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JCR Impact Factor: 0.800
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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  4/2013 - 5

 HIGHLY CITED PAPER 

Post-processing of Deep Web Information Extraction Based on Domain Ontology

LIU, L. See more information about LIU, L. on SCOPUS See more information about LIU, L. on IEEExplore See more information about LIU, L. on Web of Science, PENG, T. See more information about PENG, T. on SCOPUS See more information about PENG, T. on SCOPUS See more information about PENG, T. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (793 KB) | Citation | Downloads: 835 | Views: 3,363

Author keywords
knowledge based systems, machine learning, semantic web, web mining, World Wide Web

References keywords
information(9), systems(8), data(8), search(5), meng(5), extraction(5), automatic(5), wise(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-11-30
Volume 13, Issue 4, Year 2013, On page(s): 25 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.04005
Web of Science Accession Number: 000331461300005
SCOPUS ID: 84890180257

Abstract
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Many methods are utilized to extract and process query results in deep Web, which rely on the different structures of Web pages and various designing modes of databases. However, some semantic meanings and relations are ignored. So, in this paper, we present an approach for post-processing deep Web query results based on domain ontology which can utilize the semantic meanings and relations. A block identification model (BIM) based on node similarity is defined to extract data blocks that are relevant to specific domain after reducing noisy nodes. Feature vector of domain books is obtained by result set extraction model (RSEM) based on vector space model (VSM). RSEM, in combination with BIM, builds the domain ontology on books which can not only remove the limit of Web page structures when extracting data information, but also make use of semantic meanings of domain ontology. After extracting basic information of Web pages, a ranking algorithm is adopted to offer an ordered list of data records to users. Experimental results show that BIM and RSEM extract data blocks and build domain ontology accurately. In addition, relevant data records and basic information are extracted and ranked. The performances precision and recall show that our proposed method is feasible and efficient.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 5 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] Prediction of users webpage access behaviour using association rule mining, GEETHARAMANI, R, REVATHY, P, JACOB, SHOMONA G, Sadhana, ISSN 0256-2499, Issue 8, Volume 40, 2015.
Digital Object Identifier: 10.1007/s12046-015-0424-0
[CrossRef]

[2] Hybrid Recommendation System using Particle Swarm Optimization and User Access Based Ranking, Sumathi, G., Sendhilkumar, S., Mahalakshmi, G. S., Proceedings of the International Conference on Informatics and Analytics, ISBN 9781450347563, 2016.
Digital Object Identifier: 10.1145/2980258.2980405
[CrossRef]

Updated 3 days, 8 hours ago

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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