Open AccessA novel ontology framework supporting model-based tourism recommender
Author(s) -
Ho Quoc Dung,
Lien Thi Quynh Le,
Nguyen Huu Hoang Tho,
Tri Quoc Truong,
Cuong H. Nguyen-Dinh
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i4.pp1060-1068
Subject(s) - computer science , ontology , knowledge base , recommender system , domain knowledge , domain (mathematical analysis) , process (computing) , tourism , machine learning , naive bayes classifier , software deployment , space (punctuation) , vector space model , artificial intelligence , support vector machine , information retrieval , data mining , software engineering , mathematical analysis , philosophy , mathematics , epistemology , political science , law , operating system
In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework.