Ontology-Based Travel Recommender System

2021 ◽  
pp. 460-476
Author(s):  
Shi Pu ◽  
Isibor Kennedy Ihianle
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Web Ontology Language ◽  
Satisfaction Rate ◽  
Semantic Data ◽  
Ontology Language ◽  
Overall Evaluation ◽  
Owl Ontology ◽  
Travel Recommendation

Recommender systems are designed to suggest information to users according to their preferences. The items could be movies, books, or various kinds of products. Most of the existing recommender systems are based on a database with limited advantages. However, in this chapter, the authors propose a knowledge-driven travel recommender system to integrate semantic data built using web ontology language (OWL) ontology to allow users to find suitable destinations that fulfil users' travel preferences. This work aims to develop a travel recommendation tool and to examine the reliability, the usability of the system, and satisfaction rate of users. They are also able to demonstrate that users can obtain desired results through queries on the ontology-based system. The overall evaluation of the system shows that users are happy and satisfied with the recommendation results.

Intelligent POIs Recommender System Based on Time Series Analysis with Seasonal Adjustment

2021 ◽  
Vol 2 (2) ◽  
pp. 66-80
Author(s):  
Meng-Kuan Chen ◽  
Hsin-Wen Wei ◽  
Wei-Tsong Lee
Keyword(s):  
Time Series ◽  
Recommender Systems ◽  
Recommender System ◽  
Research Articles ◽  
Seasonal Adjustment ◽  
Huge Amount ◽  
Travel Information ◽  
Seasonal Factor ◽  
Correct Location ◽  
Travel Recommendation

Recommender systems have been applied on a variety of applications including movies, music, news, books, research articles, search queries, and travel information. Instead of searching travel information from the extremely huge amount of travel data, a personalized travel recommender system is desired. However, an inappropriate travel recommendation may result from a wrong season, even if it is already a correct location. The current recommender systems from time to time make an inappropriate commendation without considering the seasonal factor. In order to resolve the discrepancy, the seasonal factor should have been taken into consideration when making a good travel recommender system. Therefore, this study has taken the trend analysis, time series, and seasonal factor into considerations to cope with the above mentioned discrepancy and to make the travel recommender system renders a better fit.

scholarly journals RDB2OWL: AN IMPROVED METHOD FOR CONVERTING RELATIONAL DATABASES INTO OWL

2017 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
Phạm Thị Thu Thúy
Keyword(s):  
Semantic Web ◽  
Relational Databases ◽  
Web Ontology Language ◽  
Improved Method ◽  
Ontology Language ◽  
Owl Ontology

Một trong những lợi thế của Semantic Web là để mô tả dữ liệu với một ý nghĩa rõ ràng và liên kết giữa các dữ liệu bằng cách sử dụng ngôn ngữ OWL (Web Ontology Language). Ngày nay hầu hết các dữ liệu được lưu trữ trong cơ sở dữ liệu quan hệ. Để tận dụng lại các dữ liệu này, cần thiết phải có phương pháp chuyển dữ liệu lưu trữ trong cơ sở dữ liệu quan hệ vào định dạng của OWL Ontology. Một số phương pháp đã được đề xuất, tuy nhiên, hầu hết các quy tắc chuyển đổi đã không được hoàn chỉnh. Bài báo này đề xuất một số quy tắc cải thiện trong việc chuyển đổi cơ sở dữ liệu quan hệ sang OWL Ontology. Ngoài ra, tất cả các bước chuyển đổi trong thuật toán RDB2OWL được thực hiện tự động mà không cần bất kỳ sự can thiệp của người dùng.

scholarly journals Algorithm of Ontology Transformation to Concept Map for Usage in Semantic Web Expert System

2013 ◽  
Vol 14 (1) ◽  
pp. 80-87
Author(s):  
Olegs Verhodubs ◽  
Janis Grundspenkis
Keyword(s):  
Expert System ◽  
Semantic Web ◽  
Knowledge Base ◽  
Concept Map ◽  
Web Ontology Language ◽  
Subsequent Generation ◽  
Ontology Language ◽  
Owl Ontology ◽  
System Knowledge

Abstract The main purpose of this paper is to present an algorithm of OWL (Web Ontology Language) ontology transformation to concept map for subsequent generation of rules and also to evaluate the efficiency of this algorithm. These generated rules are necessary to supplement and even to develop SWES (Semantic Web Expert System) knowledge base. This paper is a continuation of the earlier research of OWL ontology transformation to rules.

scholarly journals A Methodology for an Auto-Generated and Auto-Maintained HL7 FHIR OWL Ontology for Health Data Management

2021 ◽  
Author(s):  
Vassilis Kilintzis ◽  
Vasileios C. Alexandropoulos ◽  
Nikolaos Beredimas ◽  
Nicos Maglaveras
Keyword(s):  
Data Management ◽  
Health Data ◽  
Semantic Model ◽  
Web Ontology Language ◽  
Complete Conversion ◽  
Data Types ◽  
Ontology Language ◽  
Hl7 Fhir ◽  
Health Level Seven ◽  
Owl Ontology

The process of maintenance of an underlying semantic model that supports data management and addresses the interoperability challenges in the domain of telemedicine and integrated care is not a trivial task when performed manually. We present a methodology that leverages the provided serializations of the Health Level Seven International (HL7) Fast Health Interoperability Resources (FHIR) specification to generate a fully functional OWL ontology along with the semantic provisions for maintaining functionality upon future changes of the standard. The developed software makes a complete conversion of the HL7 FHIR Resources along with their properties and their semantics and restrictions. It covers all FHIR data types (primitive and complex) along with all defined resource types. It can operate to build an ontology from scratch or to update an existing ontology, providing the semantics that are needed, to preserve information described using previous versions of the standard. All the results based on the latest version of HL7 FHIR as a Web Ontology Language (OWL-DL) ontology are publicly available for reuse and extension.

Discussion on the Software Engineering Data Modeling in OWL

2013 ◽  
Vol 416-417 ◽  
pp. 1512-1515
Author(s):  
Yuan Yang
Keyword(s):  
Software Engineering ◽  
Machine Language ◽  
Complex Data ◽  
Data Systems ◽  
Web Ontology Language ◽  
It Industry ◽  
Semantic Web Technology ◽  
Semantic Data ◽  
Ontology Language ◽  
Engineering Data

Web ontology language, which is discussed usually by people, is abbreviated as OWL. Actually, it specifically refers to the computer Web ontology language, namely a type of computer machine language. OWL is one of the very important components composing semantic Web technology. It is an ontology language that is recently offered by W3C especially for Web language, and its ontology working group makes a description to OWL through a series of documents. OWL is applicable to the description and modeling on the semantic aspects of complex data, and it can establish a flexible semantic model when a lot of complicated structures and rich semantic data are often derived in the development process of software system. All software engineering data systems are managed in modes. OWL, as an ontology language, has been widely valued in the IT industry. However, the development of OWL has been seriously restricted because it is difficult to understand.

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

tOWL: A Temporal Web Ontology Language

2012 ◽  
Vol 42 (1) ◽  
pp. 268-281 ◽  
Author(s):  
V. Milea ◽  
F. Frasincar ◽  
U. Kaymak
Keyword(s):  
Web Ontology Language ◽  
Ontology Language
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