=Paper=
{{Paper
|id=Vol-1282/paper9
|storemode=property
|title=Clustering Citation Distributions for Semantic Categorization and Citation Prediction
|pdfUrl=https://ceur-ws.org/Vol-1282/lisc2014_submission_9.pdf
|volume=Vol-1282
|dblpUrl=https://dblp.org/rec/conf/semweb/OsbornePM14
}}
==Clustering Citation Distributions for Semantic Categorization and Citation Prediction==
https://ceur-ws.org/Vol-1282/lisc2014_submission_9.pdf
Clustering Citation Distributions for
Semantic Categorization and Citation Prediction
Francesco Osborne1, Silvio Peroni2,3, Enrico Motta1
1
Knowledge Media Institute, The Open University, Milton Keynes, UK
{francesco.osborne,e.motta}@open.ac.uk
2
Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
silvio.peroni@unibo.it
3
STLab, Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy
Abstract. In this paper we present i) an approach for clustering authors
according to their citation distributions and ii) an ontology, the Bibliometric
Data Ontology, for supporting the formal representation of such clusters. This
method allows the formulation of queries which take in consideration the
citation behaviour of an author and predicts with a good level of accuracy future
citation behaviours. We evaluate our approach with respect to alternative
solutions and discuss the predicting abilities of the identified clusters.
Keywords: Semantic Web, Research Data, Bibliometric Data, Expert Search,
Hierarchical Clustering, Data Mining, OWL, RDF, SPARQL, BiDO
1 Introduction
Exploring and analysing scholarly data [1] help to understand the research dynamics,
forecast trends and derive new knowledge, which can be effectively represented by
semantic technologies. Within this context, two important tasks are:
1) classifying authors according to a variety of semantic categories in order to
facilitate querying, sharing and reusing such data in different context;
2) forecasting their career trends, allowing us to estimate their future citation
behaviour.
In this paper we will present an innovative approach to address both tasks by
exploiting author citation distributions.
Most of today systems for the exploration of academic data offer citations or
citations-based indexes (e.g., h-index, g-index) as ranking metrics and provide
interesting visualizations of citation distributions. However, they do not exploit many
interesting features which can be derived by the analysis of citation distributions, such
as: 1) the trend of the distribution within a certain time interval (e.g., it is steadily
rising), 2) the timing of possible acceleration/deceleration (e.g., it started to rise much
faster in the last 3 years), 3) the slope of the citation curve (e.g., every year it gains
20% more citations than the year before), 4) the shape of the citation curve (e.g., it is
growing according to a logarithmic function), and 5) the estimated citation behaviour
in the following years (e.g., authors with a similar pattern usually receive 200±50
citation in their 8th career years).
These features can support formulating queries that take in consideration the
diachronic citation behaviour of authors. Examples are: “find all PhD students
working in Semantic Web who exhibit a possible rising star pattern”, “find all the
senior researchers who in their young years exhibited the same citation pattern as
author X” or “find all the postdoc working in UK whose citations exhibit a positive
trend in the last two years and are rising exponentially”.
Analysing the citation distributions can also foster a better understanding of the
dynamics of an author career, since it makes possible to categorize different kinds of
patterns and to study how they evolve. Moreover, it can allow us to forecast the future
citation behaviour of research communities or organizations by studying the patterns
of their members.
In this paper we present an approach for clustering authors according to their
citation distributions, with the aim of extracting useful semantic information and
producing statistical evidence about the potential citation behaviour of specific
categories of researchers. In addition, we introduce an ontology, i.e., the Bibliometric
Data Ontology (BiDO), which allows an accurate representation of such clusters (and
their intended semantics) according to specific categories.
The rest of the paper is organized as follows. In Section 2, we discuss existing
approaches for clustering authors and predicting future citations. Section 3 describes
our approach for clustering authors’ citation distributions, while Section 4 illustrates
BiDO and introduces the steps for associating the identified clusters to ontological
categories. In Section 5, we evaluate our approach versus alternative solutions and
discuss the predictive abilities of the identified clusters. Finally, in Section 6, we
summarize the key contributions of this paper and outline future directions of
research.
2 Related Work
Classifying entities associated to a time series is a common task that is traditionally
addressed with a variety of clustering techniques [2]. Citation distributions and their
mathematical properties have been carefully analysed in a number of empirical
studies (e.g., [3]). However, while academic authors are often classified by
community detection and clustering algorithms with the aim of identifying different
kinds of research communities [4,5], no current model exploits clusters of citation
distributions to classify researchers according to the features described earlier and
estimate their future citation behaviour.
In the past, several works have been published about the identification of the
factors that allow the prediction of future citations. Their analyses, and the related
statistical models and machine learning techniques proposed for such predictions, are
usually performed according to specific hypotheses: taking into consideration only
articles of high-rated journals of a certain discipline; analysing only particular kinds
of articles (e.g., clinical articles); choosing only multidisciplinary journals so as to
increase the coverage (and the variability) of the research communities involved; and
so forth2. As a result, different starting hypothesis gave rise to different (even
contrasting) discriminating factors and prediction models.
However, most of these works agree on the existence of two different and
complementary kinds of factors:
• intrinsic factors, i.e., those related with the qualitative evaluation of the content
of articles (quality of the arguments, identification of citation functions, etc.);
• extrinsic factors, i.e., those referring to quantitative characteristics of articles
such as their metadata (number of authors, number of references, etc.) and
other contextual characteristics (the impact of publishing venue, the number of
citation received during time, etc.).
The use of intrinsic factors data can be very effective but also time consuming.
They can be gathered manually by humans, e.g., through questionnaires to assess the
intellectual perceptions of an article (as in peer review processes). For instance, in [7]
the authors show how the editor’s and reviewer’s ratings (in the context of the
Journal of Cardiovascular Research, http://cardiovascres.oxfordjournals.org) are
good predictors of future citations.
The data of some intrinsic factors, such as the identification of citation functions
(i.e., author’s reasons for citing a certain paper), can also be gathered automatically
with the aim of being used to provide alternative metrics for assessing or predicting
the importance of articles through machine learning techniques (cf. [8]), probabilistic
models (cf. [9]), and other architectures based on deep machine reading (cf. [10]),
However, these approaches use extrinsic factors, rather than intrinsic ones, for the
analysis of the importance of articles, because of the time-consuming nature of the
latter ones and the quick availability (usually at publication time) of most of the
extrinsic-based data. In [11], Didegah and Thelwall investigate the extrinsic factors
that better correlate with citation counts, identifying three factors as the best ones for
such prediction: the impact factor of the journals where articles have been published,
the number of references in articles, and the impact of the papers that have been cited
by the articles in consideration. Other extrinsic factors identified in other studies are
article length (in terms of printed pages) [12], number of co-authors [13], rank of
author’s affiliation [13], number of bibliographic databases in which a journal was
indexed [14], proportion of the journal articles published that had been judged of high
quality by some authoritative source [14], and price index [6]. Slightly different kinds
of extrinsic factors were considered in Thelwall et al.’s work on altmetrics [15]. The
authors analysed eleven different altmetrics sources and found that six of them were
good predictors of future citations (i.e., tweets, Facebook posts, Nature research
highlights, blog mentions, mainstream media mentions and forum posts).
3 Clustering Citation Distributions
In this section, we will present our approach for detecting clusters of researchers who
share a similar citation distribution. We want to identify clusters characterized by
citation distributions which represent the typical patterns of some categories of
2 A good literature review of a large number of such approaches is available in [6].
authors, so that each cluster will suggest a common future behaviour. More formally,
we want to subdivide the authors in sets, in such a way that the population of each set
will remain homogenous with respect to the number of citations collected in the
following years, i.e., the members of each cluster will have a similar number of
citations also in the future.
Our approach takes as input the citation distributions of authors in a certain time
interval and returns 1) a set of clusters with centroids that describe the most typical
citation patterns, 2) a matrix associating each author with a number of clusters via a
membership function, and 3) a number of statistics associated to each cluster for
estimating the evolution of the authors in that cluster.
We cluster the citation distributions by exploiting a bottom-up hierarchical
clustering algorithm. The algorithm takes as input a matrix containing the distance
between each couple of entities and initially considers every entity as a cluster. It then
computes the distance between each of the clusters, joining the two most similar
clusters at each iteration. We adopt a single-linkage strategy by estimating the
distance between two clusters C1 and C2 as the shortest distance between a member of
C1 and a member of C2. The algorithm stops when it reaches a certain distance
threshold t.
To obtain cluster sets that are fit for our purpose we must thus define accordingly
1) the metric to compute the distance between each couple of citation distributions
and 2) a method to decide the threshold t.
It is possible to measure the distance between two time series by means of metrics
such as the Euclidean distance or cosine similarity. Unfortunately both of these
solutions have some shortcomings in this case. In fact, when using the Euclidean
distance, covariates with the highest variance will drive the clustering process: a
threshold value that allows clustering distributions of a certain scale (e.g., 200
citations) will also merge together perfectly valid clusters of minor scale (e.g., 20
citations). The distance based on the cosine similarity (e.g., the inverse minus one)
will solve this problem since it is scale-invariant; unfortunately it would also cluster
together distributions of completely different scale but with the same shape (e.g.,
[1,1,2] and [100,100,200]). Let us assume a couple of citation distributions A and B
having both a total of n citations, and a different couple of them C and D with m
citation each, C having the same distribution as A, and D the same as B. We want a
distance that will yield dis{A,B} = dis{C,D} (avoiding the covariate with the highest
variance to drive the clustering) and also dis{A,C} > 0 (making scale a feature), and
furthermore can be calculated incrementally (thus sparing processing time by
stopping the computation over a threshold). A simple way to satisfy these three
requirements makes use of a Euclidean distance normalized with the number of total
citations of both distributions (similarly to [16]):
/2 (1)
where and are the number of citations of the two distributions in the i-th year.
We also want to choose a threshold value t that will maximize the homogeneity of
the cluster populations in the following years. We compute the homogeneity of a
population with respect to citations using the Median Absolute Deviation (MAD).
MAD is a robust measure of statistical dispersion [17] and it is used to compute the
variability of an univariate sample of quantitative data. It was first used by Gauss for
determining the accuracy of numerical observations and it is defined as the median of
the absolute deviations from the original data's median:
! "# $ !% "% &#& (2)
The procedure for computing the MAD consists in calculating the median of the n
original data (x1,x2,…,xj,…,xn), computing the differences between each one of the n
original values xi and the median of the whole data distribution and finally computing
the median of the previous differences. We preferred MAD to different solutions,
such as standard deviation, for its robustness. In fact, standard deviation is too much
influenced by outliers such as a few authors with a very high number of citations.
Hence, we estimate the quality of a set of clusters in a certain year by computing the
weighted average of their MAD:
∑*+,- ∙/0-
'( /0-
(3)
where 1 and 1 are respectively the MAD and the number of authors
associated with the i-th cluster.
We set the threshold t by running the hierarchical algorithm with different t values
and then selecting the threshold which yields clusters with the lowest average MADav
in the following n years (n=10 in the herein presented evaluation). For characterizing
completely the author space we compute the clusters for different intervals of time,
e.g., 1-5, 1-10 and 1-15 career years, using a significant author sample (e.g., 5000).
We then compute the memberships of all authors in our dataset with the centroids of
the resulting clusters, so as to determine exactly how much a specific author is similar
to each cluster centroid. For associating authors to clusters, we adopt the well known
membership formula of the Fuzzy C-Mean algorithm [18], that is:
3
2 /@AB (4)
5 67898:; ,=
∑4 ?
5 67898:>,=
where 2 is the membership value of author x with cluster k,
C1 !D E , the distance between x and the centroid of cluster i, and m is a
constant for modulating the level of cluster fuzziness (m=2 in the prototype).
Finally we analyse the distribution of each cluster population with respect to the
number of citations received in the following years, in order to extract statistical
evidence about their future behaviour. As mentioned before, standard deviation is
severely influenced even by few outliers, making it hard to use the mean on the full
population as a predictor. Hence, for each year we automatically select a percentage p
of the population (e.g., 90%) in the most populated area of the distribution and
compute its interval of citations (e.g., 40-80), mean (e.g., 45) and standard deviation
(e.g., 14). Technically, we do so by computing the number of authors who fall into
different ranges of citations, ordering those categories in decreasing order and then
selecting the authors from subsequently smaller categories until the percentage of
authors selected is equal to p. The citation interval, mean and standard deviation of
this sample produce accurate, intuitive and statistically sound predictions which are
more resilient to outliers.
Intuitively, some categories of authors are too mundane to suggest a common
future behaviour, and may be used only for classification purposes. Hence, in this
phase we care especially about the “uncommon signature” that points to particularly
homogenous population of authors. Figure 1 shows the distributions of authors in
their seventh career year associated to some clusters detected by analysing their first
five career years (the dashed line refers to the overall distribution). Clusters C29 and
C30 are associated with a very specific citation patterns and thus their distributions
have a small kurtosis and point to two narrow categories of authors who normally
receive a relatively low number of citations. Clusters C25 and C28 are also quite
homogeneous and represent two distinct populations of more frequently cited authors.
Naturally, the homogeneity of the population associated with a cluster will decrease in
the following years and so will the accuracy of the predictions.
Figure 1. Percentage of authors vs. number of their citations in their 7th career
year. The clusters were derived by the citations received over the first 5 career years.
4 An ontology for describing bibliometric data
Having a model developed according to a well-known format (such as OWL) for
enabling the classification of authors and journals according to bibliometric data is
crucial to allow one to query, share and reuse such data in different context, e.g., for
providing smart visualisation of bibliometric data for sense-making activities and for
enabling automatic reasoning on them.
However, bibliometric data are not simple objects, since they 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, in order to say that a
particular value, e.g., the fact that an article has been cited 42 times, was
associated to such article only for a time period;
• the particular agent who provided such data (e.g., Google Scholar, Scopus, our
algorithm), in order to keep track of the way data evolve in time according to
particular sources;
• 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).
The time-indexed value in time (TVC) ontology design pattern [19] seems to be a
good starting model for the development of an ontology for bibliometric data, since
TVC’s entities enable the precise description of all the aforementioned variables:
time, responsible agent and kinds of data.
Figure 2. A: the core module of BiDO, describing generic bibliometric data with
their characterising variables. B: the module modelling a particular kind of categorial
bibliometric data, i.e., the research career categories, according to the main
dimensions used by the algorithm in Section 3.
Starting from TVC, we have created the Bibliometric Data Ontology (BiDO,
available at http://purl.org/spar/bido), i.e., a modular OWL 2 ontology that allows the
description of bibliometric data of people, articles, journals, and other entities
described by the SPAR Ontologies (http://purl.org/spar) in RDF.
The core module of the ontology, shown in Fig. 2.A, allows us to describe any
entity and the related bibliometric data (through the property
holdsBibliometricDataInTime) at a certain time (i.e., tvc:atTime, a property defined
by the imported TVC ontology for specifying temporal instants or intervals) and
according to a certain agent (through the property accordingTo, which is a sub-
property of prov:wasAttributedTo and allows us to indicate the agent responsible for
such bibliometric data). In addition, BiDO imports PROV-O [20] for adding
provenance data about the activities related to the creation of such bibliometric data.
Two alternative kinds of bibliometric data are specifiable (through the property
withBibliometricData) in BiDO: numeric and categorial bibliometric data. Numeric
bibliometric data are those characterised by a certain integer or float value related to a
particular bibliometric measure. Some of these measures – i.e., h-index, author
citation count, e-index, and journal impact factor – are available in a particular
module of BiDO responsible for describing the most common bibliometric measures.
We have developed an additional module of BiDO that extends the class
CategorialBibliometricData of the core module with specific categories describing
the research career of people, in order to address the mapping of the clusters identified
by the algorithm presented in Section 3 with specific facets. As shown in Fig. 2.B,
these facets are described by the class ResearchCareerCategory, which is
characterised by four specific dimensions that have been used by our algorithm to
cluster citation data:
• the research period considered, i.e., the interval of research years that the
algorithm is taking into consideration (e.g., the first 5/10 years);
• the curve, i.e., the specific shape proper to the clusters identified by the
algorithm, which is characterised by a trend (flat/increasing/decreasing) and,
in the latter two cases, by an acceleration or deceleration point (none or
premature, median, overdue acceleration/deceleration);
• the slope of such curve, in terms of strength (low/moderate/high) and kind of
growth (linear/polynomial/exponential/logarithmic);
• the order of magnitude, which categorises the number of citations received
in the considered period according to a uniform model of common-sense
estimation [21], which describes intervals of half-order of magnitude – i.e.,
“[0,1)”, “[1,3)”, “[3,9)”, “[9,27)”, “[27,81)”, “[81,243)”, “[243,729)”, etc.
The combinations of all these values related to the aforementioned dimensions
have been used to define all the possible descriptive categories of research career of
people as instances of the class ResearchCareerCategory.
Even if we did not define a particular category for each cluster found by the
algorithm – rather, more clusters can be described by the same category –, we have
defined an algorithmic procedure to determine the association between the cluster
centroids and the categories described by the ontological model. For instance, let us
consider the centroid “[31.3, 46.1, 52.8, 55.3, 60.8]”3 of one of the clusters detected
by our algorithm according to the first 5 years of research career. The related
dimensions are identified in the following way:
• order of magnitude: we sum the values of the cluster centroid and select the
interval containing such sum, i.e., “[243,729)”;
• curve trend: the linear regression of the centroid is calculated, and then its
slope is divided by the mean of all the centroid values. If the result of such
division is greater than 0.05, then we have an increasing trend (which is the
case of our example, since that value is 0.14), if it is less than -0.05 we have
a decreasing trend, otherwise we have what we can approximately consider a
flat trend;
3
The five values of the centroid identify the number of citations that have been received during
the five years of the research period considered.
• curve acceleration: the ratio of the slopes of the linear regressions of series
k-n and 1-k (for each k between 2 and n - 1, where n is length of the list of
values defining a cluster centroid) is calculated, in order to identify in which
year (i.e., k) the acceleration or deceleration (this is the case of our example)
happens, if any. Then, the acceleration/deceleration is considered premature
if F G H !/3J (as in our example), overdue if k ≥ H 2!/3J, and median
otherwise;
• slope strength: the linear regression of the centroid is calculated, its slope is
divided by the mean of all the centroid values, and then we calculate the
absolute value s of this division. We say that the slope strength is low if
s<0.25 (as in our example), high if s>0.45, and moderate otherwise;
• slope growth: by means of the least squares method, we create the four
functions (one linear, one polynomial, one exponential and one logarithmic)
that best match with the cluster centroid. Then we compare the centroid data
with such functions through Wilcoxon’s non-parametric test for matched
data and choose the best fitting function (logarithmic in our example).
Following these steps, the example cluster we considered is mapped in the
following category:
:increasing-with-premature-deceleration-and-low-logarithmic-slope-in-[243,729)-5-
years-beginning a :ResearchCareerCategory ;
:hasCurve [ a :Curve ;
:hasTrend :increasing ; :hasAccelerationPoint :premature-deceleration ] ;
:hasSlope [ a :Slope ; :hasStrength :low ; :hasGrowth :logarithmic ] ;
:hasOrderOfMagnitude :[243,729) ;
:concernsResearchPeriod :5-years-beginning .
Thus, combining the results of our clustering algorithm with BiDO it is possible to
associate authors with specific categories describing their research career as follows:
ex:john-doe :holdsBibliometricDataInTime [
a :BibliometricDataInTime ;
tvc:atTime [ a time:Interval ; time:hasBeginning :2014-07-11 ] ;
:accordingTo [ a fabio:Algorithm ;
frbr:realization [ a fabio:ComputerProgram ] ] ;
:withBibliometricData
:increasing-with-premature-deceleration-and-low-logarithmic-slope-in-
[243,729)-5-years-beginning .
The RDF descriptions of such bibliometric data make easier to query them with
standard languages such as SPARQL, in order to retrieve, for instance, all the authors
that in the first 5 years of their research career had a citation behaviour pattern like
that described by the aforementioned category.
5 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 [1], a system that combines statistical analysis, semantic
technologies and visual analytics to provide support for exploring scholarly data, and
integrates several data sources (Microsoft Academic Search, DBLP++ and DBpedia).
In particular we wanted to show that the normalized Euclidean distance introduced
in Section 3 works better than other choices for the task of clustering citation
distributions. Hence, we compared three metrics: the normalized Euclidean distance
(label NEU), the Euclidean distance (EU) and the distance based on the cosine
similarity (CO). We measured the quality of the produced set of clusters in a certain
year by their MADav, as in Formula (3).
Figure 3. Comparison between NEU, EU and CO applied on the first five and ten
career years according to their MADav in the following five years.
Figure 3 shows the performance of the three techniques when clustering the first
five and ten career years. In all cases the normalized version of the Euclidean distance
performs much better than the other solutions, being characterized by a smaller
MADav value, e.g., a smaller degree of dispersion. CO performs slightly better than
EU in the 1-5 years interval while EU performs better than CO in the 1-10 years
interval. Analogous results were obtained by considering the weighted average of
standard deviation rather than MADav.
Career C18 (1.4%) C22 (2.5%) C25 (2.7%) C28 (2.3%) C29 (8.8%)
year range mean±s.d. range mean±s.d. range mean±s.d. range mean±s.d. range mean±s.d.
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
Table 1. Range of citations and mean citations in subsequent career years
predicted with 75% accuracy for authors associated with clusters detected in the 1-5
career year interval. In parenthesis the percentage of authors in each cluster.
Our approach yields a number of clusters with different prediction capabilities. We
can suggest a narrower or larger interval of predicted citations for increasing or
lowering the precision of our predictions. Table 1 shows some example of predictions
that yield 75% accuracy. For example we are able to suggest with 75% precision that
2.5% authors in Computer Science associated with cluster C22 will have 225±45
average citations in their seventh career years (with a minimum number of citations
equal to 160 and a maximum one equal to 320).
The left panel of Figure 4 shows the citation distributions of the centroids of the
cluster in Table 1 and the algorithm predictions. Even if the predictions become less
accurate in time, however they still can give a fair idea of the kind of potential citation
behaviour of the authors. Moreover, these predictions are particular valuable for
forecasting the future citation behaviour of an organization or research communities.
In fact, while it is relatively hard to foresee a single author’s citation behaviour (e.g.,
she/he may be an outlier), it is much easier to compute the predicted citations of a
group of authors since in a large sample statistical fluctuations have a smaller weight.
Finally, the right panel of Figure 4 shows the evolution of some the main clusters
in terms of average citations of the associated authors. We can notice that our
approach allows a very good coverage of the possible career trajectories, from the
most modest to the outstanding ones. This variety of patterns allow also for a very
fine-grained semantic classification of researcher careers.
Figure 4. Left Panel: the citation distributions of the centroids of the clusters in
Table 1 and the resulting predictions (the error bars represent the standard deviations
of the predicted citations). Right Panel: the evolution in term of average number of
citations of the authors associated to the main clusters in the 1-5 interval.
6 Conclusion
In this paper, we presented a novel approach for clustering author’s citation
distributions, with the aim of 1) classifying authors with a variety of semantic facets,
and 2) forecasting the citation behaviour of categories of researchers. We also
introduced the Bibliometric Data Ontology, a.k.a. BiDO, which is an OWL ontology
that allows an accurate representation of such semantic facets describing people’s
research careers. In addition, we showed that our approach outperforms other
solutions in terms of population homogeneity and is able to categorize a variety of
career trajectories, some of which allow predicting future citations with fair accuracy.
For the future we plan to augment the clustering process with a variety of other
features (e.g., research areas, co-authors), to extend BiDO in order to provide a
semantically-aware description of such new features, and to make available a
triplestore of bibliometric data linked to other datasets such as Semantic Web Dog
Food and DBLP.
References
1. Osborne, F., Motta, E., Mulholland, P.: Exploring Scholarly Data with Rexplore. In
Proceedings of the ISWC 2013: 460-477. (2013)
2. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal
of Intelligent Information Systems, 17(2-3), 107–145. (2001)
3. Redner, S.: How popular is your paper? An empirical study of the citation distribution. The
Physics of Condensed Matter Journal, 4(2), 131–134. (1998)
4. Ding, Y.: Community detection: topological vs. topical. Journal of Infometrics, 5(4). (2011)
5. Osborne, F., Scavo, G., Motta, E.: Identifying diachronic topic-based research communities
by clustering shared research trajectories. In The Semantic Web: Trends and Challenges
(pp. 114-129). Springer International Publishing. (2014)
6. Onodera, N., & Yoshikane, F.: Factors affecting citation rates of research articles: Factors
Affecting Citation Rates of Research Articles. Journal of the Association for Information
Science and Technology. (2014)
7. Opthof, T.: The significance of the peer review process against the background of bias:
priority ratings of reviewers and editors and the prediction of citation, the role of
geographical bias. Cardiovascular Research, 56(3), 339–346. (2002)
8. Teufel, S., Siddharthan, A., Tidhar, D.: Automatic classification of citation function. In
Proceedings of the EMNLP 2006: 103–110. Stroudsburg, Pennsylvania, USA. (2006)
9. Dietz, L., Bickel, S., Scheffer, T.: Unsupervised prediction of citation influences. In
Proceedings of ICML 2007: 233–240. (2007).
10. Di Iorio, A., Nuzzolese, A. G., Peroni, S.: Towards the automatic identification of the
nature of citations. In Proceedings of SePublica 2013. (2013)
11. Didegah, F., Thelwall, M.: Determinants of research citation impact in nanoscience and
nanotechnology. Journal of the American Society for Information Science and Technology,
64(5): 1055–1064. (2013)
12. Falagas, M. E., Zarkali, A., Karageorgopoulos, D. E., Bardakas, V., Mavros, M. N.: The
Impact of Article Length on the Number of Future Citations: A Bibliometric Analysis of
General Medicine Journals. PLoS ONE, 8(2), e49476. (2013)
13. Antonakis, J., Bastardoz, N., Liu, Y., Schriesheim, C. A.: What makes articles highly cited?
The Leadership Quarterly, 25(1), 152–179. (2014)
14. Lokker, C., McKibbon, K. A., McKinlay, R. J., Wilczynski, N. L., Haynes, R. B.:
Prediction of citation counts for clinical articles at two years using data available within
three weeks of publication: retrospective cohort study. BMJ, 336(7645), 655–657. (2008)
15. Thelwall, M., Haustein, S., Larivière, V., Sugimoto, C. R.: Do Altmetrics Work? Twitter
and Ten Other Social Web Services. PLoS ONE, 8(5), e64841. (2013)
16. Ning, T.: Computing correlation integral with the Euclidean distance normalized by the
embedding dimension. In Proceedings of ICSP 2008: 2708–2712. (2008)
17. Hoaglin, D. C., Mosteller, F., Tukey, J. W.: Understanding robust and exploratory data
analysis (Vol. 3). New York: Wiley. (1983)
18. Bezdek, J. C., Ehrlich, R., Full, W.: FCM: The fuzzy c-means clustering algorithm.
Computers and Geosciences, 10(2), 191–203. (1984).
19. Peroni, S., Shotton, D., Vitali, F.: Scholarly publishing and linked data: describing roles,
statuses, temporal and contextual extents. In Proceedings of i-Semantics 2012: 9–16.
(2012).
20. Lebo, T., Sahoo, S., McGuinness, D.: PROV-O: The PROV Ontology. W3C
Recommendation, 30 April 2013. World Wide Web Consortium. (2013)
21. Hobbs, J. R., Kreinovich, V.: Optimal choice of granularity in commonsense estimation:
Why half-orders of magnitude? International Journal of Intelligent Systems, 21(8), 843–
855. (2006)