CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Knowledge based Recommendation System in Semantic Web - A Survey

by Ayesha Ameen
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 43
Year of Publication: 2019
Authors: Ayesha Ameen
10.5120/ijca2019918538

Ayesha Ameen . Knowledge based Recommendation System in Semantic Web - A Survey. International Journal of Computer Applications. 182, 43 ( Mar 2019), 20-25. DOI=10.5120/ijca2019918538

@article{ 10.5120/ijca2019918538,
author = { Ayesha Ameen },
title = { Knowledge based Recommendation System in Semantic Web - A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2019 },
volume = { 182 },
number = { 43 },
month = { Mar },
year = { 2019 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number43/30437-2019918538/ },
doi = { 10.5120/ijca2019918538 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:14:08.917659+05:30
%A Ayesha Ameen
%T Knowledge based Recommendation System in Semantic Web - A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 43
%P 20-25
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Knowledge based recommendation systems use knowledge about users and products to make recommendations. Knowledge-based recommendations are not dependent on the rating, nor do they have to gather information about a particular user to give recommendations. Knowledge acquisition is the most important task for constructing knowledge-based recommendation system. Acquired knowledge must be represented in some structured machine-readable form, e.g., as ontology to support reasoning about what products meets the user’s requirements. In Semantic Web, knowledge is represented in the form of ontology. Representation of knowledge in structured form of ontology in Semantic Web makes the application of knowledge based recommendations system on Semantic Web very easy, as there is no need to construct knowledge base from scratch. Performance of knowledge based recommendations systems can be enhanced by exploiting ontology reasoning characteristics. This paper explores different techniques used to generate knowledge-based recommendations highlighting the advantages of knowledge based recommendation system over other recommendation techniques.

References
  1. Berendt, Bettina, Andreas Hotho, Dunja Mladenic, Maarten Van Someren, Myra Spiliopoulou, and Gerd Stumme. “A roadmap for Web mining: From Web to Semantic Web”. Springer Berlin Heidelberg, 2004.
  2. Nasraoui, Olfa. "World wide Web personalization." Encyclopedia of Data Mining and Data Warehousing, Idea Group (2005).
  3. Bobadilla, Jesús, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. "Recommender systems survey." Knowledge-Based Systems 46 (2013): 109-132.
  4. Gedikli, Fatih, and Dietmar Jannach. "Recommender Systems, Semantic-Based." Encyclopedia of Social Network Analysis and Mining. Springer New York, 2014. 1501-1510.
  5. Felfernig, Alexander, Michael Jeran, Gerald Ninaus, Florian Reinfrank, Stefan Reiterer, and Martin Stettinger. "Basic approaches in recommendation systems." In Recommendation Systems in Software Engineering, pp. 15-37. Springer Berlin Heidelberg, 2014.
  6. Pazzani, Michael J., and Daniel Billsus. "Content-based recommendation systems." In The adaptive Web, pp. 325-341. Springer Berlin Heidelberg, 2007.
  7. Lops, Pasquale, Marco De Gemmis, and Giovanni Semeraro. "Content-based recommender systems: State of the art and trends." In Recommender systems handbook, pp. 73-105. Springer US, 2011.
  8. IJntema, Wouter, Flavius Frasincar, Frederik Hogenboom, and Uzay Kaymak. "News Personalization using the CF-IDF Semantic Recommender." (2011).
  9. Bogdanov, Dmitry, MartíN Haro, Ferdinand Fuhrmann, Anna Xambó, Emilia Gómez, and Perfecto Herrera. "Semantic audio content-based music recommendation and visualization based on user preference examples." Information Processing & Management 49, no. 1 (2013): 13-33.
  10. Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4.
  11. Aggarwal, Charu C., and S. Yu Philip. "Semantic based collaborative filtering." U.S. Patent 6,487,539, issued November 26, 2002.
  12. Lee Jae-Won, Kim Han-Joon And Lee Sang-Goo, ”Applying taxonomic knowledge and semantic collaborative filtering to personalized search: a bayesian belief network based approach”,in proceedings of 2010 12th International Asia-Pacific Web Conference IEEE. (2010).
  13. López-Nores, Martín, Yolanda Blanco-Fernández, José J. Pazos-Arias, and Alberto Gil-Solla. "Property-based collaborative filtering for health-aware recommender systems." Expert Systems with Applications 39, no. 8 (2012): 7451-7457.
  14. Fard, Karamollah Bagheri, Mehrbakhsh Nilashi, and Naomie Salim. "Recommender system based on semantic similarity." International Journal of Electrical and Computer Engineering (IJECE) 3, no. 6 (2013): 751-761.
  15. Smyth, Barry. "Case-based recommendation." In The adaptive Web, pp. 342-376. Springer Berlin Heidelberg, 2007.
  16. Daramola, Olawande, Mathew Adigun, and Charles Ayo. "Building an ontology-based framework for tourism recommendation services." Information and Communication Technologies in Tourism 2009 (2009): 135-147.
  17. 17.Felfernig, Alexander, and Robin Burke. "Constraint-based recommender systems: technologies and research issues." In Proceedings of the 10th international conference on Electronic commerce, p. 3. ACM, 2008.
  18. Felfernig, Alexander, Gerhard Friedrich, Dietmar Jannach, and Markus Zanker. "Developing Constraint-based Recommenders." Recommender systems handbook 1 (2011): 187.
  19. Felfernig, Alexander, Sarah Haas, Gerald Ninaus, Michael Schwarz, Thomas Ulz, Martin Stettinger, Klaus Isak, Michael Jeran, and Stefan Reiterer. "Recturk: Constraint-based recommendation based on human computation." In RecSys 2014 CrowdRec Workshop, pp. 1-6. 2014.
  20. Dell’Aglio, Daniele, Irene Celino, and Dario Cerizza. "Anatomy of a Semantic Web-enabled knowledge-based recommender system." In Proceedings of the 4th international workshop Semantic Matchmaking and Resource Retrieval in the Semantic Web, at the 9th International Semantic Web Conference. 2010.
  21. Alferes, Jóse Júlio, Carlos Viegas Damásio, and Luís Moniz Pereira. "Semantic Web logic programming tools." In Principles and practice of Semantic Web reasoning, pp. 16-32. Springer Berlin Heidelberg, 2003.
  22. Berners-Lee, Tim, James Hendler, and Ora Lassila. "The Semantic Web." Scientific american 284, no. 5 (2001): 28-37.
  23. Smart, Paul R. "Rule-Based Intelligence on the Semantic Web: Implications for Military Capabilities." (2007).
  24. Antoniou, Grigoris, Matteo Baldoni, Cristina Baroglio , Robert Baumgartner, François Bry, Thomas Eiter, Nicola Henze et al.” Reasoning methods for personalization on the Semantic Web”. na, 2004.
  25. Antoniou, G., Billington, D., Governatori, G., And Maher, M.” Representation Results for Defeasible Logic”. ACM Transactions on Computational Logic 2,2 (2002), 255–287.
  26. Heymans, S., And Vermeir, D. “Integrating Semantic Web reasoning and answer set programming”. In Answer Set Programming, Advances in Theory and Implementation, Proc. 2nd Intl. ASP’03 Workshop, Messina, Italy (2003), pp. 194–208
  27. Alferes, José Júlio, Antonio Brogi, João Alexandre Leite, and Luís Moniz Pereira. "Evolving logic programs." In Logics in Artificial Intelligence, pp. 50-62. Springer Berlin Heidelberg, 2002.
  28. Mu, Xiangwei, Yan Chen, Yan Cao, and Yan Lin. "Personalized Recommendation System Modeling in Semantic Web." Advances in Information Sciences and Service Sciences 5, no. 2 (2013): 278.
  29. Baader, Franz, Ian Horrocks, and Ulrike Sattler. "Description logics as ontology languages for the Semantic Web." In Mechanizing Mathematical Reasoning, pp. 228-248. Springer Berlin Heidelberg, 2005.
  30. Krötzsch, Markus, Frantisek Simancik, and Ian Horrocks. "Description Logics."IEEE Intelligent Systems 29, no. 1 (2014): 12-19.
  31. Tran, Thanh, Philipp Cimiano, and Anupriya Ankolekar. "A Rule-Based Adaption Model for Ontology-Based Personalization." In Advances in Semantic Media Adaptation and Personalization, pp. 117-135. Springer Berlin Heidelberg, 2008.
  32. Papamarkos, George, Alexandra Poulovassilis, and Peter T. Wood. "Event-Condition-Action Rule Languages for the Semantic Web." In SWDB, pp. 309-327. 2003.
  33. Moore, Philip T., and Hai V. Pham. "Personalization and rule strategies in data-intensive intelligent context-aware systems." The Knowledge Engineering Review 30, no. 02 (2015): 140-156.
  34. Debattista, Jeremy, Simon Scerri, Ismael Rivera, and Siegfried Handschuh. "Ontology-based Rules for Recommender Systems." In SeRSy, pp. 49-60. 2012.
  35. Barla, Michal, Michal Tvarožek, and Mária Bieliková. "Rule-based user characteristics acquisition from logs with semantics for personalized Web-based systems." Computing and Informatics 28, no. 4 (2012): 399-428.
  36. P. Jackson, “Introduction to Expert Systems”, 3rd edition. Boston: Addison- Wesley Longman Publishing Co., 2001
  37. Verhodubs, Olegs, and Janis Grundspenkis. "Towards the Semantic Web Expert System." Scientific Journal of Riga Technical University. Computer Sciences 44, no. 1 (2011): 116-123.
  38. García-Crespo, Ángel, José Luis López-Cuadrado, Ricardo Colomo-Palacios, Israel González-Carrasco, and Belén Ruiz-Mezcua. "Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain."Expert systems with applications 38, no. 10 (2011): 13310-13319.
  39. Wanner, Leo, Marco Rospocher, Stefanos Vrochidis, Lasse Johansson, Nadjet Bouayad-Agha, Gerard Casamayor, Ari Karppinen et al. "Ontology-centered environmental information delivery for personalized decision support." Expert Systems with Applications 42, no. 12 (2015): 5032-5046.
  40. Singh, Swapna, and Ragini Karwayun. "A comparative study of inference engines." In Information Technology: New Generations (ITNG), 2010 Seventh International Conference on, pp. 53-57. IEEE, 2010.
  41. Doulaverakis, Charalampos, George Nikolaidis, Athanasios Kleontas, and Ioannis Kompatsiaris. "Panacea, a semantic-enabled drug recommendations discovery framework." In VDOS+ DO@ ICBO. 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Semantic Web Ontologies Reasoning Knowledge base.

Powered by PhDFocusTM