A New Ontology-Supported and Hybrid Recommending Information System for Scholars

A new ontology-supported and hybrid recommending information system for scholars was proposed. Not only can it fast integrate specific domain documents, but also it can extract important information from them through the hybrid filtering technology to take information integration and recommendation ranking. The experiment outcomes proved that the reliability and validity measurements of the whole system performance can achieve the high-level outcomes of information recommendation. Furthermore, this paper also discussed and investigated the advantages and shortcomings of the construction of a recommendation system with different approaches and accordingly provided the design philosophy of customized services for recommendation systems.

[1]  Balakrishnan Chandrasekaran,et al.  What are ontologies, and why do we need them? , 1999, IEEE Intell. Syst..

[2]  V. R. Benjamins,et al.  WonderTools? A comparative study of ontological engineering tools , 2000, Int. J. Hum. Comput. Stud..

[3]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[4]  Henrik Eriksson,et al.  The evolution of Protégé: an environment for knowledge-based systems development , 2003, Int. J. Hum. Comput. Stud..

[5]  J. P. Peter.  Reliability: A Review of Psychometric Basics and Recent Marketing Practices , 1979 .

[6]  Sheng-Yuan Yang,et al.  Ontology-supported webpage classifier for scholar’s webpages in ubiquitous information environment , 2008, 2008 First IEEE International Conference on Ubi-Media Computing.

[7]  Sheng-Yuan Yang,et al.  Ontology-Supported Web Recommender for Scholar Information , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[8]  Sheng-Yuan Yang,et al.  Ontology-Supported Focused-Crawler for Specified Scholar's Webpages , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[9]  Henrik Eriksson,et al.  Knowledge modeling at the millennium : The design and evolution of Protégé-2000 , 1999 .

[10]  Von-Wun Soo,et al.  Ontology-Based Information Gathering Agents , 2001, Web Intelligence.

[11]  Thorsten Joachims,et al.  A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization , 1997, ICML.

[12]  Karen Sparck Jones.  A statistical interpretation of term specificity and its application in retrieval , 1972 .

[13]  Timothy W. Finin,et al.  Swoogle: a search and metadata engine for the semantic web , 2004, CIKM '04.

[14]  Keh-Jiann Chen,et al.  Introduction to CKIP Chinese Word Segmentation System for the First International Chinese Word Segmentation Bakeoff , 2003, SIGHAN.

[15]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[16]  Larry Kerschberg,et al.  WebSifter II: A Personalizable Meta-Search Agent Based on Weighted Semantic Taxonomy Tree , 2001, International Conference on Internet Computing.

[17]  Hahn-Ming Lee,et al.  An intelligent web-page classifier with fair feature-subset selection , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).