Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloA Concurrent Neural Classifier for HTML Documents Retrieval
Anno di pubblicazione2003
Formato-
Autore/iConti Vincenzo, Pilato Giovanni, Sorbello Filippo, Vassallo Giorgio, Vitabile Salvatore
Affiliazioni autori1- ICAR-CNR; 2- Centro di Ricerche Elettroniche in Sicilia; 3- Dipartimento di ingegneria Informatica, University of Palermo
Autori CNR e affiliazioni
  • SALVATORE VITABILE
  • GIOVANNI PILATO
Lingua/e-
AbstractA neural based multi-agent system for automatic HTML pages retrieval is presented. The system is based on the E-alpha-Net architecture, a neural network able to learn the activation function of its hidden units and having good generalization capabilities. The starting hypothesis is that the HTML pages are stored in networked repositories. The system goal is to retrieve documents satisfying a user query and belonging to a given class (i.e. documents containing the word “football” and talking about “Sports”). The system has been implemented using the Jade platform and it is composed by three agents: the E-alpha-Net Neural Classifier Agent, the Query Agent, and the Locator Agent. The system is very efficient: the preliminary experimental results show that in the best case a classification error of 9.98% is obtained.
Lingua abstract-
Altro abstract-
Lingua altro abstract-
Pagine da210
Pagine a217
Pagine totali-
RivistaLecture notes in computer science
Attiva dal 1973
Editore: Springer - Berlin
Paese di pubblicazione: Germania
Lingua: multilingue
ISSN: 0302-9743
Titolo chiave: Lecture notes in computer science
Titolo proprio: Lecture notes in computer science.
Titolo abbreviato: Lect. notes comput. sci.
Titoli alternativi:
  • Lecture notes in computer science. Lecture notes in artificial intelligence
  • Lecture notes in artificial intelligence
  • LNCS. Lecture notes in computer science (Print)
  • Lecture notes in computer science (Print)
  • Lecture notes in computer science. LNAI. Lecture notes in artificial intelligence
  • Lecture notes in computer science. Lecture notes in bioinformatics (Print)
  • Lecture notes in computer science. Journal subline
Numero volume della rivista2859-
Fascicolo della rivista-
DOI-
Verificato da referee-
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)-
Parole chiaveNeural Networks, Intelligent Data Analysis, Web Mining, Information Retrieval, Multi Agent Systems
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ICAR — Istituto di calcolo e reti ad alte prestazioni
  • ICAR — ICAR - Sede secondaria di Palermo
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati

Dati storici
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Area disciplinareComputer Science & Engineering
Area valutazione CIVRIngegneria industriale e informatica
Rivista ISILECTURE NOTES IN COMPUTER SCIENCE [00538S0]
Descrizione sintetica del prodottoWith the recent increasing of digital libraries it has grown the necessity of designing automatic systems for information retrieval. In this paper a system, based on both neural networks and multi-agent paradigm, for automatic concurrent retrieval of HTML pages, has been presented. The system is composed by three agents: the Query Agent, the Locator Agent and the EaNet Classifier Mobile Agent. The proposed system has been implemented exploiting the features and facilities of the Jade platform, a multi-agent FIPA specification compliant platform. At system start-up, an autonomous process for EaNet neural network training starts. The trained neural network is successively embedded into the EaNet Neural Classifier Mobile Agent. The user interacts, through the Query Agent, with the system in order to retrieve documents satisfying a query and belonging to a given class. The EaNet Neural Classifier Mobile Agent receives the request and clones itself in the document repositories registered in the platform. Each clone interacts with the Locator Agent, present in each repository, in order to find the location of documents to be classified. After the classification task, each clone sends the results to the Query agent that shows them to the user.