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CLC number: TP39

On-line Access: 2022-08-22

Received: 2021-07-23

Revision Accepted: 2022-03-23

Crosschecked: 2022-08-29

Cited: 0

Clicked: 1748

Citations:  Bibtex RefMan EndNote GB/T7714


Jingfa LIU


Fan LI


Ruoyao DING


Zi’ang LIU


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.8 P.1189-1204


Focused crawling strategies based on ontologies and simulated annealing methods for rainstorm disaster domain knowledge

Author(s):  Jingfa LIU, Fan LI, Ruoyao DING, Zi’ang LIU

Affiliation(s):  Guangzhou Key Laboratory of Multilingual Intelligent Processing, Guangdong University of Foreign Studies, Guangzhou 510006, China; more

Corresponding email(s):   jfliu@nuist.edu.cn, bj2014_lifan@163.com

Key Words:  Focused crawler, Ontology, Priority evaluation, Simulated annealing, Rainstorm disaster

Jingfa LIU, Fan LI, Ruoyao DING, Zi’ang LIU. Focused crawling strategies based on ontologies and simulated annealing methods for rainstorm disaster domain knowledge[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1189-1204.

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publisher="Zhejiang University Press & Springer",

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%T Focused crawling strategies based on ontologies and simulated annealing methods for rainstorm disaster domain knowledge
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%A Ruoyao DING
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%DOI 10.1631/FITEE.2100360

T1 - Focused crawling strategies based on ontologies and simulated annealing methods for rainstorm disaster domain knowledge
A1 - Jingfa LIU
A1 - Fan LI
A1 - Ruoyao DING
A1 - Zi’ang LIU
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2100360

At present, focused crawler is a crucial method for obtaining effective domain knowledge from massive heterogeneous networks. For most current focused crawling technologies, there are some difficulties in obtaining high-quality crawling results. The main difficulties are the establishment of topic benchmark models, the assessment of topic relevance of hyperlinks, and the design of crawling strategies. In this paper, we use domain ontology to build a topic benchmark model for a specific topic, and propose a novel multiple-filtering strategy based on local ontology and global ontology (MFSLG). A comprehensive priority evaluation method (CPEM) based on the web text and link structure is introduced to improve the computation precision of topic relevance for unvisited hyperlinks, and a simulated annealing (SA) method is used to avoid the focused crawler falling into local optima of the search. By incorporating SA into the focused crawler with MFSLG and CPEM for the first time, two novel focused crawler strategies based on ontology and SA (FCOSA), including FCOSA with only global ontology (FCOSA_G) and FCOSA with both local ontology and global ontology (FCOSA_LG), are proposed to obtain topic-relevant webpages about rainstorm disasters from the network. Experimental results show that the proposed crawlers outperform the other focused crawling strategies on different performance metric indices.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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