1 / 16

Semantic Web Aided Itinerary Planner

An intelligent system that generates personalized itineraries based on user preferences and trip profiles, using semantic data extraction and querying RDF data sources.

johncastillo
Download Presentation

Semantic Web Aided Itinerary Planner

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Semantic Web Aided Itinerary Planner Rohit Sud Aditya Sakhuja Mayur Bhosle Aditya Devurkar Course: CS8803 AIAD Prof: Ling Liu

  2. Objectives • Provide users with an itinerary that is aligned with their specific interests • Automate the process

  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 visit

  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

  5. System architecture

  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

  7. Algorithm Description • Itinerary Plan Generator where, wi are the weights, xi 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

  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 -

  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

  10. RDF : Description • RDF Extractor module • RDF? • Why RDF? • SPARQL querying. City Name RDF SPARQL Engine City Details

  11. Algorithm Description: RDF • Our Sample Schema in RDF/XML. • <?xml version="1.0" encoding="UTF-8" ?> • <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" • xmlns:ct="http://city.org/Info#" • xmlns:spot="http://city.org/spot#"> • <rdf:Description about=“Some City"> • <ct:name>Atlanta</ct:name> • <ct:latitude>Val_Latitude</ct:latitude> • <ct:longitude>Val_Longitude</ct:longitude> • <ct:incityspots> • <rdf:Description about="s1"> • <spot:name>s1</spot:name> • <spot:famousfor rdf:resource="Athletics"/> • <spot:famousfor rdf:resource="Trekking"/> • <spot:bestvisittime rdf:resource="morning"/> • <spot:spenttime rdf:resource="6 hr"/> • <spot:costsyou rdf:resource="$5000"/> • </rdf:Description> • </ct:incityspots> • </rdf:RDF>

  12. Generating User Reports • The result of the trip is published on a Google Map • We use the Google Map JS API for it.

  13. Using Google Maps API • Clicking on maximize displays information about the place from RDF data source.

  14. 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

  15. 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

  16. Questions?

More Related