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Social Networks, the Semantic Web, and the

Future of Online Scientific CollaborationJennifer 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 it’s 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 won’t 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

Video

Location

Date

President

Prime Minister

More data

NWS

WeatherData

Precipitation

Temperature

CameraInfo

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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 maximum=4095.

• A user has annotated some images with a key-value pair center=UChicago. Find the outputs of align_warp where the inputs are annotated with center=UChicago.

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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 Networks1. MySpace 120,000,0002. Adult Friend Finder 23,000,0003. Friendster 21,000,0004. Tickle 20,000,0005. BlackPlanet 17,000,0006. Hi5 14,000,0007. LiveJournal* 10,000,0008. Orkut 8,500,0009. Facebook 8,000,00010.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 don’t 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.

A B CtAB tBC

tAC

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Caveats and Insights

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

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SourceSink

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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 don’t 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

• Can’t expect scientists to use a Facebook/MySpace style social network (and we probably don’t 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@cs.umd.edu• http://trust.mindswap.org