SEMANTIC ENRICHMENT of ROUTING ENGINES USING LINKED DATA: A CASE STUDY USING GRAPHHOPPER

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.