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Proximity Analysis among Researchers at UP and some Institutions
using Bibliographic Databases
L. Trigo 1
1 Faculty of Economics, University of Porto, Portugal.
Before starting an investigation in particular, the researcher often might want to know which other
researchers share the same thematic concerns. Large research institutions, like UP, face the problem
that searching and finding people with similar interests can be a complex task.
The objective of this work is to automate the construction of a proximity matrix among researchers
from UP and INESC Porto research unit. The matrix can be simply transformed into a graph
representation. In the first phase, when our concern was to develop a working prototype, we have
focused on a few researchers from LIAAD-INESC Porto only. This phase was completed. Some results
are shown for illustration below. In the next phase we will extend the set of researchers to other
institutions, including the members of some units of INESC Porto and/or some other R&D unit
associated with UP (e.g. CRACS). Then we will gradually extend the prototype to more and more
institutions and people.
This goal of constructing a proximity matrix is achieved with the help of existing text mining
techniques and applying them to the items found in bibliographic databases. The prototype was
developed in language R, which includes package “tm” that facilitates this work, as it contains many
useful functions that can be exploited. The first task is to collect names from the institution web site
(e.g. web site of LIAAD). In the next step we collect the list of publication titles for each person. We
can find these, for instance, on bibliographic database of Digital Bibliography & Library Project
(DBLP).
After downloading each page relative to the respective researcher, we have to process the html files to
retrieve the paper titles. The resulting document collection represents our target corpus. Each
“document” characterizes a particular researcher. It includes all words that appear in his/her
publications and their frequencies. The document collection can be processed by applying stemming,
removal of stop words, spaces, punctuation and numbers. The result is called document-term matrix.
The lines correspond to individual researchers. In the next step it is possible to calculate a cosine
measure of proximity / distance for any two lines (researchers). The result can be represented in the
form of a matrix showing all proximity figures. An example of a dissimilarity matrix that has been
obtained with our prototype is shown below.
#1 #2 #3 #4 #5 #6
1 – J. Gama 0 0.573 0.959 0.529 0.538 0.595
2 – P. Brazdil 0.573 0 0.981 0.672 0.705 0.623
3 – A. M. Jorge 0.959 0.981 0 0.98 0.893 1
4 – L. Torgo 0.529 0.672 0.98 0 0.565 0.676
5 – C. Soares 0.538 0.705 0.893 0.565 0 0.816
6 – J. F. Gonçalves 0.595 0.623 1 0.676 0.816 0
For instance, the most related work to J. Gama is the work from L.Torgo (0.529). And the second most
related work to J. Gama is the one from C. Soares (0.538).
References
[1] R. Feldman, J. Sanger: The Text Mining Textbook: Advanced Approaches in Analyzing
Unstructured Data, Cambridge Univ. Press, 2007
Acknowledgements: I wish to express my gratitude to Prof. P. Brazdil (LIAAD / FEP) for suggesting
this problem and for his supervision.
3 rd meeting of young researchers at UP 239