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WIMS’ 14

Providing a context-aware location based web service through semantics and user-defined rules Iosif Viktoratos 1 , Athanasios Tsadiras 1 , Nick Bassiliades 2 , 1 Department of Economics , 2 Department of Informatics, Aristotle University of Thessaloniki GR-54124 Thessaloniki, Greece

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WIMS’ 14

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  1. Providing a context-aware location based web service through semantics and user-defined rules Iosif Viktoratos1, Athanasios Tsadiras1, Nick Bassiliades2, 1Department of Economics, 2Department of Informatics, Aristotle University of Thessaloniki GR-54124 Thessaloniki, Greece {viktorat, tsadiras, nbassili}@auth.gr WIMS’ 14

  2. Contents • Location Based Services (LBS) & Context • Semantic Technologies • The System Geo SPLIS • Design and General idea • Geo SPLIS’s Features • Geos SPLIS’s Architecture • Geo SPLIS’s Operations • Usage Scenarios • Evaluation • Conclusions • Future work

  3. Location Based Services (LBS) • Very popular over the last few years • Usedby millions of people • Navigation • Tracking • Information • Marketing • Entertainment • Emergency • Communication

  4. LBS & Context • Should offer information to users, relevant to their situation - context • Time, Location, User Preferences, Relationships… • Context awareness enables proactive personalized information of higher quality • Researchers and industries focus on contextual knowledge • Hardware (e.g. GPS, sensors) • Software (e.g. ontologies,rules)

  5. Semantic Technologies (1/2)Ontologies • Representation of related concepts (persons’ profiles, location) • High quality context perception • Complicated reasoning about concepts • Formal representation standard • Reusability • Interoperability • Connecting and sharing data from various sources • Flexibility • Knowledge sharing

  6. Semantic Technologies (2/2)Rules • Extensive reasoning capabilities • Reasoning about instances • Intelligence • Increased expressivenessfor complicated inferences • Autonomy - Proactiveness • Data-driven inference • Automatic derivation upon context changes

  7. Geo SPLISGeographicSemanticPersonalizedLocationInformationSystem • What? • Web mapping application for connecting user defined preferences (regarding POIs) with POI owners’ group targeted offers • Why? • To model human daily patterns and provide proactive, customized and contextualized information to users • How? • Combining semantics (rules & ontologies) with LBS

  8. Design and General idea (1/2) POIs adopt a rule-based policy to deploy their specific marketing strategy A restaurant offers 25% discount to students on Friday Regular users provide their profile My job is student Regular users have preferences/daily patterns If it is Sunday noon I would like some restaurants that serve Chinesecuisine The LBS provides spatiotemporal context E.g. its Friday! Combine POI policies with regular user preferences & spatiotemporal context Presents personalized offers on Google Maps 8/36

  9. Design and General idea (2/2)

  10. Geo SPLIS’s Features (1/2) • Collects data from external sources • Google+, Google Places API, POI websites • Regular users add contextualized rule based preferences • Via a web editor • Upload a RuleML file in their Google+ account • POI owners add group targeted offering policies via a web editor • Data from editor  RuleML  Jess Sesame • Executes and evaluates data and rules on the fly • Uses Google Maps for information

  11. Geo SPLIS’s Features (2/2) • Rule conditions • LBS context • Location (e.g. within 1 km) • Weather (e.g. sunny, rainy etc. ) • Time (e.g. between 14:00-18:00) • Day (e.g. Saturday) • Every existing propertyof a POI • E.g. cuisine currently serves • Rule consequences • Add a place in a recommendation list

  12. Geo SPLIS’sArchitecture (1/2) • Client • PC browser-based • Html, JavaScript, Css • Google Maps • Android mobile application • Server • Java Server Pages (JSP) • RDF data management • Sesame • Rules • Reaction RuleML  XSLT  Jess

  13. Geo SPLIS’sArchitecture (2/2)

  14. Geo SPLIS’s Operations(1/11) • Data collection • Information presentation • Operations concerning rules • Rule insertion through editor • Rule insertion through Google+ • Rule update operation • “Get a rule operation” • Planning operation 14/36

  15. Geo SPLIS’s Operations(2/11)Data collection • Ontology loading • Load the schema.org ontology into Sesame for updates • Data update • Get user data either from a registration form or from Google+ account • Obtain user position and retrieve nearest POIsfrom Google Places API • Store data for POIs into Sesame using predefined mapping to schema.org

  16. Geo SPLIS’s Operations(3/11)Information presentation • Data retrieval • Fetch user’s profile data and rules (if any) • Calculate contextual property values (location etc.) • Fetch POIs’ property values and rules (if any) • Rule evaluation • Assert above data to the Jess rule engine • Evaluate rules using the asserted facts • Presentation of personalized information • Bigger in size marker  recommended POI • Green marker  at least one valid offer for the user • Yellow marker  no rule is fired for the current user • Red marker  no offers at all • Red star  POI owner

  17. Geo SPLIS’s operations(4/11)Information presentation layer • Mobile version • PC browser-based version

  18. Geo SPLIS’s Operations (5/11) Rule insertion through editor • Enter the title and the priority of the rule • Four “Add …. Condition” buttons • Each one for the corresponding contextual condition. • The condition customization consists of three elements: • The property field (weather, day, time, distance) • The operator field (“is” and “<”,”>” for time and distance) • The value • An “AND” is implied among them • Drop down menus and read only forms to avoid mistakes • Select the desired POI category • Clicking the “Add Where Condition” button to add more conditions regarding desired POI properties • Add a textual explanation • Assert the rule “If day is Friday and time is before 17:00, I would like to visit some museums”

  19. Geo SPLIS’s operations (6/11)Geo SPLIS’s Web Editor

  20. RuleML representation Jess representation (defrule museums (declare (salience 1)) (place( type Museum) ( uri ?id)) (person ( day friday) ( time ?t) ) (test (< ?t 17)) => (assert (recommendation( id ?id))) (store EXPLANATION " If day is Friday and time is before 17:00, I would like to visit some museums ")) 20/36

  21. Geo SPLIS’s Operations (8/11)Rdf data representation 21/36

  22. Geo SPLIS’s Operations (9/11)Rule insertion through Google+ • Set as label the text “policy_link” • Upload a ruleml link in Google+ • Geo SPIS crawls user page, parses the rules and evaluates them on the fly 22/36

  23. Geo SPLIS’s Operations (10/11)Operations concerning rules • Rule update operation • Edit and delete a rule through editor • “Get a rule” operation • Get rules from other users • Check and get the rule • In case of editing a rule, a user is simply “unlinked” from the rule so that not to affect other users 23/36

  24. Geo SPLIS’s Operations (11/11)Planning operation • A user inserts a future day (1-5 days) • The time of day • The system gets user’s future contextual situation • Geo SPLIS provides POIs and offers regarding the future situation • Usage scenario will presented 24/36

  25. Usage Scenarios (1/6)Normal usage • A scenario concerning a selected user (“Mary”) • Personalized information • Planning operation

  26. Usage Scenarios (2/6)Normal usage • Becauseits Wednesday, 14:25 Mary’s rule 1 is fired • As a result restaurants are represented with a bigger in size marker

  27. Usage Scenarios (3/6)Normal usage • POI “Nama” is represented with a bigger marker because it is a restaurant (rule 1) • Mary is entitled for a special spaghetti price as a student (POI owner’s rule) 27/36

  28. Usage Scenarios (4/6)Normal usage • POI “Pizza Greca” is represented with a bigger marker because it is a restaurant • Mary is not entitled for a special pizza price as a student (POI owner’s rule) 28/36

  29. Usage Scenarios (5/6)Planning mode • Mary will visit Rome on Friday afternoon • Clicks “Planning” button from menu • Selects the following options in the pop window • after 2 days (it’s Wednesday) • the time she will be there (13:00 in our scenario) • As soon as she is inserted into planning mode she can right click on the map in Rome and find places and offers matching the future situation 29/36

  30. Usage Scenarios (6/6)Planning mode • The rule “If day is Friday and time is before 17:00, I would like to visit some museums” is fired (It’s Friday 13:00 o’clock) • Museums are represented with a bigger in size marker • Offers regarding the future situation

  31. Evaluation(1/3) • Quantitative evaluation was made • Geo SPLIS compared with a stripped-down version • Only yellow and red markers • Users had to click on the marker, view POI’s category to understand if it matches his/her preference and read POI owner’s message to understand if their offers matches his/her profile or not • “If day is Wednesday, find me some Coffee shops” • In stripped-down it was just told to users 31/36

  32. Evaluation (2/3) • 44 undergraduate students of Economics (18-22 years old, both genders) • 3 tasks in both systems • Task1.Find how many places match with your preference and contain an offer which also matches with your profile. • Task2.Find how many places contain an offer which matches with your profile. • Task3.Find how many places match with your preference and contain an offer that does not match with your profile. • Equal number of solutions • 25 places • 2 places/solutions to Task 1 • 6 places/solutions to Task 2 • 3 places/solutions to Task 3 32/36

  33. Evaluation (3/3) • Significant difference (p-significant was calculated 0,00131<0,05) 33/36

  34. Conclusions • Geo SPLIS models human daily preferences and connect them with POI owners group based offering policies • Advantages • POI owners have highly targeted marketing audience • Regular users enjoy proactive contextualized information • Adding rules dynamically leads to more customized and personalized user experience • The more rules were added to the system,the more interesting/intelligent it becomes • Exploit people collaboration and social intelligence to create large knowledge bases 34/36

  35. Future work • More extensive user evaluation • Add a social layer • Combine user rules with those of their logged in friends and provide group based information • Manually using friends’ rules • When personal preferences do not fire, then automatically use social graph to recommend • Expand the editor to add a wider range of preferences (movies, music etc.) • Rules (semi-)automatically induced by mining users’ logs, likes or reviews • Connect other social media sources (facebook, twitter etc.) 35/36

  36. Thanks for your time! Geo SPLIS is available at: http://tinyurl.com/GeoSPLIS Mobile version is available at:http://tinyurl.com/GeoSoSPLIS 36/36

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