The Gold Tree: An Information System for Analyzing Academic

Genealogy

Gabriel Madeira, Eduardo N. Borges, Matheus Bara

nano, Pr

ıcilla Karen Nascimento,

Giancarlo Lucca, Maria de Fatima Maia, Helida Salles and Grac¸aliz Dimuro

Centro de Ci

encias Computacionais, Universidade Federal do Rio Grande – FURG,

{giancarlo.lucca88, mafas.maia, ldasalles, gracaliz}@gmail.com

Keywords:

Academic Genealogy, Genealogy Trees, Data Integration, Information Visualization.

Abstract:

Academic genealogy investigates the relationships between student researchers and academy professionals. In

recent years, it proved to be a powerful technique to help analyze the spread of scientiﬁc knowledge. Tools that

make to visualize these relationships among academics easier are potentially useful and have been proposed.

This work speciﬁes and describes the development of a Web information system for creating and visualizing

academic genealogy trees from a set of metadata extracted and integrated from multiple sources. The proposed

system allows a researcher to query and track information about his or her advisers and graduate students at

any level. A case study was explored to validate the system using data from more than 570 thousand theses

and dissertations.

1 INTRODUCTION

Currently, there are a large number of scientiﬁc publi-

cations and academic papers available in various Web

repositories. Each research institution or university

publishes the results achieved in its own institutional

repository. In this way, scientiﬁc publications are cat-

aloged and organized in a dispersed way. These data

together contain the major scientiﬁc contributions and

collaborations among researchers over time. Analyz-

ing the metadata from multiple publications allows

mapping and understanding how the relationships be-

tween researchers affect the advancement of knowl-

edge in several areas of science.

Genealogy is an auxiliary ﬁeld of history that stud-

ies the origin, evolution and spread of family groups

(Malmgren RD, 2010). This evolution is often repre-

sented using a structured diagram in the form of fam-

ily trees (David and Hayden, 2012). Genealogy trees

are well-known structures that organize, through kin-

ship ties, the whole history of an individual’s ances-

tors. Using this structure we can analyze the origin

and development over time of the lineage of a family.

Genealogy trees can be used in academia to an-

alyze relationships between professors, students, and

researchers. Figure 1 shows an example of academic

genealogy tree. Any metadata sets that describe the

Figure 1: An academic genealogy tree representing advis-

ing relationships in graduate programs. Each edge starts

from the advisor and presents the year of master’s or doc-

toral degree.

elements or their relationships can be used.

When drawing an academic genealogy tree, it is

possible to see who guided a researcher and how he

or she inﬂuenced other researchers over time. Get-

ting a forest (set of trees) makes possible to describe

a research area using metrics that allow, through sta-

tistical analyzes and data mining, to extract relevant

knowledge for the area under study (Chang, 2011).

114

Madeira, G., Borges, E., Barañano, M., Nascimento, P., Lucca, G., Maia, M., Salles, H. and Dimuro, G.

The Gold Tree: An Information System for Analyzing Academic Genealogy.

DOI: 10.5220/0007758401140120

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 114-120

ISBN: 978-989-758-372-8

Therefore, these structures allow us to analyze how

knowledge is spreading across generations of scien-

tists and how these links affect the development of

science.

This paper describes an information system called

The Gold Tree, which goal is to visualize academic

genealogy trees created from a set of metadata ex-

tracted and integrated from multiple sources. The

Information Management Research Group developed

it in Centro de Ci

encias Computacionais at Universi-

dade Federal do Rio Grande (FURG). The proposed

system allows a researcher to query and track infor-

mation about his or her advisers and graduate students

at any level. A case study was explored to validate the

system using data from more than 570 thousand the-

ses and dissertations.

The rest of this paper is organized as follows. In

Section 2, we discuss related work. In Section 3, we

present the methodology to develop the proposed so-

lution. In Section 4, we give details on the obtained

results. Finally, in Section 5, we draw our conclusions

and point out some future work directions.

2 RELATED WORK

In recent years, several studies have explored the visu-

alization of academic collaboration data. While some

platforms such as ResearchGate (Yu et al., 2016),

Google Citations, and the Web of Science (WoS) clas-

sify registered researchers by citation indexing for

their articles and papers (Barab

asi et al., 2002), other

tools such as Pajek (Batagelj and Mrvar, 2002) and

PubNet (Douglas et al., 2005) are only concerned

with viewing research networks. Furthermore, we

point out that there are also solutions that use spe-

ciﬁc data sources to extract information and generate

knowledge from co-authoring relationships (Mena-

Chalco and Cesar-Jr, 2013; Laender et al., 2011). The

following subsections present in detail the work used

as baseline in the validation of the proposed system.

2.1 Academic Family Tree

Neurotree is a Web database created to document

the lineage of academic mentorship in neuroscience

(David and Hayden, 2012). The authors present a

temporal analysis of the database growth in a pe-

riod of seven years. The following metric were per-

formed: the number of researchers and relationships,

the monthly growth rate, the fraction of researchers

linked in the main graph, the average distance be-

tween researchers, and the average number of connec-

tions per researcher. In addition, they report the accu-

racy of related data in Neurotree with data reported

on Web sites of ﬁve research groups. Finally, in order

to study the relationship between mentorship groups

and research areas within neuroscience, they provide

a clustering analysis.

This tree exists as a part of the larger Academic

Family Tree

, which seeks to build a genealogy across

multiple academic ﬁelds, building a single, interdis-

ciplinary academic genealogy. Figure 2 present the

result of a query by research name.

The contents of the database are entirely crowd-

sourced. So it is totally dependent on human effort.

This feature makes it very susceptible to ﬁeld ﬁll er-

rors, as well as always presenting incomplete data.

Any Web user can add information about researchers

and the connections between them, which can leave

the database with poor quality and with false infor-

mation.

2.2 Ac

acia Plataform

The Ac

acia Platform

(Damaceno, 2017) is a sys-

tem created in 2017 for documenting the formal rela-

tions of advising in the context of the Brazilian grad-

uate programs. The system uses data registered in

the Lattes Platform

, which is a database of Brazilian

researchers’ curricula maintained by the Ministry of

Science and Technology and Innovation. Currently,

the Acacia Platform has over 1 million vertices and

relationships. Each vertex represents a researcher

and each edge an advising relation completed be-

tween two researchers (advisor and student). Figure

3 present the result of a query by research name. The

system shows some bibliometric indexes as the num-

ber of direct and indirect descendants and information

about the advising relationships.

2.3 Science Tree

Created in 2015, the Science Tree

application collect

metadata of academic genealogy from many coun-

tries (Dores et al., 2016). The authors are crawling

data from a variety of sources, including the Net-

worked Digital Library of Theses and Dissertations

(NDLTD), which has more than 4.5 million theses and

dissertations from around the world. They develop a

framework to extract academic genealogy trees from

this data and, providing a series of analyses that de-

scribe the main properties of the academic genealogy

https://academictree.org

http://plataforma-acacia.org

http://lattes.cnpq.br

http://www.sciencetree.net

The Gold Tree: An Information System for Analyzing Academic Genealogy

115

Figure 2: Result of the query “Erik Edlund” using the Academic Family Tree.

Figure 3: Result of the query “Jos

e Moreira Oliveira Palazzo” using the Ac

acia Platform. For each relationship the name,

academic degree and year of conclusion are presented.

Figure 4: Result of the query “Jos

e Moreira Oliveira Palazzo” using the Science Tree.

tree. Figure 4 present the result of a query using the

same researcher of Figure 3.

3 METHODOLOGY

This section presents the methodology adopted in this

article. The proposed approach is divided into the fol-

lowing steps: data source deﬁnition, harvesting and

pre-processing, data modeling and indexing, and web

information system construction.

Unlike the Academic Family Tree related work

presented in the 2.1 section, which is user depen-

dent, we have chosen to collect ofﬁcial data available

in digital libraries. These data sources must support

some interoperability feature such as the OAI-PMH

protocol (Lagoze and Van de Sompel, 2001) and the

Dublin Core

format. This choice solves the problem

of cold start and registration of false information.

In order to evaluate the proposed system we use

the Brazilian Digital Library of Theses and Disser-

tations

(BDTD). Developed and managed by the

Brazilian Institute of Information in Science and

Technology (IBICT), it integrates the repositories of

educational and research institutions in Brazil, and

also stimulates the registration and publication of the-

ses and dissertations electronically. At the time of

our harvesting more than 570,000 documents were in-

dexed.

The main metadata ﬁelds collected were:

• author;

http://dublincore.org

http://bdtd.ibict.br

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116

• advisor;

• name of the educational or research institution;

• acronym of the educational or research institution;

• title;

• topics;

• URL of the document in the original repository;

• PhD thesis or Master’s dissertation;

• URL of the author curriculum in Lattes Plataform;

• citation;

• year of publication.

After the data selection and harvesting, a set of clean-

ing operations were applied. Several analyzes were

performed to identify anomaly patterns, so errors

were corrected or eliminated. Some transformation

operations were applied on the author and advisor

ﬁelds. The most common ones include inverting last

names based on the comma character, removing in-

stitution acronyms, and removing structural preﬁxes

present in the content of a metadata. In addition, du-

plicate tuples from more than one digital repository

were removed. At the end of the cleaning process,

the number of theses and dissertations decreased to

465,847.

In Computer Science, trees are data structures

widely used to represent elements hierarchically or-

ganized. However, the academic genealogy trees are

represented by graphs, because a researcher may have

more than one ascending (one for each dissertation or

thesis) and because there may be cycles. That’s why

we transformed the cleaned data to generate two ta-

bles in the relational model. The ﬁrst table contains

all the academics and their properties. The second

Figure 5: Relational data model representing the genealogy

academic trees.

keeps M:N directed advising relationships between

pairs of academics. Figure 5 shows this data model.

The Database Management System (DBMS) used for

storage was PostgreSQL. All the process of harvest-

ing and pre-processing was developed in the program-

ming language PHP.

The Web application interface was developed us-

ing HTML, CSS and Javascript. We have used the

dagre-d3 library (Roeder, 2018) to draw the aca-

demic trees. The queries are sent to the back-end us-

ing Jquery library (Osmani, 2012). Methods imple-

mented in PHP retrieve the information stored in the

DBMS. Figure 6 shows the data ﬂow between front

and back-end.

4 RESULTS

Figure 7 shows the web interface of the proposed sys-

tem, which is designed to be simple and intuitive. The

button Search Academic opens a query ﬁeld that al-

lows the user to search by the name of the researcher.

The architecture presented in Figure 6 allowed to

implement a dynamic search that suggests multiple

researchers as the user types in the query ﬁeld. For

each character entered, the results are ﬁltered and dis-

played on the screen. All substrings of author and

advisor names with at least 3 characters has been in-

dexed in the DBMS, so the user does not need to

know the full name of a researcher, nor the order of

names or to complete each name. Figure 8 exem-

pliﬁes this behavior while the user are querying by

“avancini mar”.

Next to the button Search Academic the user can

edit the depth level. The tree expands that number

of levels both toward the leaves, and toward the root.

Thus, he or she can see the advising lineage and the

graduated students. Figure 7 shows the result tree se-

lecting “Rita Maria Pereira Avancini” among the ﬁve

returned researches and setting two levels.

Each researcher is represented by a vertex that

contains his or her full name. Master’s advising re-

lationships are edges with M-YEAR labels in blue.

Doctoral advising have D-YEAR labels in red.

Figure 6: Data ﬂow between front and back ends.

The Gold Tree: An Information System for Analyzing Academic Genealogy

117

Figure 7: Web interface of the proposed system showing the result selecting “Rita Maria Pereira Avancini” with 2 levels.

Figure 8: Example of the dynamic search feature, that suggests multiple researchers as the user types in the query ﬁeld.

The user can freely move the tree in the rectangu-

lar area in which it appears and zoom in/out using the

mouse controls. By clicking on a relationship, a win-

dow opens displaying the information available in the

thesis or dissertation metadata (Figure 9). Also, when

you click on a vertex, a new tree is generated using

the selected researcher as the target of the query.

The information system developed is available on-

line

5 CONCLUSION

This work presents a study on tools for visualization

of academic genealogy, besides proposing the devel-

opment of an unprecedented and effective system.

The Gold Tree is based on ofﬁcial information

stored in the Brazilian Digital Library of Theses and

Dissertations. However, the data source chosen can

http://thegoldtree.c3.furg.br/

easily be replaced by one that publishes the metadata

using the Dublin Core standard and the OAI-PHM

protocol.

The information system described in this paper

can assist in research that investigates relationships of

academic genealogy to reach the most diverse objec-

tives. With the user-friendly search interface, users

can analyze and draw conclusions from multiple tree

views.

Compared with related work presented in Section

2, it is clear that the proposed system has several ad-

vantages. We use a reliable data source to map ad-

vising, unlike Academic Family Tree where these re-

lationships are entered by users. In Ac

acia Platform,

although data is extracted from Lattes Platform, it is

not possible to visualize the genealogy trees. Finally,

when compared to Science Tree, our system allows to

build trees with any depth levels, in addition to allow-

ing the dynamic search by the researcher name.

Future work includes the implementation of a rou-

tine for updating the collected data, the incorporation

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Figure 9: Metadata describing the thesis or dissertation presented when clicking on an advising relationship.

of other international data sources, improvements of

interface, the inclusion of bibliometric indexes and

the implementation of the search for other metadata

besides the name of the academic.

In addition, we intend to implement an expert sys-

tem that aims to suggest doctoral advising relation-

ships based on the advisors’ research subjects. The

system will be able to extract several features from

the titles and abstracts of the theses and dissertations

of all the descendants of a researcher to set up the

advising proﬁle. Using multiple machine learning al-

gorithms, a learned proﬁle will be assigned to a doc-

toral candidate with certain conﬁdence to make the

choice of an advisor easier. Combining multiple clas-

siﬁers with high diversity will improve the quality of

the system.

ACKNOWLEDGEMENTS

This research is partially supported by CNPq (Proc.

307781/2016-0 & 305882/2016-3) and FAPERGS

(TO 17/2551-0000872-3). Special thanks to Wash-

ington Lu

ıs R. de Carvalho Segundo, IBICT em-

ployee who helped metadata harvesting.

REFERENCES

Barab

asi, A.-L., Jeong, H., N

eda, Z., Ravasz, E., Schubert,

A., and Vicsek, T. (2002). Evolution of the social

network of scientiﬁc collaborations. Physica A: Sta-

tistical mechanics and its applications, 311(3-4):590–

614.

Batagelj, V. and Mrvar, A. (2002). Pajek – analysis and

visualization of large networks. In Graph Drawing,

pages 477–478, Berlin, Heidelberg. Springer.

Chang, S. (2011). Academic genealogy of mathematicians.

World Scientiﬁc.

Damaceno, R. J. P.; Rossi, L. M.-C. J. P. (2017).

Identiﬁcac¸

ao do grafo de genealogia acad

emica

de pesquisadores: Uma abordagem baseada na

plataforma lattes. In Proceedings of the 32nd Brazil-

ian Symposium on Databases, Uberl

andia.

David, S. V. and Hayden, B. Y. (2012). Neurotree: A collab-

orative, graphical database of the academic genealogy

of neuroscience. PLoS ONE, 7(10):e46608+.

Dores, W., Benevenuto, F., and Laender, A. H. (2016). Ex-

tracting academic genealogy trees from the networked

digital library of theses and dissertations. In Proceed-

ings of the 16th ACM/IEEE-CS on Joint Conference

on Digital Libraries, pages 163–166, New York, NY,

USA. ACM.

Douglas, S. M., Montelione, G. T., and Gerstein, M. (2005).

Pubnet: a ﬂexible system for visualizing literature de-

rived networks. Genome Biology, 6(9).

The Gold Tree: An Information System for Analyzing Academic Genealogy

119

Laender, A., Moro, M., Silva, A., et al. (2011). Ci

encia

brasil-the brazilian portal of science and technol-

ogy. In Semin

ario Integrado de Software e Hard-

ware, pages 1366–1379. Sociedade Brasileira de

Computac¸

ao.

Lagoze, C. and Van de Sompel, H. (2001). The open

archives initiative: Building a low-barrier interop-

erability framework. In Proceedings of the 1st

ACM/IEEE-CS Joint Conference on Digital ibraries,

pages 54–62, New York, NY, USA. ACM.

Malmgren RD, Ottino JM, N. A. L. (2010). The role of

mentorship in prot

e performance. Nature Interna-

cional Journal of Science, 465.

Mena-Chalco, J. P. and Cesar-Jr, R. M. (2013). Prospecc¸

de dados acad

emicos de curr

ıculos Lattes atrav

es de

scriptLattes, chapter Bibliometria e Cientometria: re-

ﬂex

oes te

oricas e interfaces, pages 109–128. Pedro &

ao Editores, S

ao Carlos.

Osmani, A. (2012). Learning JavaScript Design Patterns:

A JavaScript and jQuery Developer’s Guide. O’Reilly

Media, Inc.

Roeder, L. (2018). Dagre-d3.

https://github.com/dagrejs/dagre-d3.

Yu, M.-C., Wu, Y.-C. J., Alhalabi, W., Kao, H.-Y., and Wu,

W.-H. (2016). Researchgate: An effective altmetric

indicator for active researchers? Computers in Human

Behavior, 55:1001–1006.

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

120