a fabio:Algorithm ;
:increasing-with-premature-deceleration-and-low-logarithmic-slope-in-[243,729)-
5-years-beginning a :ResearchCareerCategory ;
frbr:realization [ a fabio:ComputerProgram ] ] ;
:withBibliometricData
:increasing-with-premature-deceleration-and-low-logarithmic-
slope-in-[243,729)-5-years-beginning .

Evaluation
• We evaluated our method on a dataset of 20000 researchers
working in the field of computer science in the 1990-2010
interval.
• This dataset was derived from the database of Rexplore , a
system to provide support for exploring scholarly data, which
integrates several data sources (Microsoft Academic Search,
DBLP++ and DBpedia).

Evaluation
Y
C18 (1.4%) C22 (2.5%) C25 (2.7%) C28 (2.3%) C29 (8.8%)
range mean range mean range mean range mean range mean
6 420-800 567±98 160-280 209±34 100-180 129±25 60-100 72±14 40-60 39±9
7 440-960 610±120 160-320 225±45 100-200 138±30 60-120 79±18 40-80 45±14
8 440-1020 650±137 160-400 246±58 100-260 158±45 60-160 90±26 40-100 50±18
9 440-1260 699±186 160-440 269±74 100-340 187±68 60-200 104±37 40-120 57±25
10 480-2940 751±411 160-500 292±85 100-400 211±82 60-280 125±57 40-160 68±35
11 480-2480 826±336 180-660 331±112 100-520 241±100 60-540 155±103 40-200 82±47
12 480-3520 914±467 180-860 370±151 100-640 270±126 60-440 166±96 40-260 97±60

Linked science presentation 25

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