An ontology-driven multimedia focused crawler based on linked open data and deep learning techniques

Web-page indexing and classification have been studied extensively starting from the early WWW years. A smart intelligent web agent called focused crawler is a specific software able to seek web pages that are relevant to a particular topic domain. In this article we propose a novel approach to focu...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 79; no. 11-12; pp. 7577 - 7598
Main Authors: Capuano, Andrea, Rinaldi, Antonio M., Russo, Cristiano
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2020
Springer Nature B.V
Subjects:
Artificial neural networks
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Domains
Feature extraction
Ground truth
Image classification
Linked Data
Multimedia
Multimedia Information Systems
Neural networks
Ontology
Open data
Special Purpose and Application-Based Systems
Websites
Knowledge engineering
Multimedia processing
Focused crawling
Document classification
Ontologies
Linked open data
Convolutional neural networks
Document analysis
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:Web-page indexing and classification have been studied extensively starting from the early WWW years. A smart intelligent web agent called focused crawler is a specific software able to seek web pages that are relevant to a particular topic domain. In this article we propose a novel approach to focused crawling based on the use of both textual and multimedia web page content. In our approach we define a novel strategy to choose if a web page should be further explored. We implement our framework in a system which aims to improve the crawling task using semantic based techniques and combining the results with novel technologies like convolutional neural networks and linked open data. Our framework uses ontologies to correlate different topics and understanding their relationships. The correlation among topics is used to improve a textual topic detection step. These results are combined with multimedia analysis and classification based on convolutional neural networks to extract image features. Experimental results are also presented and discussed in order to measure the effectiveness of our framework compared with other approaches using a ground truth composed of web pages about a specific domain.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08252-2