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Semantic Web Aided Itinerary Planner Rohit Sud Aditya Sakhuja Mayur Bhosle Aditya Devurkar Course: CS8803 AIAD Prof: Ling Liu Slide 2 Objectives Provide users with an itinerary that is aligned with their specific interests Automate the process Slide 3 Background and motivation Complete Vacation Planners like :- www.tourguidemike.com The results are not personalized. User plans his own itinerary:- www.mapquest.com Hence Need for a system which relieves the user from manually planning a trip Services like (www.wikitravel.org) provide excellent and extensive information about almost all places to visitwww.wikitravel.org Slide 4 Putting them together: Research Problems An intelligent itinerary generator. The current systems are static in nature We propose an intelligent system which takes decisions inferred from the available knowledge using user's profile and trip profile Semantic data Extraction Major challenge due to area being unexplored. RDF Querying RDF data sources SPARQL Effectively identifying the relevant factors for the itinerary generator Slide 5 System architecture Slide 6 Algorithm Description Itinerary Plan Generator : Inputs Static data User Profile (Interests) Trip data Start/end date, starting location, min cities to cover, cost limits, activities, location category Distance Matrix Similarity Matrices Traveler City Similarity Matrix City City Similarity Matrix Location specific weather, traffic reports Slide 7 Algorithm Description Itinerary Plan Generator where, w i are the weights, x i are the factors The factors for first level of pruning - Interest Category mapping Cost factor = ( Expected Cost Limit ) Travelling time Location Hotness Rating = Ratings i / N ; where N - Users Slide 8 Algorithm Description Itinerary Plan Generator Traveller-City similarity matrix Similarity equation factors - Events : Activity mapping City category : User interest mapping Spot category : User interest mapping Calculation the score - Slide 9 Algorithm Description: Feedback Feedback Is the user satisfied with the results ? Is the user happy with the weather and traffic updates ? Traffic Sunnyvale Weather Sunnyvale Slide 10 Algorithm Description : Cases We store all the results in the form : : If the number of such cases in the memory (Knowledge Base) exceeds a certain threshold Case based reasoning can also be performed. Slide 11 RDF : Description RDF Extractor module RDF? Why RDF? SPARQL querying. SPARQL Engine RDF City Name City Details Slide 12Atlanta Val_Latitude Val_Longitude s1"> Algorithm Description: RDF Our Sample Schema in RDF/XML.Atlanta Val_Latitude Val_Longitude s1 Slide 13 Generating User Reports The result of the trip is published on a Google Map We use the Google Map JS API for it. Slide 14 Using Google Maps API Clicking on maximize displays information about the place from RDF data source. Slide 15 Evaluation of Results Leave one out test to evaluate the correctness of the algorithm Freshness of results is ensured by giving dynamic data to the user User Satisfaction is measured using the feedback loop Slide 16 References [1] http://www.tourguidemike.com/ [2] http://www.lonelyplanet.com/ [3] http://www.mapquest.com [4] http://www.tripit.com/ [5] "Crumpet: Creation of user-friendly mobile services personalised for tourism", Stfan Poslad,Heimo Laamanen, Rainer Malaka, Achim Nick, Phil Buckle and Alexander Zip [6]Cyberguide: A Mobile context aware tour guide. [7] http://www.travelok.com/ [8] http://wikitravel.org/en/Main_Page [9] http://www.holidayandtravelguide.com/ [10] http://www.holidaytraveldestinations.com Slide 17 Questions?