Semantic Matching and Heuristic Search for a ... - Semantic Scholar
Semantic Matching and Heuristic Search for a Dynamic Tour Guide Klaus ten Hagen Ronny Kramer Marcel Hermkes Bjoern Schumann Patrick Mueller Department of Computer Science University of Zittau-Goerlitz, Germany [email protected] Abstract
A lack of information forces many tourists on the same crow Guide (DTG) is a mobile agent w a couple of hours to explore a city. For this the DTG interrogates Tour Building Blocks (TBB), e.g. potential sights or restaurants, to determine current information, e.g. opening hours or availability. An ontology is used to capture the profiles of the TBBs and the interests of a tourist. Both are used by a semantic match algorithm to rank the TBBs. Based on the start point and the available time period a heuristic approximate algorithm computes an individual tour w Benchmarks of relevant complexity have show mputed in 5 seconds deviates only by 5% from the optimal tour.
Keywords: ontology, semantic matching, dynamic t our guide, heuristic search, location based service, sustainable tourism, context-aw e, mobile computing, intelligent agents
1
Introduction
Unfortunately too many tourists end-up on the beaten tracks. Interesting sights just a couple of hundred yards off the main tourist arteries are rarely visited. Restaurants on the tourist pathw hers get little customer traffic. The dream of a tourist is to have a local guide, w know This is the objective of the Dynamic Tour Guide (DTG). The DTG is a softw accessible through a mobile device. The DTG relies on w information about each Tour Building Block (TBB) and a w personal tour. A TBB is a station of the tour. It can be a sight like a building or a service provider like a restaurant. The mobile device is aw DTG maintains a personal interest profile based on an ontology. This ontology is used as w time period and the DTG w e TBBs available at this destination, interrogate the corresponding w to update the current information and then use a w
map and after optional modifications the DTG w information. The DTG addresses the most urgent information needs of a tourist, w (Schmidt-Belz, 2003a). Last, but not least the DTG w provide exposure to a broader range of service providers than are “visible” to most tourists today.
The next section discusses related w as w the follow be introduced afterw benchmark results w ill be presented. The article summarizes the contribution and concludes w future research.
2
Related Work
The trip planning problem (TPP) (Godart, 2003) deals w couple of days w al of the DTG is to compute a tour for a couple of hours to explore a single destination. Therefore lodging isn’t included. The DTG neglects the cost of transportation and any other activities. The DTG accounts for the time spent to travel betw an average staying time and a profile based on a common ontology. A semantic match betw Interest Matching Points (IMP). The IMP is equivalent to the “attractiveness of an activity” in the TPP. The DTG is used by a mobile tourist and hence it needs to present at least one acceptable tour in less than 5 seconds. In (Lopez, 2003) “Holiday Scheduling” is defined as the problem to select activities at a single destination for a “single stay”. The planning challenges in tourism can be categorized by the time horizon for a tour (
A lack of information forces many tourists on the same crow Guide (DTG) is a mobile agent w a couple of hours to explore a city. For this the DTG interrogates Tour Building Blocks (TBB), e.g. potential sights or restaurants, to determine current information, e.g. opening hours or availability. An ontology is used to capture the profiles of the TBBs and the interests of a tourist. Both are used by a semantic match algorithm to rank the TBBs. Based on the start point and the available time period a heuristic approximate algorithm computes an individual tour w Benchmarks of relevant complexity have show mputed in 5 seconds deviates only by 5% from the optimal tour.
Keywords: ontology, semantic matching, dynamic t our guide, heuristic search, location based service, sustainable tourism, context-aw e, mobile computing, intelligent agents
1
Introduction
Unfortunately too many tourists end-up on the beaten tracks. Interesting sights just a couple of hundred yards off the main tourist arteries are rarely visited. Restaurants on the tourist pathw hers get little customer traffic. The dream of a tourist is to have a local guide, w know This is the objective of the Dynamic Tour Guide (DTG). The DTG is a softw accessible through a mobile device. The DTG relies on w information about each Tour Building Block (TBB) and a w personal tour. A TBB is a station of the tour. It can be a sight like a building or a service provider like a restaurant. The mobile device is aw DTG maintains a personal interest profile based on an ontology. This ontology is used as w time period and the DTG w e TBBs available at this destination, interrogate the corresponding w to update the current information and then use a w
map and after optional modifications the DTG w information. The DTG addresses the most urgent information needs of a tourist, w (Schmidt-Belz, 2003a). Last, but not least the DTG w provide exposure to a broader range of service providers than are “visible” to most tourists today.
The next section discusses related w as w the follow be introduced afterw benchmark results w ill be presented. The article summarizes the contribution and concludes w future research.
2
Related Work
The trip planning problem (TPP) (Godart, 2003) deals w couple of days w al of the DTG is to compute a tour for a couple of hours to explore a single destination. Therefore lodging isn’t included. The DTG neglects the cost of transportation and any other activities. The DTG accounts for the time spent to travel betw an average staying time and a profile based on a common ontology. A semantic match betw Interest Matching Points (IMP). The IMP is equivalent to the “attractiveness of an activity” in the TPP. The DTG is used by a mobile tourist and hence it needs to present at least one acceptable tour in less than 5 seconds. In (Lopez, 2003) “Holiday Scheduling” is defined as the problem to select activities at a single destination for a “single stay”. The planning challenges in tourism can be categorized by the time horizon for a tour (
