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JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: Nov 2020
Next issue: Feb 2021
Avg review time: 58 days


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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2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

2020-Jun-11
Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

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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
 
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: 484 | Views: 2,502

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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Full text preview
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  «-- Click to see who has cited this paper

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[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[2] C. H. Chang, M. Kayed, M. R. Girgis, and K. F. Shaalan, "A survey of Web information extraction systems," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1411-1428, 2006.
[CrossRef] [SCOPUS Times Cited 556]


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[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 31]


[6] H. He, W. Meng, C.T. Yu, and Z. Wu. "WISE-integrator: an automatic integrator of web search interfaces for e-commerce," In Proceedings of the 29th International Conference on Very Large Data Bases(VLDB), Berlin, pp: 357-368, 2003. [PubMed]

[7] H. He, W.Y. Meng. C. Yu, and Z.H. Wu, "Constructing interface schemas for search interfaces of web databases," In Proceedings of WISE, pp: 29-42, 2005. [PubMed]

[8] X. Peng and Z. Huang, "Enabling semantic queries against the spatial database," Advances in Electrical and Engineering, Vol. 12, no. 1, pp. 45-50, 2012.
[CrossRef] [Full Text] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[9] R.B. Doorenbos, O. Etzioni, and D. Weld, "A scalable comparison shopping agent for the World Wide Web, " Proc of the First International Conference on Autonomous Agents, Marina del Rey, CA, pp. 39-48, 1997.
[CrossRef]


[10] L. Gravano, P.G. Ipeirotis, and M. Sahami, "QProbe: a system for automatic classification of hidden-Web databases,"ACM Transactions on Information Systems, vol. 21, no. 1, pp. 1-41, 2003.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 90]


[11] F. Ashraf, T. Ozyer, and R. Alhajj, "Employing clustering techniques for automatic information extraction from HTML documents," IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Review, vol. 38, no. 5, 2008.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 26]


[12] B. Liu and Y.H. Zhai, "NET-a system for extracting web data from flat and nested data records," Web Information Systems Engineering-WISE 2005, Lecture Notes in Computer Science, vol.3806, pp. 487-495, 2005.
[CrossRef] [SCOPUS Times Cited 48]


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[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 29]


[14] N. Marian and S. Top, "Integration of simulink models with component-based software model," Advances in Electrical and Computer Engineering, Vol. 8, no. 2, pp. 3-10, 2008.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 4]


[15] L. Stanescu and D. Burdescu, "Information structuring and retrieval with topic maps for medical e-learning," Advances in Electrical and Computer Engineering, Vol. 9, no. 3, pp. 27-33, 2009.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[16] S. Slderland, "Learning information extraction rules for semi-structured and free text," Machine Learning, vol. 34, nos. 1-3, pp. 233-272, 1999.
[CrossRef] [Web of Science Times Cited 369] [SCOPUS Times Cited 623]


[17] V. Crescenzi, G. Mecca, and P. Merialdo, "RoadRunner: towards automatic data extraction form large Web site," Proceeding of the 27th. International Conference on Very Large Data Bases, Roma, pp. 109-118, 2001.

[18] D. Cai, S.P. Yu, J.R. Wen, and W.Y. Ma, "VIPS: a vision-based page segmentation algorithm," Microsoft Technical Report, MSR-TR 2003-79.

[19] H. Zhao, W. Meng, Z. Wu, V. Raghavan, and C. Yu, " Fully automatic wrapper generation for search engines," In Proceedings of the 14th World Wide Web Conference, pp. 66-75, 2005.
[CrossRef]


[20] Z.P. Wang, Y.G. Zhang, J.F. Zhang, and J. Ma, "Recent research process in fault analysis of complex electric power systems," Advances in Electrical and Computer Engineering, Vol. 10, no. 1, pp. 28-33, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 18] [SCOPUS Times Cited 22]


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[25] Y. Y., Lu, W. Y. Meng, L. C. Shu, C. Yu, and K. L. Liu, "Evaluation of result merging strategies for metasearch engines," 6th International Conference on Web Information Systems Engineering(WISE05), New York City, pp. 53-66, November 2005.



References Weight

Web of Science® Citations for all references: 491 TCR
SCOPUS® Citations for all references: 1,613 TCR

Web of Science® Average Citations per reference: 19 ACR
SCOPUS® Average Citations per reference: 62 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-01-20 21:28 in 105 seconds.




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


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