Information Retrieval: A Comparative Study of Textual Indexing Using an Oriented Object Database (db4o) and the Inverted File
The growth in the volume of text data such as books
and articles in libraries for centuries has imposed to establish
effective mechanisms to locate them. Early techniques such as
abstraction, indexing and the use of classification categories have
marked the birth of a new field of research called "Information
Retrieval". Information Retrieval (IR) can be defined as the task of
defining models and systems whose purpose is to facilitate access to
a set of documents in electronic form (corpus) to allow a user to find
the relevant ones for him, that is to say, the contents which matches
with the information needs of the user.
Most of the models of information retrieval use a specific data
structure to index a corpus which is called "inverted file" or "reverse
index".
This inverted file collects information on all terms over the corpus
documents specifying the identifiers of documents that contain the
term in question, the frequency of each term in the documents of the
corpus, the positions of the occurrences of the word...
In this paper we use an oriented object database (db4o) instead of
the inverted file, that is to say, instead to search a term in the inverted
file, we will search it in the db4o database.
The purpose of this work is to make a comparative study to see if
the oriented object databases may be competing for the inverse index
in terms of access speed and resource consumption using a large
volume of data.
[1] Ricardo B Y., Berthier R N. Modern information retrieval, ACM
(Association for Computing Machinery).
[2] Baziz, M. (2005). Indexation conceptuelle guidée par ontologie pour la
recherche d'information (Doctoral dissertation, Toulouse 3).
[3] Mooers, C. N. (1948). Application of random codes to the gathering of
statistical information (Doctoral dissertation, Massachusetts Institute of
Technology).
[4] Karbasi, S. Pondération des termes en Recherche d’Information
(Doctoral dissertation, Toulouse 3).
[5] Harrathi, F. (2009). Extraction de concepts et de relations entre concepts
à partir des documents multilingues: approche statistique et ontologique.
[6] Salton, G. (1969). A comparison between manual and automatic
indexing methods. American Documentation, 20(1), 61-71.
[7] Mallak, I. (2011). De nouveaux facteurs pour l'exploitation de la
sémantique d'un texte en Recherche d'Information (Doctoral dissertation,
Université Paul Sabatier-Toulouse III).
[8] Aouicha, M. B. (2009). Une approche algébrique pour la recherche
d'information structurée (Doctoral dissertation).
[9] Barry, C. L. (1994). User-defined relevance criteria: an exploratory
study.JASIS, 45(3), 149-159.
[10] Boubekeur-Amirouche, F. (2008). Contribution à la définition de
modèles de recherche d'information flexibles basés sur les CP-Nets
(Doctoral dissertation, Université de Toulouse, Université Toulouse IIIPaul
Sabatier).
[11] Roussey, C. (2001). Une méthode d’indexation sémantique adaptée aux
corpus multilingues. Institut National des Sciences Appliquées de Lyon
Lyon, Ecole Doctorale Informatique et Information pour la Société.
[12] Azzoug, W. (2014). Contribution à la définition d’une approche
d’indexation sémantique de documents textuels.
[13] Porter, M. F. (1980). An algorithm for suffix stripping. Program:
electronic library and information systems, 14(3), 130-137.
[14] Buckley, C., Singhal, A., Mitra, M., & Salton, G. (1995, November).
New retrieval approaches using SMART: TREC 4. In Proceedings of the
Fourth Text REtrieval Conference (TREC-4) (pp. 25-48).
[15] Brini, A. H. (2005). Un modèle de recherche d'information basé sur les
réseaux possibilistes (Doctoral dissertation, Toulouse 3).
[16] Maron, M. E., & Kuhns, J. L. (1960). On relevance, probabilistic
indexing and information retrieval. Journal of the ACM (JACM), 7(3),
216-244.
[17] Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining
association rules between sets of items in large databases. In ACM
SIGMOD Record (Vol. 22, No. 2, pp. 207-216). ACM.
[18] Tebri H. Formalisation et spécification d’un système de filtrage
incrémental d’information. Thèse de doctorat de l’université Paul
Sabatier, Toulouse, 2004.
[19] V.Rijsbergen C. J. Information Retrieval. Department of Computing
Science University of Glasgow.
[20] Iadh O. Un modèle d'indexation relationnel pour les graphes conceptuels
fondé sur une interprétation logique, Thèse pour obtenir le grade de
Docteur de l'Université Joseph Fourier, 1992.
[21] Piwowarski B, Denoyer L, Gallinari P. Un modèle pour la recherche
d’information sur des documents structurés. 6es Journées internationales
d’Analyse statistique des Données Textuelles. LIP6, PARIS – France,
2002.
[22] Denos N. Modélisation de la pertinence en recherche d'information :
modèle conceptuel, formalisation et application. Thèse pour obtenir le
grade de Docteur de l'Université Joseph Fourier-Grenoble I, 1997.
[23] http://www.comp.lancs.ac.uk/computing/research/stemming/Links/lovin
s.htm
[24] http://www.comp.lancs.ac.uk/computing/research/stemming/Links/paice
.htm
[25] http://tartarus.org/martin/PorterStemmer/
[26] http://snowball.tartarus.org/
[1] Ricardo B Y., Berthier R N. Modern information retrieval, ACM
(Association for Computing Machinery).
[2] Baziz, M. (2005). Indexation conceptuelle guidée par ontologie pour la
recherche d'information (Doctoral dissertation, Toulouse 3).
[3] Mooers, C. N. (1948). Application of random codes to the gathering of
statistical information (Doctoral dissertation, Massachusetts Institute of
Technology).
[4] Karbasi, S. Pondération des termes en Recherche d’Information
(Doctoral dissertation, Toulouse 3).
[5] Harrathi, F. (2009). Extraction de concepts et de relations entre concepts
à partir des documents multilingues: approche statistique et ontologique.
[6] Salton, G. (1969). A comparison between manual and automatic
indexing methods. American Documentation, 20(1), 61-71.
[7] Mallak, I. (2011). De nouveaux facteurs pour l'exploitation de la
sémantique d'un texte en Recherche d'Information (Doctoral dissertation,
Université Paul Sabatier-Toulouse III).
[8] Aouicha, M. B. (2009). Une approche algébrique pour la recherche
d'information structurée (Doctoral dissertation).
[9] Barry, C. L. (1994). User-defined relevance criteria: an exploratory
study.JASIS, 45(3), 149-159.
[10] Boubekeur-Amirouche, F. (2008). Contribution à la définition de
modèles de recherche d'information flexibles basés sur les CP-Nets
(Doctoral dissertation, Université de Toulouse, Université Toulouse IIIPaul
Sabatier).
[11] Roussey, C. (2001). Une méthode d’indexation sémantique adaptée aux
corpus multilingues. Institut National des Sciences Appliquées de Lyon
Lyon, Ecole Doctorale Informatique et Information pour la Société.
[12] Azzoug, W. (2014). Contribution à la définition d’une approche
d’indexation sémantique de documents textuels.
[13] Porter, M. F. (1980). An algorithm for suffix stripping. Program:
electronic library and information systems, 14(3), 130-137.
[14] Buckley, C., Singhal, A., Mitra, M., & Salton, G. (1995, November).
New retrieval approaches using SMART: TREC 4. In Proceedings of the
Fourth Text REtrieval Conference (TREC-4) (pp. 25-48).
[15] Brini, A. H. (2005). Un modèle de recherche d'information basé sur les
réseaux possibilistes (Doctoral dissertation, Toulouse 3).
[16] Maron, M. E., & Kuhns, J. L. (1960). On relevance, probabilistic
indexing and information retrieval. Journal of the ACM (JACM), 7(3),
216-244.
[17] Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining
association rules between sets of items in large databases. In ACM
SIGMOD Record (Vol. 22, No. 2, pp. 207-216). ACM.
[18] Tebri H. Formalisation et spécification d’un système de filtrage
incrémental d’information. Thèse de doctorat de l’université Paul
Sabatier, Toulouse, 2004.
[19] V.Rijsbergen C. J. Information Retrieval. Department of Computing
Science University of Glasgow.
[20] Iadh O. Un modèle d'indexation relationnel pour les graphes conceptuels
fondé sur une interprétation logique, Thèse pour obtenir le grade de
Docteur de l'Université Joseph Fourier, 1992.
[21] Piwowarski B, Denoyer L, Gallinari P. Un modèle pour la recherche
d’information sur des documents structurés. 6es Journées internationales
d’Analyse statistique des Données Textuelles. LIP6, PARIS – France,
2002.
[22] Denos N. Modélisation de la pertinence en recherche d'information :
modèle conceptuel, formalisation et application. Thèse pour obtenir le
grade de Docteur de l'Université Joseph Fourier-Grenoble I, 1997.
[23] http://www.comp.lancs.ac.uk/computing/research/stemming/Links/lovin
s.htm
[24] http://www.comp.lancs.ac.uk/computing/research/stemming/Links/paice
.htm
[25] http://tartarus.org/martin/PorterStemmer/
[26] http://snowball.tartarus.org/
@article{"International Journal of Information, Control and Computer Sciences:68724", author = "Mohammed Erritali", title = "Information Retrieval: A Comparative Study of Textual Indexing Using an Oriented Object Database (db4o) and the Inverted File", abstract = "The growth in the volume of text data such as books
and articles in libraries for centuries has imposed to establish
effective mechanisms to locate them. Early techniques such as
abstraction, indexing and the use of classification categories have
marked the birth of a new field of research called "Information
Retrieval". Information Retrieval (IR) can be defined as the task of
defining models and systems whose purpose is to facilitate access to
a set of documents in electronic form (corpus) to allow a user to find
the relevant ones for him, that is to say, the contents which matches
with the information needs of the user.
Most of the models of information retrieval use a specific data
structure to index a corpus which is called "inverted file" or "reverse
index".
This inverted file collects information on all terms over the corpus
documents specifying the identifiers of documents that contain the
term in question, the frequency of each term in the documents of the
corpus, the positions of the occurrences of the word...
In this paper we use an oriented object database (db4o) instead of
the inverted file, that is to say, instead to search a term in the inverted
file, we will search it in the db4o database.
The purpose of this work is to make a comparative study to see if
the oriented object databases may be competing for the inverse index
in terms of access speed and resource consumption using a large
volume of data.
", keywords = "Information Retrieval, indexation, oriented object
database (db4o), inverted file.", volume = "8", number = "11", pages = "2038-6", }
