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Using Trust and Provenance for Content Filtering on the Semantic Web. By Jen Golbeck & Aaron Mannes Maryland Information Network Dynamic Lab University of Maryland, College Park. What are social networks. Connections between people Can be Explicit (people say who they know) - PowerPoint PPT Presentation
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Using Trust and Provenance for Content Filtering on the
Semantic Web
By Jen Golbeck & Aaron MannesMaryland Information Network Dynamic Lab
University of Maryland, College Park
2/12
What are social networks
• Connections between people• Can be
– Explicit (people say who they know)– Derived (e.g. from email archives)– Simulated
3/12
Web-Based Social Networks (WBSNs)
• Websites and interfaces that let people maintain browsable lists of friends
• Last count– 142 social networking websites– Over 200,000,000 accounts– Full list at http://trust.mindswap.org
• Over 10,000,000 accounts are represented in FOAF, an OWL ontology
4/12
Trust in WBSNs
• 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
5/12
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
6/12
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
7/12
Film Trust
• Working example of this can be found at - FilmTrust available at http://trust.mindswap.org/FilmTrust
• A movie recommendation site backed by a social network that uses trust values to generate predictive recommendations and sort reviews
8/12
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
9/12
Trust Networks & Intelligence
• Intelligence agencies no longer face hierarchies, now they face networks
• Several major intelligence failures due to lack of information-sharing or adequately questioning dominant assumptions
• Sheer size of intelligence communities are often a barrier to information sharing
• Trust networks could help intelligence agencies connect the dots
10/12
Use Case Scenarios
• Help individual analyst sort through mass of material by identifying reliable sources
• Trust ratings would allow analysts to check veracity of information by seeing how sources are rated by other trusted analysts
• Importance of outliers for red-teaming - a team comes to strong conclusions on an issue: policy-makers could use trust ratings to check with dissenters
11/12
Uses for Meta-Data
• Analyzing patterns of trust ratings could help break organizational barriers
• While outliers are useful on a case by case basis they could also indicate an organizational dysfunction
• A pattern of low trust ratings between units could indicate a conflict or lack of understanding
• Alternately a pattern of particularly high ratings could indicate group think
12/12
References• Papers and software available at
http://trust.mindswap.org
• FilmTrust available at http://trust.mindswap.org/FilmTrust
• Terrorism Analysis available at http://profilesinterror.mindswap.org/