Integrating Everyting

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Short presentation from a working group at the 2008 social web communities workshop held in September 2008 at the Dagstuhl in Saarbrucken. The presentation discusses the social aspects of the kinds of tools that could be built once a connected web of data was easily mined.

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Integrating Everyting

  1. 1. Closing Triangles at the Café Symantique Harith Alani Ian Mulvany Alexandre Passant Alexander Löser Christian Bizer Peter Mika Nicolas Maisonneauve Ciro Cattuto Christian Bauckhage topics of the talk: - connecting data sources - connecting the real world These connections can be considered as closing triangles across hyper-dimensional networks Key issues raised include:provenance, accurate profiling, disambiguation, privacy, pushing and polling data Will discuss - a real world example - how to do this - what does it mean, and what does it give us in our lives
  2. 2. The talk takes a global view we assumed that all of the nitty gritty problems would be solved (we recognize many of the problems and believe them to be tractable) Wanted to take a more discursive approach.
  3. 3. Let‘s assume you are hungry and you look for a restaurant We wanted to look at a real world scenario to ground our thinking and we settled on this question,
  4. 4. In 1974 you would • Call 2 friends for recommendations ($0,40) • You only reach the one that has no idea • Ask a taxi driver • he recommends you a fast-food place • Stroll through the street … and probably reach the following restaurant Process steps are: Gathering The data Trusting the data Disambiguating Understanding and analysing the data closing triangles
  5. 5. Café Symantique You find yourself, perhaps in an unfamilliar setting, The question is, do you go in to the Cafe ?
  6. 6. • What could we improve with 21th century technology?
  7. 7. Google has answered only some of this for us Finding some places is now easy, but how can we help with the decision on whether we should enter this place? These recommendations donʼt show you how to find unpopular places, they appear off of the front page.
  8. 8. Whats the process? • Gathering The data • Trusting the data • Integration / Disambiguating • Understanding and analyzing the data • closing triangles In 1974 the process of gathering data is easy, but the data is poor, Now merging the data is hard, but the potential for the data quality is high
  9. 9. Gatherin Trustin Integrat Analyz Triangl g g ing ing es del.icio.us An issue with merging data is that the data exists across many different islands - rfid, fire eagle point the way to merging these islands with the real world - we assume that these data sources can be combined
  10. 10. Gatherin Trustin Integrat Analyz Triangl g g ing ing es Trust ? what do you do when you have 34k friends? can we convince people to trust collaborative filters more than their friends?
  11. 11. Gatherin Trustin Integrat Analyz Triangl g g ing ing es Privacy • Social graph fragmentation / delivering issues • Deciding which data you will deliver to whom • oAuth / OpenID / Social networking policies Want to ensure that when we merge data we merge the correct personas
  12. 12. Analyz Triangl Gather Trust Integrate e es • Tag cloud merging – Disambiguation – Individual/Community tag frequency – Tag  Concept – Syntactical analysis • Building profiles of interest How do we understand mixed signals from different sources?
  13. 13. Gatherin Trustin Integrat Analyz Triangl g g ing ing es Itʼs clear that tags taken from more than one source will give us a stronger sense of the ground truth of the personomy of a person
  14. 14. Gatherin Trustin Integrat Analyz Triangl g g ing ing es Rated 5/5 Rated 1/5 Redemption Based-on-Play Android Love Refugee Spacecraft Time-Travel Soldier Famous-Score Hope Alien Blockbuster Alien Broken-Heart Blockbuster Space War Futuristic Based-on-Novel Racism Artificial-Intelligence Hero Melodrama
  15. 15. Gatherin Trustin Integrat Analyz Triangl g g ing ing es Can use semantic tools to help with disambiguation
  16. 16. • But does this tool make you happy? However an important question to ask
  17. 17. C’mon, Be Happy • Hope (… find the secret little grandma style restaurant) • Belonging ( … to the small insider group knowing the secret restaurant) • self esteem (be the first one found it …) • more more, optimization (it took you only 30 minutes … ) • Security (gov reports mean you know the place won’t poison you ) Look to marketing to tell us what the drivers of happiness are
  18. 18. • Not just about friending people • Connect people to places • Connect people to things In our discussions we felt strongly that the web of data is about connecting more than just people to people, that novel, surprising and fun tools could be built on top of the frameworks described at this meeting.
  19. 19. Can we connect a place that you are walking along with a book that you have read? Can you be presented with a piece of music at a location that a friend of yours listened to at some point in the past at that same location? This is a mix between serendipity and reality mining
  20. 20. Can we connect a place that you are walking along with a book that you have read? Can you be presented with a piece of music at a location that a friend of yours listened to at some point in the past at that same location? This is a mix between serendipity and reality mining
  21. 21. Can we connect a place that you are walking along with a book that you have read? Can you be presented with a piece of music at a location that a friend of yours listened to at some point in the past at that same location? This is a mix between serendipity and reality mining
  22. 22. • Adds to the delight in our lives • More Happy, make numinous Can we connect a place that you are walking along with a book that you have read? Can you be presented with a piece of music at a location that a friend of yours listened to at some point in the past at that same location? This is a mix between serendipity and reality mining
  23. 23. How do we map „happy“ as a multi-dimensional-vector? • V = {?,? …. ?} • where ? in {who, what, where, when, why} two key challenges to this community - define the vector of happy cost functions are defined against an assumed need, our needs in this context are not so well defined as we wish to accentuate the element of surprise in the lives of people - easily tie interrogative attributes to triples, or what have you, by context such as person, event, location, time or reason

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