Social Networks the Semantic Web and the Future

Social Networks, the Semantic Web, and the Future of Online Scientific Collaboration Jennifer Golbeck University of Maryland, College Park 1

Overview • • 2 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 W 3 C 3

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? 4

Stuff We Want • Find me the mp 3 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 5

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 6

Semantic Web Technologies • URIs • Ontologies • Standard Languages – RDFS – OWL • SPARQL 7

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? 8

Linking Distributed Data Location Camera Info Temperature NWS Weather Data Date Precipitation President Video Prime Minister 9 More data

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 10

Semantic Web for Science 11

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 12

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 13

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? 14

Case Study: A Semantic Web Approach to the Provenance Challenge 15

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 (f. MRI) is the example in the challenge 16

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Challenge • Represent all data that we consider relevant about the history of each file • Answer as many queries as possible 18

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. 19

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 20

Semantic Web Approach • Ontology represents information about the execution of services and the dependencies of files 21

Provenance. owl 22

Answering the Queries • SPARQL, a W 3 C standard, is used to formulate queries • Reasoning with the semantics of OWL and some rules 23

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? 24

Social Networks: The Phenomenon 25

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. My. Space, Friendster, Orkut, Linked. In 26

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 user accounts among the networks • Number of users has doubled in the last 18 months • Full list at http: //trust. mindswap. org 27

Biggest Networks 28 1. My. Space 2. Adult Friend Finder 3. Friendster 4. Tickle 5. Black. Planet 6. Hi 5 7. Live. Journal* 8. Orkut 9. Facebook 10. Asia Friend Finder 120, 000 23, 000 21, 000 20, 000 17, 000 14, 000 10, 000 8, 500, 000 8, 000 6, 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 social network profiles are available in FOAF format • Approximately 60% of all semantic web data is FOAF data 29

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) 30

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 31

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 32

Examples • Only people I trust can see my phone number • I will only accept emails from people I trust 33

Challenges to Using Trust • Each person only knows a 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 34

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. t AC A 35 t. AB B t. BC C

Caveats and Insights • Trust is contextual • Trust is asymmetric • Trust is not exactly transitive 36

Source 37 Sink

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 38

How Well Does It Work? • Pretty well • On networks where we have tested it, trust is computed accurately within about 10% 39 – 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 40

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 41

Social Networks for Science Data + Provenance + Social Networks = Social Policies 42

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 43

Applications of the policy aware web • • 44 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 W 3 C Workshop on Constraints and Capabilities for Web Services – http: //www. w 3. org/2004/09/ws-cc-program. html 45

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 46

Policies and Trust 47 • 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 48

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 49

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 50

What do we need to do? • “Easy” Steps – Building ontologies for representing scientific data / metadata – Publishing data on the web 51

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 52

Forcing the Anti-Social Into Social Nets • Can’t expect scientists to use a Facebook/My. Space style social network (and we probably don’t want to see that anyway…) • Integrate social networking into other activities – E. g. email 53

The Payoff • A whole new way of working over the web • Multiple levels of collaboration • New ways of sharing data and working together 54

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 55 – 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 • http: //trust. mindswap. org 56