# On Ontology-Based Tourist Knowledge Representation and Recommendation
## 1 Introduction  
Many travelers enjoy sharing their experiences or knowledge with others, and this can then be used as a reference by others when planning their trips, thus enabling them to learn more about the attractions they are going to visit [1]. Tourist knowledge is thus defined in this work as the opinions, experiences, and appraisals of travelers regarding target tourist attractions The most commonly used form of traveler knowledge is now online travel reviews (OTR), which are shared via virtual communities or blogs [2; 3; 4]. The value of tourist knowledge in OTR can be raised if it is first analyzed,so that the positive appraisals of attractions can then be read by users. Nevertheless, there are some problems that knowledge engineers must address in order to analyze the data in OTR. First and foremost, OTR are usually posted on a variety of virtual communities or blogs and stored as webpage content rather than filed documents, making it difficult to extract, manage, analyze, and apply the tourist knowledge they contain. Ontology is a methodology used to organize knowledge. Using classification and hierarchy, an ontology is able to store and express the knowledge in a certain field. A number of researchers have applied ontology to express tacit knowledge. For example, [PERSON] and [PERSON] applied it to analyze the discussions on web forums, in order to identify the evolution of discussion topics [5]. [PERSON] et al. employed ontology to examine the knowledge structure of electronic word-of-mouth (eWOM) [6]. Such research primarily utilizes ontology to express tacit knowledge, and since OTR usually contains knowledge related to tourist opinions and experiences, it can be seen as this kind of knowledge. Therefore, OTR may also be expressed via an ontology. Similarly, [PERSON] et al. applied an ontology to classify web content [7], while [PERSON] et al. employed this approach to develop an automatic summarization mechanism for web news [8], and other studies have used ontology to develop a platform for semantic inquiry [9; 10]. Sine OTR is a kind of web content, the ontology structure may also be applied to this, and thus this is the approach adopted in the current study. The most common form of web search is based on keyword searches, but this can lead to the problem of information overload [11; 12; 13; 14]. This means that travelers may need to browse a huge amount of content to find what they are looking for, which makes efficient tour planning and decision-making more difficult. In addition, when travelers do not know the names of popular attractions, they cannot use the correct keywords to search for attractions. If the user replaces keywords with their current location, virtual communities, blogs, and search engine can only provide the relevant content based on keyword; but cannot find the information about the other attraction. To address this issue, tourist businesses have developed a large number of package tours that they offer on the Internet [15], but these still require considerable time and effort to be compared in order to find the most suitable package [16; 17; 18]. Moreover, the package tours provided by tourist businesses may not satisfy consumer needs and are usually not flexible or subject to slight modifications. In addition, some travelers simply do not like to plan their tour in advance, but do so in real-time, while they are on their vacation. This is when a tourist attraction recommendation system can be especially useful, and there are currently many such systems. These can search for and recommend nearby attractions based on the travelers' location, as seen with the Touriseye mobile application. Nonetheless, these applications mostly rely on information shared by other users and do not provide access to information from other platforms, making comprehensive tourist information hard to obtain. Two problems faced by tourist information systems are thus how to offer sufficient information to travelers while also reducing the amount of time needed to examine it.