Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.stecf.org/conferences/adass/adassVII/reprints/egretd2.ps.gz Äàòà èçìåíåíèÿ: Mon Jun 12 18:51:45 2006 Äàòà èíäåêñèðîâàíèÿ: Tue Oct 2 03:26:31 2012 Êîäèðîâêà: IBM-866 Ïîèñêîâûå ñëîâà: m 8

Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
ó Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
Information Mining in Astronomical Literature with
Tetralogie
D. Egret
CDS, Strasbourg, France, Eímail: egret@astro.uístrasbg.fr
J. Mothe 1 , T. Dkaki 2 and B. Dousset
Research Institute in Computer Sciences, IRIT, SIG, 31062 Toulouse
Cedex, France, Eímail: {mothe/dkaki/dousset}@irit.fr
Abstract. A tool derived for the technological watch (Tetralogie 3 ) is
applied to a dataset of three consecutive years of article abstracts pubí
lished in the European Journal Astronomy and Astrophysics. This tool
is based on a two step approach: first, a pretreatment step that extracts
elementary items from the raw information (keywords, authors, year of
publication, etc.); second, a mining step that analyses the extracted iní
formation using statistical methods. It is shown that this approach allows
one to qualify and visualize some major trends characterizing the current
astronomical literature: multiíauthor collaborative work, the impact of
observational projects and thematic maps of publishing authors.
1. Introduction
Electronic publication has become an essential aspect of the distribution of así
tronomical results (see Heck 1997). The users who want to exploit this informaí
tion need e#cient information retrieval systems in order to retrieve relevant raw
information or to extract hidden information and synthesize thematic trends.
The tools providing the former functionalities are based on query and document
matching (Salton et al. 1983; Frakes et al. 1992; Eichhorn et al. 1997). The latí
ter functionalities (called data mining functionalities) result from data analysis,
data evolution analysis and data correlation principles and allow one to discover
a priori unknown knowledge or information (Shapiro et al. 1996). We focus on
these latter functionalities.
A knowledge discovery process can be broken down into two steps: first,
the data or information selection and preítreatment; second, the mining of these
pieces of information in order to extract hidden information. The main objecí
tives of the mining are to achieve classification (i.e., finding a partition of the
1 Institut Universitaire de Formation des MaÓÐtres de Toulouse
2 IUT StrasbourgíSud, UniversitÒe Robert Schuman, France
3 http://atlas.irit.fr
461

462 Egret, Mothe, Dkaki and Dousset
data, using a rule deduced from the data characteristics), association (one tries
to find data correlations) and sequences (the objective is to identify and to find
the temporal relationships between the data). The information resulting from
an analysis have then to be presented to the user in the most synthetic and
expressive way, including graphical representation.
Tetralogie 4 is an information mining tool that has been developed at the
Institut de Recherche en Informatique de Toulouse (IRIT). It is used for science
and technology monitoring (Dousset et al. 1995; Chrisment et al. 1997) from
document collections. In this paper it is not possible to present in detail all the
system functionalities. We will rather focus on some key features and on an
example of the results that can be obtained from astronomical records.
2. Information Mining using ``TÒetralogie''
The information mining process is widely based on statistical methods and more
precisely on data analysis methods. First, the raw information have to be seí
lected and preítreated in order to extract the relevant elements of information
and to store them in an appropriate form.
2.1. Information Harvesting and Preítreatment
This step includes the selection of relevant raw information according to the
user's needs. It is generally achieved by querying a specific database or a set
of databases. Once the raw information has been selected, the next phase is to
extract the relevant pieces of information : e.g., authors, year of publication,
a#liations, keywords or main topics of the paper, etc. This is achieved using
the ``rewriting rule principle''. In addition, the feature values can be filtered
(e.g., publications written by authors from selected countries) and semantically
treated (dictionaries are used to solve synonymy problems). These relevant
feature values are stored in contingency and disjunctive tables.
Di#erent kinds of crossing tables can be performed according to the kind of
information one wants to discover, for example:
Kind of crossing Expected discovering
(authors name, authors name) Multiíauthor collaborative work
(authors name, document topics) Thematic map of publishing authors
(document topics, authors a#liation) Geographic map of the topics
2.2. Data Analysis and Graphical Representation
The preprocessed data from the previous step are directly usable by the impleí
mented mining methods. These methods are founded on statistical fundamení
tals (see e.g., Benzecri 1973; Murtagh & Heck 1989) and their aim is either to
represent the preítreated information in a reduced space, or to classify them.
The di#erent mining functions used are described in Chrisment et al. (1997).
They include: Principal component analysis (PCA), Correspondence Factorial
4 This project is supported by the French defense ministry and the Conseil RÒegional de la Haute
Garonne.

Information Mining in Astronomical Literature with TETRALOGIE 463
Analysis (CFA), Hierarchical Ascendant Classification, Classification by Partií
tion and Procustean Analysis.
The Result Visualization The information mining result is a set of points in
a reduced space. Tools are proposed for displaying this information in a four
dimensional space, and for changing the visualized space, or the point of view
(zoom, scanning of the set of points).
The User Role In addition to be a real actor in the information harvesting
phase, the user has to intervene in the mining process itself: elimination of some
irrelevant data or already studied data, selection of a data subset to analyze it
deeper, choice of a mining function, and so on.
3. Application to a Dataset from the Astronomical Literature
3.1. The Information Collection
The information collection used for the analysis was composed of about 3600 abí
stracts published in the European Journal Astronomy and Astrophysics (years
1994 to 96). Note that this dataset is therefore mainly representative of Euroí
pean contributions to astronomy, in the few recent years.
In that abstract sample, it is possible to extract about 1600 di#erent auí
thors, and 200 can be selected as the most prolific. Topics of the documents can
be extracted either from the title, from the keywords or from the abstract field.
Titles have been considered as too short to be really interesting for the study.
In addition, the use of keywords was considered too restrictive, as they belong
to a controlled set. Indeed, we preferred to automatically extract the di#erent
topics from the words or series of words contained in the abstracts.
3.2. Study of the Collaborative Work
The details concerning the collaborative works can be discovered using (author
name / author name) crossing and analyzing it.
The first crossing was done using all the authors. A first view is obtained
by sorting the author correlations in order to find the strong connexities. The
resulting connexity table shows strongly related groups (about 15 groups appear
on the diagonal). They are almost all weakly linked via at least one common
author (see the several points above and below the diagonal line, linking the
blocks). The isolated groups appear on the bottom right corner. This strong
connexity is typical of a scientific domain including large international projects
and strong cooperative links.
One can go further and study in depth one of these collaborative groups: a
CFA of the (main author / author) crossing shows, for instance, some features of
the collaborative work around the Hipparcos project in the years 1994í96 (i.e.,
before the publication of the final catalogues) as can be viewed by grouping toí
gether authors having papers coíauthored with M. Perryman (Hipparcos project
scientist) and L. Lindegren (leader of one of the scientific consortia). The system
allows the extraction of 25 main authors (with more than two publications, and
at least one with one of the selected central authors) and the crossíreferencing
of them with all possible coíauthors.

464 Egret, Mothe, Dkaki and Dousset
3.3. Thematic Maps of Publishing Authors
The details concerning the thematic map of publishing authors can be discovered
using (author name / topic) crossing and analyzing it. The significant words in
the abstracts have been automatically extracted during the first stage (see 2.1)
and they are crossed with the main authors. That kind of crossing allows one
to discover the main topics related to one or several authors ; it can also show
what are the keywords that link several authors or that are shared by several
authors.
For instance, crossing main authors of the `Hipparcos' collaboration with
topical key words, allowed us to discover the main keywords of the `peripheral'
authors (those who bring specific outside collaborations).
4. Conclusion
In this paper, we have tried to show the usefulness of the TETRALOGIE system
for discovering trends in the astronomical literature. We focused on several
functionalities of this tool that allow one to find some hidden information such
as the teams (through the multiíauthor collaborative work) or the topical maps
of publishing authors. This tool graphically displays the discovered relationships
that may exist among the extracted information.
Schulman et al. 1997, using classical statistical approaches, have extracted
significant features from an analysis of subsequent years of astronomy literature.
In a forthcoming study, we will show how the Tetralogie system can also be used
to discover thematic evolutions in the literature over several years.
The Web version 5 contains additional figures for illustration.
References
Benzecri, J.P. 1973, L'analyse de donnÒees, Tome 1 et 2, Dunod Edition
Chrisment, C., Dkaki, T., Dousset, & B., Mothe, J. 1997, ISI vol. 5, 3, 367
(ISSN 1247í0317)
Dousset, B., Rommens, M., & Sibue, D. 1995, Symposium International, Omegaí
3, LipoprotÒeines et atherosclerose
Eichhorn, G., et al. 1997, in ASP Conf. Ser., Vol. 125, Astronomical Data Analí
ysis Software and Systems VI, ed. Gareth Hunt & H. E. Payne (San
Francisco: ASP), 569
Frakes et al. 1992, Information retrieval, Algorithms and structure (ISBN 0í13í
463837í9)
Heck, A. 1997, ``Electronic Publishing for Physics and Astronomy'', Astrophys.
Space Science 247, Kluwer, Dordrecht (ISBN 0í7923í4820í6)
Murtagh, F., & Heck, A. 1989, Knowledgeíbased systems in astronomy, Lecture
Notes in Physics 329, SpringeríVerlag, Heidelberg (ISBN 3í540í51044í3)
5 http://cdsweb.uístrasbg.fr/publi/tetraí1.htx

Information Mining in Astronomical Literature with TETRALOGIE 465
Salton, G., et al. 1983, Introduction to modern retrieval, McGraw Hill Internaí
tional (ISBN 0í07í66526í5)
Shapiro et al. 1996, Advances in Knowledge discovery and Data Mining, AAAI
Press (ISBN 0í262í56097í6)
Schulman, E., et al. 1997, PASP 109, 741