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Institute of Informatics, Slovak Academy of Sciences Michal Laclavík Ladislav Hluchý Partner Description Institute of Informatics (II SAS) is one of more than 50 scientific and research institutes of Slovak Academy of Sciences, Bratislava, Slovakia. The scope of activities of II SAS includes scientific and research work in informatics, information technology, control theory, robotics and artificial intelligence. Organized in 7 departments: – Parallel and distributed computing • Intelligent and Knowledge oriented Technologies Group – – – – – – EDA Meeting Design and diagnostics of digital systems Numerical methods and algorithms Speech analysis and synthesis Electron beam lithography Discrete processes modeling and control Sensor systems EADS, Paris, 24th February 2009 2 Primary Research Team & Capabilities URL: http://ikt.ui.sav.sk Dept. of Parallel and Distributed Computing Research and Development Areas: – Large-scale HPCN and Grid applications – Intelligent and Knowledge oriented Technologies Director & leader of PDC: Dr. Dipl. Ing. Ladislav Hluchý Experience from IST: – 3 project in FP5: ANFAS, CrosGRID, Pellucid – 6 project in FP6: EGEE II, K-Wf Grid, DEGREE (coordinator), EGEE, int.eu.grid, MEDIGRID – 4 projects in FP7: Secricom, Commius, Admire, EGEE III Several National Projects (SPVV, VEGA, APVT) IKT Group Focus: – – – – Information Processing Semantic Web Knowledge oriented Technologies Parallel and Distributed Information Processing – Multi-agent systems Solutions: – – – – EDA Meeting Ontea: Pattern-based Semantic Annotation ACoMA: KM tool in Email EMBET: Recommendation System AgentOWL: Agent library, model and methodology EADS, Paris, 24th February 2009 3 IISAS Expertise • Many Experience from European and National projects – FP5,FP6 and FP7 EU projects • • • • Multi agent systems: Pellucid, Secricom Data Integration and Data mining: Admire Large Scale Data processing: Crossgrid, EGEE, DEGREE, … Simulation (Flood Application): CrossGrid, K-Wf Grid, … – National project Raport • Support for Military Training – National project NAZOU • Heterogeneous distributed data processing, integration including semantics • Very good technology background – – – – – – Data integration (related to Sensor Data fusion) Large scale data processing Simulation and Visualization Multi-agent Systems Semantics, social networks Recommendation Systems EDA Meeting EADS, Paris, 24th February 2009 4 Relevant Projects ADMIRE (Advanced Data Mining and Integration Research for Europe) • • • • 7RP programme, ICT priority, 2008/03 – 2011/02, 6 institutions from EU Accelerate access to and increase the benefits from data exploitation; Deliver consistent and easy to use technology for extracting information and knowledge; Cope with complexity, distribution, change and heterogeneity of services, data, and processes, through abstract view of data mining and integration; and Provide power to users and developers of data mining and integration processes UISAV’s role • Environmental dataspecific methods for data integration Tools applying knowledge management to data mining in SOA environment Pilot application, user interface EDA Meeting EADS, Paris, 24th February 2009 6 RAPORT project • Experience with Military project • • (2004-2007) The goal of project was proposal of knowledge management support system for organization of military training. • • Intelligent process management Recommendation system • Organization of military exercise in a Centrum of Simulation Technologies (CST) of National Academy of Defense (NAD) in Liptovský Mikuláš (Slovakia) Exercise planning Exercises • CST organizes several overlapping military exercises during a training year – – – problems with large amount of documents and e-mails problems with knowledge management problems with time management Headquarters staff Exercise management A B C Exercise outline D E F CST A B C I J D Exercise planning Dept. of technical support Dept. of military aplications Dept. of staff training G I Exercise execution H E • knowledge system based on e-mail communications and a web portal SW support Activities Execution Context Modification Ontology Technical support Exercise evaluation Workflow execution EDA Meeting EADS, Paris, 24th February 2009 7 Pellucid IST Project Interaction Layer Process Layer • Title: Platform for Organizationally Mobile Public Employees • Duration: Sep 2002Dec 2004 • Agent Architecture based on autonomous co-operating agents • Knowledge Management to support employees • Workflow based Administration Processes • To support Employee Mobility in organization Workflow Tracking/Management System CONTEXT & ACTIONS USERS Pellucid “core” PELLUCI D ACTIVE HINTS USER FEEDBACK Pellucid interface EDA Meeting EADS, Paris, 24th February 2009 8 Secricom: Seamless Communication for Crisis Management EU FP7 project: (2008-2011) FP7-218123 Service platform for mobile devices Support for Intelligent Crisis Management Securing legacy data Using Multi-agents and SOA • Distributed Secure Agent Platform (DSAP) – – • Process Execution Agents (PEA) – – Activity initialization – Specific requirements – Context Generic plan New agent (Untrusted) Server 1 Trusted administrator DS Problem Event’s verification – Iniciator DS Trusted Server Action authorisation Trusted local contact DS – Our trusted domain EDA Meeting Public keys Servers and services register Trusted users Trusted authority Database of system users, agents and their certificates. Process of accreditation of agents. • Resource Inquire System (RIS) – Base of agents (Untrusted) Server N Communicating with users in a form of guided dialog through electronic device. Including authentication and interface to authorization of the user. • Agent Repository (AR) Event Trusted domain Z Based on the plan collected from users generating a plan of activities. Executes the plan. • User Communication Agent (UCA) Specific plan Trusted domain A The core agent platform. Providing means for agent deployment, execution, migration and communication. Provide information which system to query for specific information. EADS, Paris, 24th February 2009 9 K-Wf Grid Project • Semantic Service Oriented Architecture • Workflows of Web Services EDA Meeting EADS, Paris, 24th February 2009 10 NAZOU • • Tools for acquisition, organization and maintenance of knowledge in an environment of heterogeneous information resources Slovak National project, duration: 2004-2007 • Web => Documents => Text => RDF/OWL => XML + XSL => HTML Stemmer Lemmatizer Document Language Doc Converter NALIT Documents URL RIDAR RFTS OnTeA Indexing Documents Offers URL Corporate Memory Internet File Management ERID URL Relevance Relational Database Management Semantic and Ontological Management Searching, Management, Presentation URL Documents URL HTML Documents EDA Meeting Web Crawler Relational Data Ontological data Job Offers RDB2Onto JOP • OnTeA - Ontology based Text Annotation using Regular Expression Patterns • RDB2Onto – Relational database data to ontology converter • OSID - Offer Separation for Internet Document • RIDAR - Relevant Internet Data Resource Identification • SETH - SETH is a software effort to deeply integrate Python with Web Ontology Language (OWL-DL dialect) • WEBCRAWLER - WebCrawler downloads recursively web pages. Only relevant documents are downloaded estimating relevance using ERID. EADS, Paris, 24th February 2009 11 Commius project • • • • • • • Commius IST FP7 project FP7-213876 Community-based Interoperability Utility for SMEs (2008-2011) Pattern based Information Extraction and Semantic Annotation External and Legacy System Integration Large Scale Information Processing Information and Knowledge Management Social Networks Extraction • • Addressing ISU like interoperability Commius partners envision a future in which SMEs enjoy a zero-cost entry into interoperability based on non-proprietary protocols. • Systems Interoperability – • Semantic Interoperability – – • interoperability over SMTP Understanding communication and documents exchanged Process Interoperability – Understanding and supporting business process from communication activities EDA Meeting EADS, Paris, 24th February 2009 12 Prototypes and Solutions Agent Knowledge Model • Used in: – Business process management – recommendation systems for user or agent modeling • Based on Events, Resources, Actions, Actors, Context • Formally Described using Sets, Description Logic (compatible with OWL-DL), Graph Representation • Actor Context updating function/algorithm (Actor Environment State) • Resources updating function/algorithm (result of fulfilled actor goals) EDA Meeting EADS, Paris, 24th February 2009 14 AgentOWL Library • Agents with OWL ontology models using JADE agent system and Jena • • Support for OWL based Agent Knowledge Model Support for XML-RPC connection to receive event and send plain XML Support for agent communication using FIPA ACL with OWL and RDQL as content languages Support for Presentation of Ontological Knowledge (RDF/OWL => plain XML + XSL => HTML) JADE and Jena Integration Available on JADE official website to MAS community • • • • Multi Agent System ACL IIOP, HTTP, SMTP User requests Displaying results XML, XML-RPC, SOAP KM Agent 1 External System FIPA ACL, KIF, FIPA-SL, FIPA-RDF Agent 3 Knowledge Model KM Agent 2 Directory Facilitator EDA Meeting Graphical User Interface FIPA ACL, RDF/OWL, RDQL EADS, Paris, 24th February 2009 Knowledge Storage Querying Knowledge Base 15 Ontea: Platform for Pattern Based Annotation • • Input: Sensor data, log files or text Output: Relation to semantic model • Text – • • Bratislava is the capital of Slovakia. Slovakia is in Europe. Pattern: “(in|by) + (the)? *([A-Z][a-z]+)” for Location Ontea discovers key – value pair: – • Location – Europe By transformation to ontology knowledge base - it finds Europe as continent using inference (sub-class of Location) – • Continent – Europe Features Identification of concept instances from the ontology Automatic population of ontologies with instances Identifying relevance, when creating instances using information retrieval techniques Key-value pairs transofrmation Integration with data from external systems Large scale semantic annotation of documents or texts using Google’s MapReduce architecture. More Examples are in the table: # Text Key – value Patterns – regular expressions 1 Apple, Inc. Company: Apple Company: ([A-Za-z0-9]+)[, ]+(Inc|Ltd) 2 Mountain View, CA 94043 Settlement: Mountain View Settlement: ([A-Z][a-z]+[ ]*[A-Za-z]*)[ ]+[A-Z]{2}[ ]*[0-9]{5} 3 [email protected] Email: [email protected] Email: [-_.a-z0-9]+@[-_.a-zA-Z0-9]+\.[a-z]{2,8} 4 Mr. Michal Laclavik Person: Michal Laclavik Person: (Mr.|Mrs.|Dr.) ([A-Z][a-z]+ [A-Z][a-z]+) EDA Meeting EADS, Paris, 24th February 2009 16 Semantic Graphs processing • Spread Activation – Fast method for inference – Logic based inference is exponential and slow – Used e.g. by IBM in Galaxy Library – Experiments on emails – Experiments on wikipedia links • Discovery of relations between articles • 72 mil. links in network • Transformation of graphs • Exploration of related elements • Use of Google’s MapReduce architecture for large scale data processing EDA Meeting EADS, Paris, 24th February 2009 17 Recommendation Systems Objective: Recommend and provide user information or knowledge in context Email based Web based EDA Meeting Collaboration among users Knowledge sharing Active knowledge provision Reuse of knowledge: notes and other resources EADS, Paris, 24th February 2009 18 Simulation of Floods, Visualization EDA Meeting EADS, Paris, 24th February 2009 19 Technology Expertise • We work mainly with Open-Source Products, concretely: • Technology – Linux, Java, Tomcat, Apache, PHP, JSP, XML, RDF • Data Integration and Processing – – MapReduce: Hadoop, Grid: Globus, gLite OGSA-DAI • Ontologies – – – – Standards: RDF, OWL (OWL-DL) Ontology Design and Definition: Protégé (many plug-ins OntoViz) Methodology for Developing Knowledge Management Systems: CommonKADS Knowledge Bases: • • – Sesame (primary RDF storage) JENA Storage Interfaces (MEM model or RDBM model) Onto Interfaces • • JENA (SPARQL, RDQL) Jena inference engine (rule based) • Graph Processing – – – Spread Activation Semantic Transformation Back-edge heuristic for Steiner tree packing problem approximation EDA Meeting EADS, Paris, 24th February 2009 20 IISAS Contribution • Good expertise from projects participation and technology background • • • • Data Integration Large scale data and information processing Multi-agent Systems Semantics and Graph Processing • If fits with proposal – Recommendation Systems – Simulation and Visualization EDA Meeting EADS, Paris, 24th February 2009 21