# Presentation: 848877

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

- Jennifer Golbeck
- University of Maryland, College Park

## 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

## What is the Semantic Web

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

## 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?

## 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

## 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

## Semantic Web Technologies

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

## 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?

## Linking Distributed Data

- Video

- Location

- Date

- President

- Prime
-  Minister

- More data

- Camera
- Info

## 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

## Semantic Web for Science

## 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

## 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

## 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?

## Case Study: A Semantic Web Approach to the Provenance Challenge

## 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

## Challenge

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

## 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.

## 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

## Semantic Web Approach 

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

## Provenance.owl

## Answering the Queries

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

## 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?

## Social Networks: The Phenomenon

## 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

## 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](http://trust.mindswap.org/)_

## 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

## 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

## 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)

## 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

## 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

## Examples

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

## 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

## 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

- C

- tAB

- tBC

- tAC

## Caveats and Insights

- Trust is contextual
- Trust is asymmetric
- Trust is not exactly transitive

## Slide 37

## 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

## 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

## 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

## 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

## Social Networks for Science

*Data + Provenance +  Social Networks = Social Policies*

## 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

## Applications of the policy aware web

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

## 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

## 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*

## 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*

## 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

## 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

## 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

## What do we need to do?

- “Easy” Steps
- Building ontologies for representing scientific data / metadata
- Publishing data on the web

## 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

## 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

## The Payoff

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

## 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

## Questions

- Jennifer Golbeck
- _[Golbeck@cs.umd.edu](mailto:Golbeck@cs.umd.edu)_
- http://trust.mindswap.org