Abstract
Abstract. Traveling is a basic part of our daily life, whenever a person wants to travel e.g. from home to workplace, the essential question that rises is which route to follow. The choice of a route also varies based on traveler’s interest e.g. visiting hospital on way back to home or traveling on a greener route. This varied route planning may be easy for any person in his local neighborhood, however in a new neighborhood and increasing number of options e.g. possible restaurant options to visit, a guiding system is required that suggests an optimal route according to traveler’s interests i.e. answering semantic queries. Most of the existing routing engines only answer geometric queries e.g. shortest route due to lack of data semantics and adding semantics to a routing graph requires a semantic data source. Geo-semantics can be added through combination of GIS and semantic web. Semantic web is an extension of World Wide Web (WWW) where the content is maintained and structured in a standard way that is understandable by machines; hence providing linked data as a way for semantic enrichment, in this study the semantic enrichment of routing dataset. To use this semantically enriched routing network a routing application needs to be developed that can answer the semantic queries. This research serves as a proof of concept for how linked data can be used for semantic enrichment of routing networks and proposes a prototype routing framework and application designed using open source technologies along with use cases where semantic routing queries are addressed. It also highlights the challenges of this approach and future research perspectives.
Highlights
Traveling is a basic part of our daily life, whenever a person wants to travel e.g. from home to workplace, the essential question that rises is which route to follow
In case the user selects the within search buffer of the shortest/fastest route option, first of all the shortest/fastest route is calculated for the origin/destination specified, search buffer is applied on it to get the buffer polygon which is passed to the linked data query, shortest/fastest routes are calculated for all the Points of Interest (PoI) returned by the query and are displayed to the user for selection, display on map and routing
The working of prototype semantic routing application is discussed in detail with figures
Summary
Traveling is a basic part of our daily life, whenever a person wants to travel e.g. from home to workplace, the essential question that rises is which route to follow. The choice of a route varies based on traveler’s interest e.g. visiting hospital on way back to home i.e. having a hospital as a waypoint. Incorporation of traveler’s interest requires a data set which can be used to add semantics to geometric routing graphs and answer semantic queries. Adding this information to a spatial dataset leads us to semantic enrichment (Karastoyanova et al, 2007) which would add meaning to routing edges and nodes. Semantic enrichment can be done by statically binding the information of Points of Interest (PoI) to respective edges (Fileto et al, 2015) based on proximity, this results in a static relationship between PoI and routing network, where the reflection of updated relations becomes very difficult. Research results conclude that parameters like geographical proximity and textual similarity should be observed and fine-tuned carefully between movement segment and PoI to associate with them
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