Building and Analyzing Social Networks Semantic Web and Social Networks

1. Building and Analyzing Social Networks Semantic Web and Social Networks Dr. Bhavani Thuraisingham February 8, 2013

2. Semantic Web: Chapter 1 • Reference: P. Mika, Semantic Web and Social Networks, Springer, 2008: Chapter 1 • Limitations of the Current Web • The Semantic Solution • Developments of the Semantic Web • Emergence of the Social Web

3. Limitations of the Current Web • Who is Frank van Harmelen? • Show me photos of Paris • Find new music that I might like • Tell me about music players with a capacity of at least 4GB

4. The Semantic Solution • Apply knowledge technologies to fill the knowledge gap between the human and the machine • Provide personal information in semantic format • Attach metadata – e.g., to images • Provide background knowledge • Aggregate information • Knowledge representation and reasoning

5. Developments of the Semantic Web • Early developments include the WWW, Mosaic, HTML, XML • Semantic Web Technology Stack • RDF, OWL • Reasoning with semantic web technologies and the development of SWRL • Query languages and data management - SPARQL

6. Emergence of the Social Web • Web Services • Blogs • Wikipedia • Online social networks • Web 2.0 + Semantic Web = Web 3.0

7. Social Network Analysis: Chapter 2 • Reference: P. Mika, Semantic Web and Social Networks, Springer, 2008: Chapter 2 • What is Network Analysis • Development of Social Network • Concepts and Measures in Network Analysis

8. What is Network Analysis • Social Network Analysis is the study of social networks among a set of actors • Focus is on the relationships between the actors and not on the actors themselves • Some relationships are more important than others • Some actors are more important than other actors • Data collection and analysis • Collect data and build a graph, analyze the graph • Manual process consisted of filling questionnaire and analyzing the data using statistical methods • Automated methods: extracting nuggets from massive amounts of data and building relationships

9. Development of Social Networks • Social scientists influenced the field since the 1930s • Moreno’s concept of sociogram • Sociogram visualized as a collection of nodes and links • WWW is a collection of nodes and links • Links in the WWW represents the relations between two web pages

10. Concepts and Measures in Network Analysis • Networks represented as graphs • Mathematical representations of a graph could be a matrix. • 1 represents a links between nodes Vi and Vj. • 0 if there no link between Vi and Vj • Add weights to links • Strength between Vi and Vj is 0.9, between Vi and Vk is 0.2 • Observations • People are separated by 6 steps • Most people have about two coauthors while very few have more than 20 coauthors

11. Concepts and Measures in Network Analysis • Analysis • Find in-degree and out-degree • Find the hub • Find the clusters • Questions to answer • Important people in the network • Who do people go to often • Who has many relationships • Which two have the strongest relationship

12. Some Examples • This unit describes the relationship between Social Networks and Semantic Web • FOAF • LINK (Peter Mika, Free University) • Extracting social networks from Semantic Web Data (Tim Finin et al, UMBC, Jennifer Golbeck UMC) • Our Work • Convergence

13. Semantic Social Networks • The latest breed of social networking services combine social networks with the sharing of content such as bookmarks, documents, photos, reviews. • The use of of Semantic Web technology facilitated distributed control. • The friend-of-a-friend (FOAF) project is a first attempt at a formal, machine processable representation of user profiles and friendship networks. (Unlike with Friendster and similar sites that have central control) • FOAF profiles are created and controlled by the individual user and shared in a distributed fashion. • http://www.foaf-project.org.

14. FOAF • The Friend of a Friend (FOAF) project is creating a Web of machine-readable pages describing people, the links between them and the things they create and do; it is a contribution to the linked information system known as the Web. • FOAF defines an open, decentralized technology for connecting social Web sites, and the people they describe. • FOAF is part of a shift towards a Web where we can choose the sites and tools we like, without being cut off from friends who made different choices. • FOAF lets you share and inter-connect information from diverse sources, move it around, and use it in unexpected new ways. Sharif University of Technology, Semantic Web Course, Fall 2005

15. FOAF Example • <foaf:Person rdf:about="#me“ xmlns:foaf="http://xmlns.com/foaf/0.1/"> <foaf:name>Dan Brickley</foaf:name> <foaf:mbox_sha1sum>241021fb0e6289f92815fc210f9e9137262c252e</foaf:mbox_sha1sum> <foaf:homepage rdf:resource="http://danbri.org/" /> <foaf:img rdf:resource="/images/me.jpg" /> </foaf:Person>

16. Semantic Social Networks Semantic Web researchers and their connections across the globe.

17. Semantic Social Networks Social Network of a Semantic Web Researcher

18. FLINK (Peter Mika, Free University) • Flink, the system developed at Free University 9The Netherlands) is one of the early semantic social networks that exploits FOAF for the purposes of social intelligence. • social intelligence, is consdiered to be the semantics-based integration and analysis of social knowledge extracted from electronic sources under diverse ownership or control. In our case, these sourcesFrom • Flink extracts knowledge about the social networks of the community and consolidates what is learned using a common semantic representation, namely the FOAF

19. FLINK Architecture Architecture Of Flink

20. FLINK Architecture • The architecture of Flink can be divided in three layers concerned with metadata acquisition, storage and visualization • Acquisition layer of the system concerns the acquisition of metadata. (e.g., HTML pages from the web, FOAF profiles from the Semantic Web, public collections of emails and bibliographic data) • The web mining component of Flink employs a co-occurrence analysis technique The web mining component also performs the additional task of finding topic interests, i.e. associating researchers with certain areas of research. • The middle layer is responsible for storing and enhancing metadata through reasoning. • Inference is another major task of the middle layer. Sesame (we can also use JENA) applies the RDF closure rules to the data at upload time. This feature can be extended by defining domain-specific inference rules in Sesame’s custom rule language. • The third layer, is the browing and visualization layer,. The user interface of Flink is a pure Java web application based on the Model-View-Controller (MVC) paradigm.

21. Social Network Analysis on Semantic Web Data • Social network analysis tasks for Flink augments the web mining task with finding which people belong to which groups (called GROUP DETECTION) • The association and links between people including what is the relationship between John and James? Are they just friends or do they have a romantic relationship? Do they often travel together? • Semantic web reasoning tools (e.g., based on OWL, RDF and SWRL) may be used to reason and extract the nuggets.

22. Group Detection • A large community often breaks up to a set of closely knit groups of individuals, woven together more loosely by the occasional interaction across groups. Based on this theory, SNA offers a number of clustering algorithms for identifying communities based on network data. Alternatively, the subgroups may be identified by the researcher using additional attribute data on the Peter Mika’s research uses an interactive clustering software provided as a sample with the JUNG Java toolkit for SNA. This software allows the user to cluster a network using an edge-betweenness clusterand visualize the results. As an example, a group of researchers from the AIFB Institute of the University of Karlsruhe quickly emerge as a single cluster of the network.

23. Linking Social Networks with FOAF • One of the core goals of the Semantic Web is to store data in distributed locations, and use ontologies and reasoning to aggregate it. • Social networking is a large movement on the web, and social networking data using the Friend of a Friend (FOAF) vocabulary makes up a significant portion of all data on the Semantic Web. • Many traditional web-based social networks share their members’ information in FOAF format. • While this is by far the largest source of FOAF online, there is no information about whether the social network models from each network overlap to create a larger unified social network model, or whether they are simply isolated components. • Researchers at the U of MD have studied the intersection of FOAF data found in many online social networks. Using the semantics of the FOAF ontology and applying Semantic Web reasoning techniques, they show that a significant percentage of profiles can be merged from multiple networks.

24. Extracting Social Networks • Extracting social network from noisy, real world data is a challenging task, even if the information is already encoded in RDF using well defined ontologies. • The process consists of three steps: discovering instances of foaf:Person, merging information about unique individuals, and linking person through various social relation properties such as foaf:knows.

25. Extracting Social Networks (Tim Finin) • A critical problem is determining whether two foaf:Person instances denote the same person. The semantics of FOAF vocabulary suggests several heuristics to answer this question: • • named URI. Non-anonymous individuals using the same URI denote the same person. • • Inverse-functional properties. Inverse functional properties such as foaf:mbox and foaf:homepage identify unique individuals. Other properties, such as foaf:name and foaf:nick, while not strictly inverse functional, can be used in practice in conjunction with other properties like foaf:phone to identify individuals with high probability. • Semantic equality. When two or more values of an inverse functional property co-exist in the same individual’s description, they are semantically equivalent as identifying the same individual. • \

26. B Applications to the Geospatial Domain Other L Services A Entity Extraction, Relationship Extraction C e.g., Ontology Matching and Alignment Security, K Integrity B FUTURE Rule based reasoning, Data mining O WORK O RDF Graph Store Management K Storage, Transactions, Query, Integration RDF Graph Store RDF Graph Store RDF Graph Store Our Work for IARPA Under KDD Program (2007-2010) Technology Insertion into HP’s Jena RDF Data Manager Contributions to BLACKBOOK

27. Military Stabilization and Reconstruction Operations (SARO)

28. Military Stabilization and Reconstruction Operations (SARO) • Four concurrent tasks have to carry out in parallel (NDU Study). They are the following: • (i) Security: Ensure that those who attempt to destroy the emergence of a new society are suppressed. This will include identifying that are the trouble makers or terrorists and destroy their capabilities. • (ii) Law and order: Military and police skills are combined to ensure that there are no malicious efforts to disturb peace. • (iii) Repair infrastructure: Utilize the expertise of engineers and geographers both from allied countries and local people and build the infrastructure. • (iv) Establish an interim government effectively: Understand the cultures of the local people, their religious beliefs and their political connections and establish a government. • Dr. Karen Guttieri states that Human Terrain is a crucial aspect and we need hyperlinks to People, Places, Things and Events to answer questions such as * Which people are where? Where are their centers and boundaries? Who are their leaders Who is who in the zoo? What are their issues and needs? What is the news and reporting? Essentially the human domain associations builds relationships between the who, what, where, when and why (5W)

29. SARO Lifecycle (SAROL) • SAROL consists of three major phases • (1) information and relationship discovery and acquisition, • (2) information and relationship modeling and integration and • (3) information and relationship exploitation. • During the discovery and acquisition phase commanders and key people will discover the information and relationships based on those advertised as well as those obtained through inference. • During the modeling and integration phase the information and the relationship have to be modeled, additional information and relationships inferred as well as the information and relationships integrated. • During the exploitation phase the commanders and those with authority will exploit the information, make decisions and take effective actions

30. Temporal Geo-Social Semantic Web

31. Incentives for Social Communication • Incentive based communication is a major component of the SARO system. • We are working on building mechanisms to give incentives to individuals/organizations for information sharing and communication. • Once such mechanisms are built, we can use concepts from the theory of contracts( by Laffont and Martifort) to determine appropriate rewards such as ranking or, in the case of certain partners, monetary benefits. • Currently, we are exploring how to leverage secure distributed audit logs to rank individual organizations between trustworthy partners. • To handle situations where it is not possible to carry out auditing, we are developing game theoretic strategies for extracting information from the partners. The impact of behavioral approaches to sharing are also currently considered. • Finally we are conducting studies based on economic theories and integrate relevant results into incentivized assured information sharing as well as collaboration/communication.

32. Convergence • Semantic web data includes databases, files, web logs, blogs, emails, etc. • Data mining applied to semantic web data together with the reasoning capabilities of semantic web result in social networks • Data mining applied to social networks extract the nuggets • Nuggets together with additional semantic web data such as ontologies result in knowledge • Knowledge utilized to improve the effectiveness of an organization

33. Convergence Semantic Web Data/Reasoning XML, RDF, OWL e.g., databases Blogs, email Data Management/ Data Mining/ Data Analytics Social Networks/ Analysis Knowledge/ Knowledge Management

34. Vision • Improved technologies for data representation • Data will include structured and unstructured databases, emails, blogs, files, relationships, video, images, audio, tags, links, - - - - - • Improved tools for reasoning • Improved tools for data mining/data analytics • Improved tools for social network extraction • Improved tools for knowledge extraction • Improved tools for knowledge management • We call the above Information Analytics