Research On Geographic Information-based Tourism Information Services

In Web2.0 era, with the rapid development of mobile Internet, intelligent electronic products such as high-resolution smartphones, digital cameras and GPS navigation systems are widely used in various fields, thus bringing great changes in our daily lives. Then a variety of social networks and social software APPs appear, meanwhile the rapid development of location-based social media makes people incline to share their daily lives on the Internet with pictures and texts, especially what one sees and hears during travels. These social media data usually consist of textual tags, geotags (latitude and longitude), taken time and so on, and provide real data for the study of tourism services.Most traditional tourism services take a long time to make plans based on mass tourism experiences. They do not take full advantages of information technology, and can’t provide targeted recommendation services for tourists. Intelligent tourism mainly exploits information technology to integrate tourism resources involving food, accommodation, transportation, scenery spots, purchase, entertainment and so on. This paper uses ontology for data modeling, storage and matching to solve the information sharing and interaction in semantic level, and analyzes the framework of tourism services with the description logic reasoning of task ontology. Currently the analysis of points of interest for tourism is the rising field of intelligent tourism. In order to provide better recommendation services for tourists, this paper has done the following work based on geotags:(1) Propose a tourist information service based on task ontology. Due to the diversity of tourism data, this paper firstly introduces ontology concept to integrate the tourism resources and create tourism ontology. Then this paper constructs common task models and ontologies according to the needs and activities in the perspective of tourists. Finally this paper proposes a tourism information service framework based on task ontology. It makes real-time decisions with needs and context-aware information, and provides a flexible way for tourist information retrieval.(2) Analysis of points of interest (POIs) based on geotags. Firstly we use geotagged photos from Flickr as data sources, and use ontology models to store data into ontology database, and improve the clustering constraints according to the tourism features, and select the optimal parameters, and exploit P-DBSCAN algorithm to find POIs, and get the reasonable results of clustering, and then we analyze the photo text information to name points of interest. Secondly we get records of each tourist and create ontology database to lay the data foundation. Finally we use Markov model to analysis travel trajectories and provide advices for transport management.(3) Research of tourism recommendation. This paper uses the visit frequency of tourists and probabilities of photos for users as final weight of preferences from ontology database instead of traditional ratings, then builds trust network among users, applies the trust into the calculation of user similarity. Also this paper combines the popularity of POIs and time context-aware information to sort weighted points of interest in order to recommend reasonable POIs and tourism sequence for users. Experiments in this paper show that the proposed algorithms can improve the precision and accuracy of recommendation.