Social Networks, the Semantic Web, and the Future of Online Scientific Collaboration

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Social Networks, the Semantic Web, and the Future
of Online Scientific Collaboration

• Jennifer Golbeck
• University of Maryland, College Park

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Overview

• What is the Semantic Web?
• How can it help us do science?
• About Web-based Social Networks
• Combining the Semantic Web, Social Nets, Science,
  and Provenance

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What is the Semantic Web

• Extension of the current web
• Make information machine processable
• Supported at the W3C

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Current Web to Semantic Web

• HTML is designed to make documents on the web
  easy to read for humans
• Computers have difficulty understanding what is
  on the web
• We do ok with keywords for text
• What about videos, pictures, songs, data?

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Stuff We Want

• Find me the mp3 of a song that was on the
  Billboard top 10 that uses a cowbell
• Show me the URLs of the blogs written by people
  my friends know
• Get a video where its snowing
• All of this is hard to do on the web as it stands

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Making it Easier

• On the Semantic Web, data is represented in a
  machine readable standard format
• Some created automatically, some by humans
• Ontologies add semantics
• Each datum is uniquely identified by a URI
• Distributed data can be aggregated and integrated
  into one model

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Semantic Web Technologies

• URIs
• Ontologies
• Standard Languages
• RDF
• RDFS
• OWL
• SPARQL

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Example A Video of it Snowing

• On the Semantic web, people will annotate their
  data, but they wont annotate everything
• If my video is of two government officials
  meeting, the weather may be irrelevant to me
• How can the semantic web solve this? Do people
  have to annotate everything?

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Linking Distributed Data
Location
Camera Info
Date
President
Video
More data
Prime Minister
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Data Aggregation

• URIs are unique.
• If the same URI is used in two files, it refers
  to the same object
• Semantic Web tools (e.g. things like databases
  that understand the semantics of the languages)
  build models that merge information about the
  same URI
• Model can be queried, filtered, used

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Semantic Web for Science
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Provenance

• The history of a file or resource
• Files that were used in its creation
• Processes executed to create it
• When, where it was created
• Who created it

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Why is it important?

• People in the scientific and intelligence
  communities are very interested in provenance
• Science provenance of data can be used to
  recreate them
• Intelligence provenance of information is
  important to determine its reliability

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Example in Science

• We want to track the workflow that lead to a
  given scientific image
• What were the files used to create it?
• What is the provenance of those files?
• What process was performed to create the file?
• When was that file created?
• Who executed the processes?

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Case Study A Semantic Web Approach to the
Provenance Challenge
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The Provenance Challenge

• Tracking provenance is a growing topic of
  interest to computer scientists
• Applications to grid computing, file systems,
  databases, etc
• The challenge is to build a system that will
  track the provenance of files produced from a
  workflow
• Series of procedures performed to produce output
• functional Magnetic Resonance Imaging (fMRI) is
  the example in the challenge

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Challenge

• Represent all data that we consider relevant
  about the history of each file
• Answer as many queries as possible

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Queries

• Find everything that caused a given Graphic to be
  as it is.
• Find all invocations of procedure align_warp
  using a twelfth order nonlinear 1365 parameter
  that ran on a Monday.
• Find all images where at least one of the input
  files had an entry global maximum4095.
• A user has annotated some images with a
  key-value pair centerUChicago. Find the outputs
  of align_warp where the inputs are annotated with
  centerUChicago.

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Semantic Web Approach

• Each procedure in the workflow is encoded as a
  web service
• Workflow is an execution of a series of web
  services
• Web Services take files as input and output files
  to the web

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Semantic Web Approach

• Ontology represents information about the
  execution of services and the dependencies of
  files

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Provenance.owl
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Answering the Queries

• SPARQL, a W3C standard, is used to formulate
  queries
• Reasoning with the semantics of OWL and some rules

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Results

• We were easily able to answer all nine queries
  for the challenge
• Semantic Web is an easy and natural format for
  representing the provenance of scientific
  information
• So, with a format for representing data and
  metadata, what next?

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Social Networks The Phenomenon
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What are Web-based Social Networks

• Websites where users set up accounts and list
  friends
• Users can browse through friend links to explore
  the network
• Some are just for entertainment, others have
  business/religious/political purposes
• E.g. MySpace, Friendster, Orkut, LinkedIn

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Growth of Social Nets

• The big web phenomenon
• About 150 different social networking websites
  (that meet the definition that they can be
  browsed)
• 275,000,000 user accounts among the networks
• Number of users has doubled in the last 18 months
• Full list at http//trust.mindswap.org

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Biggest Networks

• MySpace 120,000,000
• Adult Friend Finder 23,000,000
• Friendster 21,000,000
• Tickle 20,000,000
• BlackPlanet 17,000,000
• Hi5 14,000,000
• LiveJournal 10,000,000
• Orkut 8,500,000
• Facebook 8,000,000
• Asia Friend Finder 6,000,000

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Social Networks on the Semantic Web

• FOAF (Friend Of A Friend)
• A simple ontology for representing information
  about people and who they know
• About 20,000,000 social network profiles are
  available in FOAF format
• Approximately 60 of all semantic web data is
  FOAF data

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Structure of Social Nets

• Small World Networks
• AKA Six degrees of separation (or six degrees of
  Kevin Bacon)
• Term coined by Stanley Milgram, 1967
• Math of Small Worlds
• Average shortest path length grows
  logarithmically with the size of the network
• Short average path length
• High clustering coefficient (friends of mine who
  are friends with other friends of mine)

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Trust in Social Networks

• People annotate their relationships with
  information about how much they trust their
  friends
• Trust can be binary (trust or dont trust) or on
  some scale
• This work uses a 1-10 scale where 1 is low trust
  and 10 is high trust
• At least 8 social networks have some mechanism
  for expressing trust explicitly, several dozen
  have implicit trust information

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Using Trust from Social Networks

• If we have trust available from a social network,
  how can we use that?
• Trust in people can influence how likely we are
  to
• Give them access to information
• Accept information from them at all
• Consider the quality of information from them

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Examples

• Only people I trust can see my phone number
• I will only accept emails from people I trust

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Challenges to Using Trust

• Each person only knows a very very small part of
  the network
• For people we know, some automatic use of trust
  may be helpful, but it does not provide any new
  information
• If we have access to the network, we need a way
  to compute how much we should trust others

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Inferring Trust
The Goal Select two individuals - the source
(node A) and sink (node C) - and recommend to the
source how much to trust the sink.
tAC
A
B
C
tAB
tBC
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Caveats and Insights

• Trust is contextual
• Trust is asymmetric
• Trust is not exactly transitive

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Trust Algorithm

• If the source does not know the sink, the source
  asks all of its friends how much to trust the
  sink, and computes a trust value by a weighted
  average
• Neighbors repeat the process if they do not have
  a direct rating for the sink

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How Well Does It Work?

• Pretty well
• On networks where we have tested it, trust is
  computed accurately within about 10
• Test this by taking a known trust value, deleting
  the edge between those people, comparing the
  known value with the value we compute
• 10 is very good for social systems with lots of
  noise

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Applications of Trust

• With direct knowledge or a recommendation about
  how much to trust people, this value can be used
  as a filter in many applications
• Since social networks are so prominent on the
  web, it is a public, accessible data source for
  determining the quality of annotations and
  information

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Ordering

• Use trust to determine the order in which
  information is presented
• Aggregating
• If data is aggregated, we can use trust to
  determine how much weight is given to different
  sources

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Social Networks for Science

• Data Provenance Social Networks Social
  Policies

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Policies on the Web

• Policies on the web are used to filter and
  restrict access to information for
• Security
• Privacy
• Trust
• Information filtering
• Accountability
• Important because of the open nature of the web

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Applications of the policy aware web

• Website access
• Network routing
• Storage management
• Grid computing
• Pervasive computing
• Information filtering
• Digital rights management
• Collaboration

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Applications and Industrial Interest

• Internet Content Rating Agency
• Using policies and rules to develop content
  ratings for websites
• Efforts underway at
• Microsoft, IBM, Sun, BEA, Oracle
• Heavily discussed at W3C Workshop on Constraints
  and Capabilities for Web Services
• http//www.w3.org/2004/09/ws-cc-program.html

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Example Policies

• Only allow members of my research group to access
  this data set
• Reject messages from anyone whose address is not
  on my list of verified senders

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Policies and Trust

• Only users whose inferred trust rating is a 9 or
  10 may run processes on this shared computing
  resource
• Access to preprints of this paper are accessible
  only to trusted Fermilab personnel, members of
  the research team at other institutions, or the
  NSF advisory board
• Include information in my knowledge base only if
  it, and all the files and processes in its
  provenance, were created or executed by people I
  trust at a level 7 or above

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Extending Trust to Science

• In collaborative scientific environments, some
  data and resources require strict access control
  (username / password)
• For others, this level of control is unnecessary
  and cumbersome

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Trust for Access Control

• With a scientific social network, trust can be
  used to restrict access to
• Data
• Computing resources
• and
• Limit what data is integrated into a knowledge
  base
• Weight conflicting information from different
  sources according to the trustworthiness of the
  source

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Leading to Collaboration

• The semantic web with social networks provides a
  platform for
• Publishing data
• Publishing metadata (so experiments can be
  verified)
• Limiting/granting access to sensitive data
• Gathering data from other sources
• Filtering data from the web

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What do we need to do?

• Easy Steps
• Building ontologies for representing scientific
  data / metadata
• Publishing data on the web

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What do we need to do?

• Hard Steps (because people dont want to do it)
• Developing web policies for limiting access to
  non-critical data
• Webmasters can do this, with training and
  collaboration with data owners
• Motivating scientists into social networks

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Forcing the Anti-Social Into Social Nets

• Cant expect scientists to use a Facebook/MySpace
  style social network
• (and we probably dont want to see that anyway)
• Integrate social networking into other activities
• E.g. email

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The Payoff

• A whole new way of working over the web
• Multiple levels of collaboration
• New ways of sharing data and working together

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Conclusions

• The intersection of the Semantic Web, social
  networks, and science holds great promise for
  revolutionizing collaboration over the web
• Steps to achieving it are mostly social, not
  technological
• Motivating the use of these technologies among
  everyone involved with data
• Introducing new ways to collaborate and
  encouraging adoption of new techniques

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Questions

• Jennifer Golbeck
• Golbeck_at_cs.umd.edu
• http//trust.mindswap.org