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Outline of the Unit

Outline of the Unit

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Outline of the Unit

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  1. Knowledge ManagementSemantic Web and Social NetworkingBuilding a Temporal Geosocial Semantic WebProf. Bhavani Thuraisingham Prof. Latifur KhanProf. Murat KantarciogluThe University of Texas at Dallasbhavani.thuraisingham@utdallas.eduOctober 2009

  2. Outline of the Unit • FOAF • LINK (Peter Mika, Free University) • Extracting social networks from Semantic Web Data (Tim Finin et al, UMBC, Jennifer Golbeck UMC) • Building a Geosocial semantic web

  3. 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. •

  4. 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

  5. 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.

  6. 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 • 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.

  7. Social Network Analysis on Semantic Web Data: Group Detecrion • 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. • 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 cluste rand 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

  8. 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 (2007-9) Technology Insertion into HP’s Jena RDF Data Manager Contributions to BLACKBOOK

  9. Black Book Utilization Blackbook REST Representational State Transfer Geospatial Proximity • Rome • Atom • XML Servlets Converting MS Access Database to MySQL Spatial Format

  10. B Other L Social Network Extraction and Analysis Services A C e.g., Ontology Matching and Alignment Security, K Integrity B FUTURE Rule based reasoning, Data mining O WORK O RDF Graph Store Managemen - JENAt K Storage, Transactions, Query, Integration RDF Graph Store RDF Graph Store RDF Graph Store Current Work Technology Insertion into HP’s Jena RDF Data Manager Contributions to BLACKBOOK

  11. Military Stabilization and Reconstruction Operations (SARO) • According to a GAO Report published in October 2007 “DOD has taken several positive steps to improve its ability to conduct stability operations but faces challenges in developing capabilities and measures of effectiveness, integrating the contributions of non-DOD agencies into military contingency plans, and incorporating lessons learned from past operations into future plans. • These challenges, if not addressed, may hinder DOD’s ability to fully coordinate and integrate stabilization and reconstruction activities with other agencies or to develop the full range of capabilities those operations may require.” • Around the same time, the Center for Technology and National Security Policy at NDU and the Naval Postgraduate School identified some key technologies crucial for the military stabilization and reconstruction processes in Iraq and Afghanistan.

  12. Military Stabilization and Reconstruction Operations (SARO)

  13. 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

  14. SARO Lifecycle (SAROL)

  15. Our Design of SAROL via TGS-SW • We are designing SAROL (Stabilization and Reconstruction Operations Lifecycle). SAROL will consist of multiple phases and will discover relationships and information, model and integrate the relationships and information, as well as exploit the relationships and information for decision support. • The system that implements SAROL will utilize geosocial semantic web technologies, a novel semantic web that we are designing. • The basic infrastructure that glues together the various phases of SAROL will be based on the SOA paradigm. • We are utilizing the technologies that we have developed in social networking and geospatial semantic web to develop temporal geosocial semantic web technologies for SAROL. • It should be noted that in our initial design we are focusing on the basic concepts for SAROL will involve the development of TGS-SW. • This includes capturing the social relationships and mapping them to the geolocations. • In our advanced design we will include some advanced techniques such as knowledge discovery, and risk based trust management for the information and relationships.

  16. Our Design of SAROL via TGS-SW

  17. Our Design of SAROL via TGS-SW

  18. Our Design of SAROL via TGS-SW

  19. Ontology Matching: Supporting research Given 2 ontologies O1 and O2 from the same knowledge domain, the goal is to find similar concepts by examining their respective names, instances and structural properties

  20. 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.