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Post-processing of Deep Web Information Extraction Based on Domain OntologyLIU, L. , PENG, T. |
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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
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. |
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[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]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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