institute of informatics, slovak academy of sciences michal laclavík ladislav hluchý

Institute of Informatics, Slovak Academy of SciencesInstitute of Informatics, Slovak Academy of Sciences

Michal Laclavík

Ladislav Hluchý

EDA Meeting EADS, Paris, 24th February 2009 2

Partner DescriptionPartner 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

– Design and diagnostics of digital systems– Numerical methods and algorithms– Speech analysis and synthesis– Electron beam lithography – Discrete processes modeling and control – Sensor systems


Primary Research Team & CapabilitiesPrimary Research Team & Capabilities

Dept. of Parallel and Distributed ComputingResearch and Development Areas:

– Large-scale HPCN and Grid applications– Intelligent and Knowledge oriented Technologies

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:– Ontea: Pattern-based Semantic Annotation– ACoMA: KM tool in Email– EMBET: Recommendation System– AgentOWL: Agent library, model and methodology

Director & leader of PDC: Dr. Dipl. Ing. Ladislav Hluchý

URL: http://ikt.ui.sav.sk


IISAS ExpertiseIISAS 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

Relevant ProjectsRelevant Projects


ADMIREADMIRE ((AAdvanced dvanced DData ata MMining and ining and IIntegration ntegration RResearch for esearch for EEuropeurope))

• 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 data-

specific methods for data integration

Tools applying knowledge management to data mining in SOA environment

Pilot application, user interface


RAPORT projectRAPORT 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)

• 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

• knowledge system based on e-mail communications and a web portal

Exercise management

Exercise planning

Exerciseexecution

Exerciseoutline

Exerciseevaluation

Technical supportSW support

Exercise planning

DA B

CST

C

I

F

E

B C DA

I

E

J

Exercises

Dept. of staff training

G

H

Headquarters staff

Dept. of militaryaplications

Dept. of technicalsupport

Context Context ModificationModification

Workflow Workflow executionexecution

Activities Activities ExecutionExecution

OntologyOntology


Pellucid IST ProjectPellucid IST Project

• Title: Platform for Organizationally Mobile Public Employees

• Duration: Sep 2002- Dec 2004

• Agent Architecture based on autonomous co-operating agents

• Knowledge Management to support employees

• Workflow based Administration Processes

• To support Employee Mobility in organization

PELLUCID

USERSCONTEXT

&ACTIONS

ACTIVEHINTS USER

FEEDBACK

Workflow Tracking/Management System

Pellucid“core”

Pellucid interface

Process Layer

Interaction Layer


Secricom: Seamless Communication for Crisis Management Secricom: Seamless Communication for Crisis Management

• Distributed Secure Agent Platform (DSAP)

– The core agent platform.– Providing means for agent

deployment, execution, migration and communication.

• Process Execution Agents (PEA)– Based on the plan collected from users

generating a plan of activities.– Executes the plan.

• User Communication Agent (UCA)– Communicating with users in a form of

guided dialog through electronic device.

– Including authentication and interface to authorization of the user.

• Agent Repository (AR)– Database of system users, agents and

their certificates.– Process of accreditation of agents.

• Resource Inquire System(RIS)

– Provide information which system to query for specific information.

DS

(Untrusted) Server N

Trusted users

DS

DS(Untrusted) Server 1

Trusted Server

Iniciator

Trusted administrator

Event

Event’s verification

New agent

Problem

Generic plan

Specific requirements

Specific plan

Context

Trusted domain Z

Our trusted

domain

Trusted domain A

Trusted authority

Action authorisation

Trusted local contact

Servers and

services register

Activity initialization

Public keys

Base of agents

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


K-Wf Grid ProjectK-Wf Grid Project

• Semantic Service Oriented Architecture• Workflows of Web Services


NAZOUNAZOU

• 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.

• 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

Corporate Memory

Internet

RIDAR

WebCrawler

ERIDFile

Management

Relational Database

Management

Semantic and Ontological

Management

DocConverter

RDB2Onto

OnTeA

Searching, Management, Presentation

Documents

URL Relevance

URL

URL

Documents

Ontological data

DocumentsIndexing

Relational Data

RFTS

HTML Documents

JOP

NALIT

Document Language

StemmerLemmatizer

Offers

Job OffersURL

URL


CommiusCommius project 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– interoperability over SMTP

• Semantic Interoperability– Understanding communication and – documents exchanged

• Process Interoperability– Understanding and supporting business

process from communication activities

Prototypes and SolutionsPrototypes and Solutions


Agent Knowledge ModelAgent 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)


AgentOWL LibraryAgentOWL 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

Agent 1

Agent 2

Agent 3

Graphical User Interface

External System

Knowledge Base

DirectoryFacilitator

Knowledge StorageQuerying

XML, XML-RPC, SOAP

User requestsDisplaying results

FIPA ACL, KIF, FIPA-SL, FIPA-RDF

FIPA ACL,RDF/OWL, RDQL

Knowledge Model

KM

KM

IIOP, HTTP, SMTP

ACL


Ontea: Ontea: Platform for Pattern Based AnnotationPlatform 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

• 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]+)

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.


Semantic Graphs processingSemantic 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


Objective: Recommend and provide user information or knowledge in context

Email basedWeb based

Recommendation SystemsRecommendation Systems

Collaboration among users Knowledge sharing Active knowledge provision Reuse of knowledge: notes and other

resources


Simulation of Floods, VisualizationSimulation of Floods, Visualization


Technology ExpertiseTechnology 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


IISAS IISAS ContributionContribution

• 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