Closing Triangles at the Café Symantique

Harith Alani

Alexandre PassantAlexander Löser

Christian Bizer

Ian Mulvany

Peter Mika

Christian Bauckhage Nicolas

Maisonneauve Ciro Cattutotopics of the talk: - connecting data sources - connecting the real worldThese connections can be considered as closing triangles across hyper-dimensional networksKey issues raised include:provenance, accurate profiling, disambiguation, privacy, pushing and polling dataWill discuss - a real world example - how to do this - what does it mean, and what does it give us in our lives

The talk takes a global viewwe 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.

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,

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

Café Symantique

You find yourself, perhaps in an unfamilliar setting, The question is, do you go in to the Cafe ?

• What could we improve with 21th century technology?

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.

• Gathering The data

• Trusting the data

• Integration / Disambiguating

• Understanding and analyzing the data

• closing triangles

Whats the process?

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

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Gathering

Trusting

Integrating

Analyzing

Triangles

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

Trust ?

Gathering

Trusting

Integrating

Analyzing

Triangles

what do you do when you have 34k friends?can we convince people to trust collaborative filters more than their friends?

Privacy

• Social graph fragmentation / delivering issues

• Deciding which data you will deliver to whom

• oAuth / OpenID / Social networking policies

Gathering

Trusting

Integrating

Analyzing

Triangles

Want to ensure that when we merge data we merge the correct personas

• Tag cloud merging

–Disambiguation

– Individual/Community tag frequency

–Tag Concept

– Syntactical analysis

• Building profiles of interest

Gather Trust Integrate Analyze

Triangles

How do we understand mixed signals from different sources?

Gathering

Trusting

Integrating

Analyzing

Triangles

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

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Alien

FuturisticBlockbuster Alien

Time-Travel

WarSpace

Spacecraft

Artificial-Intelligence

Soldier

Redemption Android

BlockbusterBased-on-Novel

Based-on-Play

Famous-Score

Melodrama

Broken-Heart

Hero

LoveHope

Racism

Refugee

Gathering

Trusting

Integrating

Analyzing

Triangles

Gathering

Trusting

Integrating

Analyzing

Triangles

Can use semantic tools to help with disambiguation

• But does this tool make you happy?

However an important question to ask

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

• 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.

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

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

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

• 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

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