User Profile ontology with social network and historical records elements

1. User Profile ontology with social network and historical records elements Anatoliy Kim

2. Table of Contents • Introduction • Motivation • Goal • Related Works • User Profile Ontology • Modelling User • Modelling User Context • Contribution • Algorithm • Usage scenario • Tools • Inferring and rating • Issues and problems • Privacy • Modelling historical records • Modelling social network elements

3. Introduction

4. Motivation • Smartphone is a very widespread device, capable of accessing Internet and running software • There is a great amount of web services that utilize some user information • But user profiles are insufficient to support many of services and apps • Social network enabled functions • Location aware apps • Context aware services

5. Goal • Provide ontology based user profile, which also supports historical information, various user preferences and social networking functions Profile Preferences Interests Personal data History Social

6. Goal • Provide ontology based user profile, which also supports historical information, various user preferences and social networking functions Profile • Name, Age, Gender • Date of Birth, SSID, Health Insurance, Address, Tel phone, Blood type, Photo, Military training • Email, Cell phone • Height, Eye color, Hair color, Weight, Health problems or disabilities • Abilities, Skills • Education, Expertise, Profession Preferences Interests Personal data History Social

7. Goal • Provide ontology based user profile, which also supports historical information, various user preferences and social networking functions Profile Preferences Interests Personal data • Literature -> Fiction, Detective, Fantasy, Love story • Movie -> Animated, Action, Adventure, TV-show • Sports -> Soccer, Golf, Hockey, Racing • Arts -> 18th century, 19th century, Renaissance, Modern • Hobbies -> Photography, Cooking, Design • Business -> Finance, Investing, Accounting History Social

8. Goal • Provide ontology based user profile, which also supports historical information, various user preferences and social networking functions Profile • Likes cats • Likes color green • Dislikes “heavy” music style • Likes dancing • Likes vanilla ice-cream • Dislikes green tea • Dislikes dogs Preferences Interests Personal data History Social

9. Goal • Provide ontology based user profile, which also supports historical information, various user preferences and social networking functions • Attended piano class • Worked in McDonalds • Attended Google Summer Camp • Internship in Samsung • Published paper on “Ontology” • Changed address and phone info • Changed country Profile Preferences Interests Personal data History Social

10. Goal • Provide ontology based user profile, which also supports historical information, various user preferences and social networking functions • became friends with “Adam Smith” • joined “KAIST student” group • recommends “Sushi bar in Dunsan” • is listening to “Nightwish” music album • attends “SNSD concert in Daejeon” event • gives 9 out of 10 for food quality and assortment in “Paris Baguette” Profile Preferences Interests Personal data History Social

11. Related Works

12. User Profile Ontology • M. Golemati et al, “Creating an ontology for user profile: method and application” • User profile ontology • Available at: http://tinyurl.com/5tevzo7 • Can be used as a starting point • This work provides a general guideline for user profile ontology creation • Is interesting to see how to structure such things as “Interest” or “Expertise”

13. Modelling user • E. Rich, “Users are individuals: individualizing user models” • Modelling a user • Available at: http://tinyurl.com/4ztrz5r • Some specifics about user modelling • 3 dimensions • Single model of canonical user vs multiple individual user models • Explicitly specified models vs inferred by system models on the basis of user behavior • Long term user characteristics models vs short term characteristics models

14. Modelling User Context • M. Tazari et al, “Modelling User Context” • Some specifics about context modelling • Available at: http://tinyurl.com/6bzsbk2 • Change of value • Static - hardly changing, e.g. resolution of a display • Dynamic – changing, e.g. time and location • Scope • Local - associated with a single object • Global - correlating multiple objects, i.e. location of user • User Context equals sum of • Profiles of resources - available devices, services, documents • Profiles of locations – location, resources, people present at location • Current time • Explains some theory behind user context

15. Contribution • Social network enabled user profile • Jim recommends his friends to read “Harry Potter” book. • Same as user profile record “Jim liked Harry Potter book”, shared to friends • Historical records in user profile • Changed address: Uzbekistan > Korea, September 2009 • Current record of address in user profile: Korea • Historical record of address: Uzbekistan • Date of change: September 2009 • Respond to queries about address and understand about availability of previous information

16. Algorithm

17. Usage scenario • Jim is a student. This morning he received a call to visit University Records Department to fill in some form. • The form requires to fill in a large amount of data, and while some of it is easy to complete (Name, Date of Birth) there are also many fields Jim does not remember about. Going to home to gather all the required documents seems to be the only way. • However, with this software, Jim can use his smartphone to access his profile, which contains the required information, such as his complete health status, classes he has taken in the previous educational institution, digital photo and many more. • Jim can use the program to connect to local Wi-Fi server and download the required fields, which the program will further correlate with information in his profile and display the data for him to fill in. Jim will approve to send this data to the server and continue filling in the updated form where almost all fields are already filled in. • In the evening, Jim comes to a big restaurant. The menu contains many dishes, so it is quite difficult to choose. Jim can use his smartphone to connect to the local server and download the list of dishes, the smartphone can also connect to the server in the Internet and access his social network account, which then gives him information if some of the dishes are recommended (like/dislike) by his friends. With that information Jim can safely choose which dishes are good to order, and which might be better to avoid.

18. Usage scenario (revised) • “Jim can use the program to connect to local Wi-Fi server and download the required fields, which the program will further correlate with information in his profile and display the data for him to fill in.” • Local server may provide the requirement list via ontology and name the photo of the User as “Image”. • So, the task of the software is to correlate ontology of the local server with ontology of user profile and find that “Image” is in fact “User Photo” (owl:sameAs) • “Jim can use his smartphone to connect to the local server and download the list of dishes, the smartphone can also connect to the server in the Internet and access his social network account, which then gives him information if some of the dishes are recommended (like/dislike) by his friends.” • The list provided by the local server at the restaurant contains a dish with Tuna under “Fishes” section. The ontology at Internet however only have “Seafood” category, which is superclass to “Fishes”. Some of Jim’s friend might have “liked” “Tuna” dish, and some just like “Seafood” in general. This all information should be used to figure out how much Jim’s friends like the particular “Tuna” dish.

19. Tools • For ontology creation • Protégé-Owl version 4.1beta • For reasoning • Pellet OWL 2 version 2.2 (integration with protege)

20. Inferring and rating • 1st statement: Jim’s friend Clark likes “movies” • 2nd statement: Jim’s other friend John likes “Harry Potter movie” • 3rd statement: Jim’s one another friend Joe likes “entertainment” • Ontology: • Entertainment -> Movies -> Harry Potter movie • Should Jim watch the Harry Potter movie, if only one statement is available? • Statement 1 should have less weight then Statement 2 • Statement 3 should have even less weight then Statement 1 • Rate accordingly among all friends and find out the combined score.

21. Issues and problems

22. Privacy issue • Issue: Which information available in user profile should be accessible to external/local services and applications? • Severity: critical • If the user doesn’t have control about what happens with his data, he will never input any into the profile. • Resolution • Define levels of information privacy – public, emergency, private • Any time non-public information is requested – alert user to make final judgment.

23. Modelling historical records • Problem: how to integrate time information for historical records into user profile? • Solution: • Differentiate between static and dynamic information, like in Tazari et. Al • Make a property with time information and class of ValueWithTime, which will be sub-classed by any data that is changing

24. Modelling social network elements • Problem: how to integrate social elements into user profile? • Resolution: • Iterative approach • Make ontology version 1 alpha • Test using application • Note the problems • Make another ontology version