Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Semantic Web Mining using RDF Data

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
V. A. Chakkarwar, Amruta A. Joshi

V A Chakkarwar and Amruta A Joshi. Article: Semantic Web Mining using RDF Data. International Journal of Computer Applications 133(10):14-19, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {V. A. Chakkarwar and Amruta A. Joshi},
	title = {Article: Semantic Web Mining using RDF Data},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {10},
	pages = {14-19},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Information on the web is increasing every minute. Redundancy in information is growing rapidly. Data mining is the technique used to extract this data as per the user’s query. Technically data mining analyzing and summarizing it into useful information. Keyword search is an important tool for exploring and searching large data corpuses whose structure is either unknown, or constantly changing. So, keyword search has already been studied in the context of relational databases XML documents and more recently over graphs and RDF data. Semantic web mining aims to combine semantic web and web mining. Semantic web mining is the need of today’s redundant data. In this paper major focus is on minimizing extraction of number of pages by ranking technique. Due to which the extraction of information is done exact as query fired and the top ranked pages are shown to user. Here for this three main areas are going to use such as semantic web, ontology and RDF data.


  1. V. Crescenzi, G.Mecca and P. Merialdo, “RoadRunner: Towards automatic data extraction from large web sites” In VLDB 2001, Proceedings of 27th International Conference on Very Large Data Bases, Roma, Italy, pages 109–118. Morgan Kaufman, Sept. 1.
  2. D.Anglunin, “Inference of Reversible Languages”, J. ACM 29(1982) 741-765.
  3. D Anglunin, “On the complexity of minimum inference of regular sets” Inform Control 39 (1978) 337-350.
  4. S.Madria, S.Bhowmick, S.Sourav, W.K.Ng & E.M.Lim “Research Issues in Web Data Mining”, CiteseerX Beeta,1999,p.4-12.
  5. A.J Gerber, A.Barnard, A.J Van der Merwe“Towards a Semantic Web Layered Architecture”
  6. T.Berners-Lee, J.Hendler and O. Lassila “The Semantic Web,” Scientific American. 284(5):35-43, 2001.
  7. P.DuPont “Regular Grammatical Inference from positive and negative samples by genetic search” the GIG method, In Proceedings of second International Colloquium.
  8. C.N. Hsu and M.T. Dung “Generating finite-state transducers for semi-structured data extraction from the web” Information Systems, 23(8):521–538, 1998.
  9. Lars Marius Garshol (2004) Metadata? Thesauri? Taxonomies? Topic Maps! Making sense of it all on 13 October 2008.
  10. A. Maedche, S. Stabb (2001) “Ontology Learning for the Semantic Web” IEEE intelligent Systems, Special Issue on the semantic Web, 16(2).
  11. R.Studer, V. Benjamins & D.Fensel “Knowledge engineering, principles and methods” Data and Knowledge Engineering 25 (1998) 161–197.
  12. Gruber Tom (1993): "A translation approach to portable ontology specifications". In: Knowledge Acquisition. 5: 199.
  13. A. H. F. Laender, B. A. Ribeiro-Neto, A. S. daSilva, and J. S. Teixeira: “A brief survey of web data extraction tools.” SIGMOD Rec., 31(2):84–93, 2002
  14. E.M Gold “Complexity of automaton identification from given data”, Inform Control 37(1978) 337-350.
  15. T. Mitchell (1997): Machine Learning, McGraw Hill. ISBN 0-07-042807-7.
  16. amruta arun joshi, Prof. V.A. Chakkarwar, "A review on Semantic web mining", IJCSIT, Vol 5 (1), pp 431-433, 2015, ISSN No. 0975-9646.


Semantic web, ontology, RDF, XML.