Skip to main content
SpringerLink
Log in

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abbas A, Zhang L, Khan SU (2015) A survey on context-aware recommender systems based. Computing 97(7):667–690

    Article  MathSciNet  Google Scholar 

  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state of the art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  • Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Ricci et al. (eds) Recommender systems handbook. Springer US, pp. 217–253

  • Bahmani A (2012) Ontology based recommendation algorithms for personalized education. Lecture notes in computer science, pp 111–120

  • Baseera A (2014) Design and development of a recommender system for E-learning modules. J Comput Sci 10(5):720–722

    Article  Google Scholar 

  • Biletskiy Y, Baghi H, Keleberda I, Fleming M (2009) An adjustable personalization of search and delivery of learning objects to learners. Exp Syst Appl 36(5):9113–9120

    Article  Google Scholar 

  • Blanco-Fernández Y, López-Nores M, Gil-Solla A, Ramos-Cabrer M, Pazos-Arias JJ (2011) Exploring synergies between content-based filtering and spreading activation techniques in knowledge-based recommender systems. Inf Sci 181(21):4823–4846

    Article  Google Scholar 

  • Bobadilla J, Hernando A, Ortega F, Bernal J (2011) A framework for collaborative filtering recommender systems. Exp Syst Appl 38(12):14609–14623

    Article  Google Scholar 

  • Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  • Brut M, Sèdes F (2010) Ontology-based solution for personalized recommendations in e-learning systems: Methodological aspects and evaluation criterias. Proceedings of the 10th IEEE international conference on advanced learning technologies. ICALT. Sousse, Tunisia, pp 469–471

    Google Scholar 

  • Buder J, Schwind C (2012) Learning with personalized recommender systems: a psychological view. Comput Hum Behav 28(1):207–216

    Article  Google Scholar 

  • Burgun A (2006) Desiderata for domain reference ontologies in biomedicine. J Biomed Inform 39(3):307–313

    Article  Google Scholar 

  • Burke R (2007) Hybrid web recommender systems. In: The adaptive web, pp. 377–408

  • Cantador I, Bellogín A, Castells P (2008) A multilayer ontology-based hybrid recommendation model. AI Commun 21:203–210

    MathSciNet  MATH  Google Scholar 

  • Capuano N, Gaeta M, Ritrovato P, Salerno S (2014) Elicitation of latent learning needs through learning goals recommendation. Comput Hum Behav 30:663–673

    Article  Google Scholar 

  • Carrer-Neto W, Hernández-Alcaraz ML, Valencia-García R, García-Sánchez F (2012) Social knowledge-based recommender system. Application to the movies domain. Exp Syst Appl 39(12):10990–11000

  • Cazella SC, Behar PA, Schneider D, Kellen K, Freitas R (2014) Developing a learning objects recommender system based on competences to education: experience report competences: an education view, vol 2. Springer International Publishing, Switzerland, pp 217–226

    Google Scholar 

  • Chen W, Niu Z, Zhao X, Li Y (2014) A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2):271–284

    Article  Google Scholar 

  • Cheng ST, Chou CL, Horng GJ (2013) The adaptive ontology-based personalized recommender system. Wirel Pers Commun 72(4):1801–1826

    Article  Google Scholar 

  • Chughtai MW, Selamat A, Ghani I, Jung JJ (2014) E-learning recommender systems based on goal-based hybrid filtering. Int J Distrib Sens Netw 2014:1–19

    Google Scholar 

  • Ciuciu I, Tang Y (2010) A personalized and collaborative elearning materials recommendation scenario using ontology-based data matching strategies. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 6428 LNCS, pp 575–584

  • Cobos C, Rodriguez O, Rivera J, Betancourt J, Mendoza M, León E, Herrera-Viedma E (2013) A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes. Inf Process Manag 49(3):607–625

    Article  Google Scholar 

  • Colombo-Mendoza LO, Valencia-García R, Rodríguez-González A, Alor-Hernández G, Samper-Zapater JJ (2015) RecomMetz: a context-aware knowledge-based mobile recommender system for movie showtimes. Exp Syst Appl 42(3):1202–1222

    Article  Google Scholar 

  • Dey A, Abowd G, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human Comput Interact 16(2–4):97–166

    Article  Google Scholar 

  • Drachsler H, Hummel HGK, Koper R (2008) Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. Int J Learn Technol 3(4):404–423

    Article  Google Scholar 

  • Drachsler H, Verbert K, Santos OC, Manouselis N (2015) Panorama of recommender systems to support learning. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook, 2nd edn. Springer US, Boston, MA, pp 1–37. http://link.springer.com/chapter/10.1007%2F978-1-4899-7637-6_12

  • Dwivedi P, Bharadwaj KK (2013) Effective trust-aware E-learning recommender system based on learning styles and knowledge levels. Educ Technol Soc 16:201–216

    Google Scholar 

  • Erdt M, Fernandez A, Rensing C (2015) Evaluating recommender systems for technology enhanced learning: a quantitative survey. IEEE Trans Learn Technol 1382(c):1

  • Felfernig A, Burke R (2008) Constraint-based recommender systems: technologies and research issues. In: Proceedings of the 10th international conference on electronic commerce ICEC ’08, vol 8(5), pp 1–10

  • Ferreira-Satler M, Romero FP, Menendez-Dominguez VH, Zapata A, Prieto ME (2012) Fuzzy ontologies-based user profiles applied to enhance e-learning activities. Soft Comput 16(7):1129–1141

    Article  Google Scholar 

  • Fraihat S, Shambour Q (2014) A framework of semantic recommender system for e-learning. J Softw 10(3):317–330

    Article  Google Scholar 

  • García I, Benavides C, Alaiz H, Alonso A (2013) A study of the use of ontologies for building computer-aided control engineering self-learning educational software. J Sci Educ Technol 22(4):589–601

    Article  Google Scholar 

  • Ghauth KI, Abdullah NA (2010) Measuring learner’s performance in e-learning recommender systems. Aust J Educ Technol 26(6):764–774

    Google Scholar 

  • Golbeck J, Hendler J (2006) Inferring binary trust relationships in Web-based social networks. ACM Trans Internet Technol 6(4):497–529

    Article  Google Scholar 

  • González-Martínez JA, Bote-Lorenzo ML, Gómez-Sánchez E, Cano-Parra R (2015) Cloud computing and education: a state-of-the-art survey. Comput Educ 80:132–151

    Article  Google Scholar 

  • Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220

    Article  Google Scholar 

  • Gutiérrez G, Margain L, Ochoa A, Rojas J (2012) Development of a computational recommender algorithm for digital resources for education using case-based reasoning and collaborative filtering. In: Advances in intelligent and soft computing, vol 151 AISC, pp 767–774

  • Han Q, Gao F, Wang H (2010) Ontology-based learning object recommendation for cognitive considerations. Proceedings of the 8th World congress on intelligent control and automation. Jinan, China, pp 2746–2750

    Google Scholar 

  • Hashizume K, Rosado DG, Fernandez-Medina E, Fernandez EB (2013) An analysis of security issues for cloud computing. J Internet Serv Appl 4(1):1–13

    Article  Google Scholar 

  • He J, Chu W (2010) A social network-based recommender system (SNRS). In: Memon N, Xu JJ, Hicks DL, Chen H (eds) Data mining for social network data. Springer, New York, pp 47–74

    Chapter  Google Scholar 

  • Hsu CK, Hwang GJ, Chang CK (2010) Development of a reading material recommendation system based on a knowledge engineering approach. Comput Educ 55(1):76–83

    Article  Google Scholar 

  • Huang C, Liu L, Tang Y, Lu L (2011a) Semantic web enabled personalized recommendation for learning paths and experiences. Commun Comput Inf Sci 235 CCIS(PART 5):258–267

  • Huang Z, Lu X, Duan H (2011b) Context-aware recommendation using rough set model and collaborative filtering. Artif Intell Rev 35(1):85–99

    Article  Google Scholar 

  • Jannach D, Zanker M, Felfernig A, Friedrich G (2011) Recommender systems: an introduction. Cambridge University Press, Cambridge

    Google Scholar 

  • Kalibatiene D, Vasilecas O (2011) Survey on ontology languages. Lecture notes in business information processing, 90 LNBIP, pp 124–141

  • Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in Software Engineering Version 2.3. Engineering 45(4ve):1051

  • Klašnja-Milićević A, Ivanović M, Nanopoulos A (2015) Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif Intell Rev 44(4):571–604

    Article  Google Scholar 

  • Klašnja-Milićević A, Vesin B, Ivanović M, Budimac Z (2011) E-Learning personalization based on hybrid recommendation strategy and learning style identification. Comput Educ 56(3):885–899

    Article  MATH  Google Scholar 

  • Kontopoulos E, Vrakas D, Kokkoras F, Bassiliades N, Vlahavas I (2008) An ontology-based planning system for e-course generation. Exp Syst Appl 35(1–2):398–406

    Article  Google Scholar 

  • Lops P, Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Systems recommender. Springer, New York, pp 73–105 Handbook

  • Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Supp Syst 74:12–32

    Article  Google Scholar 

  • Manouselis N, Drachsler H, Vuorikari R, Hummel H, Koper R (2011) Recommender systems in technology enhanced learning. Recommender systems handbook. Springer, New York, pp 387–415

    Chapter  Google Scholar 

  • Manouselis N, Vuorikari R, Van Assche F (2010) Collaborative recommendation of e-learning resources: an experimental investigation. J Comput Assist Learn 26(4):227–242

    Article  Google Scholar 

  • Mao M, Peng Y, He D (2006) DiLight: an ontology-based information access system for e-learning environments. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, vol 2006. Seattle, WA, USA, p 733

  • Martinez-Cruz C, Porcel C, Bernabé-Moreno J, Herrera-Viedma E (2015) A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf Sci 311:102–118

    Article  Google Scholar 

  • Masthoff J (2011) Group recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 677–702

    Chapter  Google Scholar 

  • Medland MB (2007) Tools for knowledge analysis, synthesis, and sharing. J Sci Educ Technol 16(2):119–153

    Article  Google Scholar 

  • Montaner M, López B, De La Rosa JL (2003) A taxonomy of recommender agents on the internet. Artif Intell Rev 19(4):285–330

    Article  Google Scholar 

  • Moradi P, Ahmadian S (2015) A reliability-based recommendation method to improve trust-aware recommender systems. Exp Syst Appl 42(21):7386–7398

    Article  Google Scholar 

  • Mota D, de Carvalho CV, Reis LP (2014) OTILIA—an architecture for the recommendation of teaching-learning techniques supported by an ontological approach. In: 2014 IEEE frontiers in education conference (FIE) proceedings. Madrid, Spain, pp 1–7

  • Najafabadi MK, Mahrin MN (2016) A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback. Artif Intell Rev 45(2):167–201

    Article  Google Scholar 

  • Neri MA, Colombetti M (2009) Ontology-based learning objects search and courses generation. Appl Artif Intell 23(3):233–260

    Article  Google Scholar 

  • Nowakowski S, Ognjanovi I, Grandbastien M (2014) Two recommending strategies to enhance online presence in personal learning environments. In: Manouselis N et al (eds) Recommender systems for technology enhanced learning: research trends and applications. Springer, New York, pp 227–249

    Chapter  Google Scholar 

  • Pan PY, Wang CH, Horng GJ, Cheng ST (2010) The development of an ontology-based adaptive personalized recommender system. In: ICEIE 2010–2010 international conference on electronics and information engineering, proceedings, p 1

  • Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Exp Syst Appl 39(11):10059–10072

    Article  Google Scholar 

  • Paredes-Valverde MA, Rodríguez-García MÁ, Ruiz-Martínez A, Valencia-García R, Alor-Hernández G (2015) ONLI: an ontology-based system for querying DBpedia using natural language paradigm. Exp Syst Appl 42(12):5163–5176

    Article  Google Scholar 

  • Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5):393–408

    Article  Google Scholar 

  • Pazzani MJ, Billsus D (2007) Content-based recommendation systems. The adaptive Web, pp 325–341

  • Poelmans J, Ignatov DI, Kuznetsov SO, Dedene G (2013) Formal concept analysis in knowledge processing: a survey on applications. Exp Syst Appl 40(16):6538–6560

    Article  Google Scholar 

  • Pu P, Chen L, Hu R (2012) Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model User Adap Inter 22(4–5):317–355

    Article  Google Scholar 

  • Pukkhem N (2013) Ontology-based semantic approach for learning object recommendation. Int J Inf Technol 3(4):12–21

    Google Scholar 

  • Pukkhem N (2014) LORecommendNet: an ontology-based representation of learning object recommendation. Adv Intell Syst Comput 265:293–303

    Google Scholar 

  • Rani M, Muyeba MK, Vyas OP (2014) A hybrid approach using ontology similarity and fuzzy logic for semantic question answering. Adv Comput Netw Inf 1:601–609

    Google Scholar 

  • Rashid AM, Karypis G, Riedl J (2008) Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explor Newsl 10(2):90–100

    Article  Google Scholar 

  • Rey-lópez M, Díaz-redondo RP, Fernández-vilas A, Pazos-arias JJ (2010) T-Learning 2.0—a personalized hybrid approach based on ontologies and folksonomies. In: Computational intelligence for technology enhanced learning, pp 125–142

  • Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Recommender systems handbook, vol 54. Springer, Boston, pp 1–35

    Chapter  Google Scholar 

  • Rodrigues JA, Cardoso LF, Moreira J, Xexéo G (2012) Bringing knowledge into recommender systems. J Syst Softw 86(7):1751–1758

    Article  Google Scholar 

  • Roussey C, Pinet F, Kang MA, Corcho O (2011) An introduction to ontologies and ontology engineering. In: Falquet G, Metral C, Telleer J, Tweed C (eds) Ontologies in Urban development projects, vol 3, pp 9–38

  • Ruiz-Iniesta A, Jimenez-Diaz G, Gomez-Albarran M (2014) A semantically enriched context-aware OER recommendation strategy and its application to a computer science OER repository. IEEE Trans Educ 57(4):255–260

    Article  Google Scholar 

  • Ruotsalo T (2010) Methods and applications for ontology-based recommender systems, Ph.D. thesis, Aalto University School of Science and Technology, Finland. http://lib.tkk.fi/Diss/2010/isbn9789526031514/isbn9789526031514.pdf

  • Salehi M, Nakhai Kamalabadi I, Ghaznavi Ghoushchi MB (2013) An effective recommendation framework for personal learning environments using a learner preference tree and a GA. IEEE Trans Learn Technol 6(4):350–363

    Article  Google Scholar 

  • Santos OC, Boticario JG (2015) Practical guidelines for designing and evaluating educationally oriented recommendations. Comput Educ 81:354–374. http://www.sciencedirect.com/science/article/pii/S0360131514002280

  • Schafer J, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. The adaptive Web, pp 291–324

  • Sharma M, Mann S (2013) A survey of recommender systems: approaches and limitations. Int J Innov Eng Technol 2(2):1–9

    Google Scholar 

  • Shen LP, Shen RM (2005) Ontology-based learning content recommendation. Int J Contin Eng Educ Life Long Learn 15(3):308–317

    Article  Google Scholar 

  • Shishehchi S, Banihashem SY (2011) Learning content recommendation for Visual Basic. Net programming language based on ontology. J Comput Sci 7(2):188–196

  • Shishehchi S, Banihashem SY, Zin NA, Noah SA (2011) Review of personalized recommendation techniques for learners in e-learning systems. 2011 International conference on semantic technology and information retrieval, STAIR 2011. Putrajaya, Malaysia, pp 277–281

    Chapter  Google Scholar 

  • Shishehchi S, Banihashem SY, Zin NA, Noah SA (2012) Ontological approach in knowledge based recommender system to develop the quality of E-learning system. Aust J Basic Appl Sci 6(2):115–123

    Google Scholar 

  • Sicilia MÁ, Lytras MD, Sánchez-Alonso S, García-Barriocanal E, Zapata-Ros M (2011) Modeling instructional-design theories with ontologies: using methods to check, generate and search learning designs. Comput Hum Behav 27(4):1389–1398

    Article  Google Scholar 

  • Sosnovsky S, Hsiao I, Brusilovsky P (2012) Adaptation “in the Wild”: ontology-based personalization of open-corpus learning material. EC-TEL’12: Proceedings of the 7th European conference on technology enhanced learning. Saarbrucken, Germany, pp 425–431

    Google Scholar 

  • Tarus JK, Gichoya D (2015) E-learning in Kenyan Universities: preconditions for successful implementation. Electron J Inf Syst Dev Ctries 66(4):1–14

    Google Scholar 

  • Tarus JK, Gichoya D, Muumbo A (2015) Challenges of implementing E-learning in Kenya: a case of Kenyan Public Universities. Int Rev Res Open Distrib Learn 16(1):120–141

    Article  Google Scholar 

  • Verbert K, Manouselis N, Ochoa X, Wolpers M, Drachsler H, Bosnic I, Duval E (2012) Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans Learn Technol 5(4):318–335

    Article  Google Scholar 

  • Vesin B, Ivanović M, Klašnja-Milićević A, Budimac Z (2012) Protus 2.0: ontology-based semantic recommendation in programming tutoring system. Exp Syst Appl 39(15):12229–12246

  • Vesin B, Klasnja-Mili’cevi’ A, Ivanovi’c M, Budimac Z (2011) Applying recommender systems and adaptive hypermedia for e-learning personalization. Computing and informatics, pp 0–30

  • Victor P, De Cock M, Cornelis C (2011) Trust and recommendations. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 645–675

    Chapter  Google Scholar 

  • Wang HC, Huang TH (2013) Personalized e-learning environment for bioinformatics. Interact Learn Environ 21(1):18–38

    Article  Google Scholar 

  • Wang PY, Yang HC (2012) Using collaborative filtering to support college students’ use of online forum for English learning. Comput Educ 59(2):628–637

    Article  Google Scholar 

  • Wang TI, Tsai KH, Lee MC, Chiu TK (2007) Personalized learning objects recommendation based on the semantic–aware discovery and the learner preference pattern. Educ Technol Soc 10:84–105

    Google Scholar 

  • Weng SS, Chang HL (2008) Using ontology network analysis for research document recommendation. Exp Syst Appl 34(3):1857–1869

    Article  Google Scholar 

  • Yang SY (2010) Developing an ontology-supported information integration and recommendation system for scholars. Exp Syst Appl 37(10):7065–7079

    Article  Google Scholar 

  • Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10

    Article  Google Scholar 

  • Yu Z, Nakamura Y, Jang S, Kajita S, Mase K (2007) Ontology-based semantic recommendation for context-aware E-learning. Lecture notes in computer science, pp 898–907

  • Zhang Z, Gong L, Xie J (2013a) Ontology-based collaborative filtering recommendation algorithm. Lecture notes in artificial intelligence, pp 172–181

  • Zhang Z, Lin H, Liu K, Wu D, Zhang G, Lu J (2013b) A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf Sci 235:117–129

    Article  Google Scholar 

  • Zhao X, Niu Z, Chen W, Shi C, Niu K, Liu D (2015a) A hybrid approach of topic model and matrix factorization based on two-step recommendation framework. J Intell Inf Syst 44:335–353

  • Zhao X, Niu Z, Wang K, Niu K, Liu Z (2015b) Improving top-N recommendation performance using missing data. Math Probl Eng 2015:1–14

  • Zhuhadar L, Nasraoui O (2010) A hybrid recommender system guided by semantic user profiles for search in the e-learning domain. J Emerg Technol Web Intell 2(4):272–281

    Google Scholar 

  • Žitko B, Stankov S, Rosić M, Grubišić A (2009) Dynamic test generation over ontology-based knowledge representation in authoring shell. Exp Syst Appl 36(4):8185–8196

    Article  Google Scholar 

  • Zydney JM, Warner Z (2016) Mobile apps for science learning: review of research. Comput Educ 94:1–17

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61370137), the National Basic Research Program of China (No. 2012CB7207002), the Ministry of Education—China Mobile Research Foundation Project (No. 2015/5-9 and No. 2016/2-7) and the 111 Project of Beijing Institute of Technology. The authors want to acknowledge the useful suggestions of the reviewers which have increased significantly the quality of the review.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    John K. Tarus & Zhendong Niu

  2. Directorate of ICT, Moi University, Eldoret, Kenya

    John K. Tarus

  3. School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA

    Zhendong Niu

  4. Department of Information Technology, University of the Punjab, Jhelum, Pakistan

    Ghulam Mustafa

Authors
  1. John K. Tarus
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhendong Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ghulam Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhendong Niu.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tarus, J.K., Niu, Z. & Mustafa, G. Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artif Intell Rev 50, 21–48 (2018). https://doi.org/10.1007/s10462-017-9539-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-017-9539-5

Keywords

Search

Navigation