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Semantic and Agent Technologies in Developing Distributed Applications Resource Agent “Device” Resource Agent “Expert” Resource Agent “Service” Vagan Terziyan [email protected] jyu. fi Industrial Ontologies Group IT 2007, Jyvaskyla, Finland, 01. 11. 2007
Contents • • • Future Web –related trends; Motivation for agent technology; Motivation for semantic technology; Vision for future distributed applications; Some relevant projects and activities Case: “Ontology-Based Portal for Management of National Educational Resources” (from Ukraine); • Presentation slides can be downloaded from: q http: //www. cs. jyu. fi/ai/Terziyan_Klymova. ppt
Three alternative trends of Web development Human Communities Machines, devices, computers Facilitates Human-to. Human interaction Applications, services, agents Facilitates Machine-to. Machine interaction Facilitates Software-to. Software interaction
Challenges of Distributed Systems • Growing complexity and heterogeneity of computer systems and networks used in industry need for new approaches to manage and control them • IBM vision: Autonomic computing – Self-Management (includes self-configuration, self-optimization, selfprotection, self-healing) • Ubiquitous computing, “Internet of Things” huge numbers of heterogeneous devices are interconnected • “nightmare of pervasive computing” when almost impossible to centrally manage the complexity of interactions, neither even to anticipate and design it. We believe that self-manageability of a complex system requires its components to be autonomous themselves, i. e. be realised as agents. Agent Technology in SE is also considered to be facilitating the design of complex systems • We also believe that interoperability among heterogeneous components can be realized by utilization of Semantic Technology. •
Agent Definition [IBM]
Semantic Web “The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications” http: //www. w 3. org/sw/ The Semantic Web is an initiative with the goal of extending the current Web and facilitating Web automation, universally accessible web resources, and the 'Web of Trust', providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people.
Semantic Web: New “Users” applications agents
Semantic Web: Annotations applications agents Semantic annotations are specific sort of metadata, which provides information about particular domain objects, values of their properties and relationships, in a machine-processable, formal and standardized way.
Semantic Web: Ontologies applications agents Ontologies make metadata interoperable and ready for efficient sharing and reuse. It provides shared and common understanding of a domain, that can be used both by people and machines.
Semantic Web: Rules applications agents Logical support in form of rules is needed to infer implicit content, metadata and ontologies from the explicit ones. Rules can act as a means to draw inferences, to configure systems, to express constraints, to specify policies, to react to events/changes, to transform data, to specify behavior of agents, etc.
Semantic Web: Languages applications agents Languages are needed for machine-processable formal descriptions of: metadata (annotations) like e. g. RDF; ontologies like e. g. OWL. ; rules like e. g. Rule. ML. The challenge is to provide a framework for specifying the syntax (e. g. XML) and semantics of all of these languages in a uniform and coherent way.
Semantic Web: Tools applications agents User-friendly tools are needed for metadata manual creation (annotating content) or automated generation, for ontology engineering and validation, for knowledge acquisition (rules), for languages parsing and processing, etc.
Semantic Web: Applications and Services applications agents Utilization of Semantic Web metadata, ontologies, rules, languages and tools enables to provide scalable Web applications and Web services for consumers and enterprises" making the web 'smarter' for people and machines.
Semantic Technology • Semantic Web itself is not the main goal; • “Semantic” is a useful feature of an application; • Semantic Technology is efficient tool to design applications and make them smart, flexible and interoperable; • Combination with modern trends like e. g. Mobile and Ubiquitous Computing, Embedded Systems; Web Services and SOA, Agent Technologies and MAS; Machine Learning, data Mining and Knowledge Discovery; Distributed, Autonomous and Self-Configurable Architectures, Grid Computing, P 2 P; etc. makes advantages provided by Semantic Web more visible.
Semantic Web: which resources to annotate ? This is just a small part of Semantic Web concern !!! Technological and business processes External world resources Web resources / services / DBs / etc. Semantic annotation Shared ontology Web users Multimedia resources (profiles, preferences) Web access devices and communication networks Smart machines, devices, spaces, etc. Web agents / applications / software components
GUN Concept [Industrial Ontologies Group] GUN – Global Understanding e. Nvironment GUN = Global Environment + Global Understanding = Proactive Self-Managed Semantic Web of Things = (we believe) = “Killer Application” for Semantic Web Technology
GUN and Ubiquitous Society GUN can be considered as a kind of Ubiquitous Eco. System for Ubiquitous Society – the world in which people and other intelligent entities (ubiquitous devices, agents, etc) “live” together and have equal opportunities (specified by policies) in mutual understanding, mutual service provisioning and mutual usability. Human-to-Human-to-Machine-to-Human Machine-to-Machine Agent-to-Agent
Resources in GUN (1) • Software: Software and software components, operation systems, tools, Web-services, etc. ; • Data: Electronic documents, warehouses, databases, histories, diaries, lifeblogs, digital media resources, etc. • Devices: all kind of devices, machines, sensors, actuators, adapters, communicators, switches, routers, etc. and their components; • Humans: Users, customers, service providers, buyers, sellers, workers, operators, experts, etc. ; • Communication systems and networks: PANs, LANs, MANs, WANs, RFIDs, Wi. Fi, Wi. Max, LTE, etc.
Resources in GUN (2) • Organizations: various compositions of various resources selected and integrated for certain purpose, e. g. companies, universities, networks, etc. ; • Processes: natural, controlled or goal-driven, dynamics of organizations; • Concepts, Models and Ontologies: various concepts, models and ontologies, which formally describe various resources, their organizational structure, dynamics and coordination; • Messages: all kind of messages various resources exchange among themselves during their lifecycle; • Standards: all kind of standards according to which resources are produced, described, used, operate, communicate etc.
The Roadmap towards GUN Qualitative transitions Adaptation and personalization Core DAI platform Coordination and networking Proactivity and behavior Security and trust Autonomicity, selfmanagement, selfconfigurability Metadata, Industrial cases semantics, implementation ontologies Intelligence, Human… learning, centricity, reasoning, GUI, Web planning 2. 0, Wiki
Smart. Resource project summary • Smart. Resource Tekes project (2004 -2007): http: //www. cs. jyu. fi/ai/Onto. Group/projects. htm. • One of the most essential results of the Smart. Resource project was creation of the “Smart Resource Technology” for designing complex software systems. • The technology benefits from considering each traditional system component as a “smart resource”, i. e. proactive, agent-driven, selfmanaging.
Challenge 1: General Adaptation Framework SC Universal reusable semantically-configurable adapters
Challenge 2: General Proactivity Framework Role “Feeder” description Role “SCADA” description Role “Maintenanc e worker” description GB Universal reusable semantically-configurable behaviors
Challenge 3: General Networking Framework Scenario “Predictive maintenance” description PI Scenario “Data integration” description Universal reusable semantically-configurable scenarios for business processes
UBIWARE: “Smart Semantic Middleware for Ubiquitous Computing” • Funded by Tekes; • In the Web: http: //www. cs. jyu. fi/ai/Onto. Group/projects. htm • Started: 1 July 2007; • Summary: q UBIWARE project will be build on the foundation laid in the Smart. Resource project. It aims at designing a new generation middleware platform (UBIWARE) which will allow creation of self-managed complex industrial systems consisting of mobile, distributed, heterogeneous, shared and reusable components of different nature. • Partners: q IOG (AC, UJ), ABB, Fingrid, Hansa Ecura, Inno-W, Metso Automation, Metso Shared Services
PRIME: “Proactive Inter-Middleware for Integrating Embedded and Enterprise Systems” • EU FP 7 STREP proposal for the objective ICT-2007 -3. 7: “Networked Embedded and Control Systems” ; • Submitted: 8 October 2007; • Summary: q The technological goal of the project is a PRIME intermiddleware which will connect industrial resources belonging to different layers through the middleware platforms that are normally used for connecting resources at the respective individual layers. Interoperability of resources of this level of heterogeneity requires wide utilization of semantic technologies to provide cross-layer communication services (data-level interoperability) to the resources and multi-agent technologies to provide collaboration-support services (functional protocol-level interoperability) for these resources. • Partners: IOG (University of Jyvaskyla, Coordinator), Free University of Amsterdam, University of Athens, 4 international companies
Existing tools, middleware and platforms for resource mediation, interoperability, integration, collaborative work, etc. Inter-Middleware for Intra-Enterprise Resource Integration …
How to make university resources interoperable? R so ur ity rs ce s ve ni U Re rs ity ive Un es rc University Resources ou es ?
Centralized data and metadata ni U Ontology Re ity rs ive Un es University Resources rc Metadata ou es R so ur ty ce s i rs ve Metadata
ni U Distributed data and centralized metadata r ve Re s ou es R ou rc ty es si ity Un ive rs es rc Ontology Metadata University Resources Metadata
Distributed data and metadata rs so ur ce s ve ni U ity rs ity Re Metadata Un ive es rc ou es R Ontology Metadata University Resources
Ontology-Based Portal for National Educational and Scientific Resources Management Masha Klymova Kharkiv National University of Radioelectronics, Ukraine
Actual problems • AMBIGUITY: There is a problem of ambiguous information about educational and scientific resources – different information about the same resources is being managed by different organizational units; • INCONSISTENCY: Inconsistency of the parameters of the resources is the result of permanent uncontrolled updates of the information about the resources without synchronization; • LACK of TRUST: Reported parameters’ values are not easily verified of their correctness (it takes much time and human resources as the procedure is not automated); • LACK of FLEXIBILITY: The structure of the educational and scientific processes in organization is permanently developing and this often leads to a cardinal changes of the appropriate ICT infrastructure.
Solution Creation of a flexible, standardized and personalized, secure, web-oriented information/knowledge management system for academic resources. Such system will be organized as Ontology-Based Portal for National Educational and Scientific Resources Management Academic resources are: – Organizations (Ministry, universities, institutes, schools, faculties, departments etc); – Academic documents (study programs, curricula, recommendations, instructions, manuals, textbooks, etc; – People (administration, teachers, researchers, students, etc); – Facilities (classes, computers, equipments, etc) – Scientific products (dissertations, papers, degrees) – Processes and activities (lectures, conferences, workshops, testing, etc)
Use case scenario (1) • Wanted: to rank national Universities according to e. g. following criteria: – (A) Amount of full-time professors per student; – (B) Amount of papers published in international journals per professor; – (C) Amount of computer classes per student • using ranking formula e. g. : Rank = 0. 4 * A + 0. 5 * B + 0. 1 * C • and provide transparency of ranking procedure and results
Use case scenario (2) • Universities register each new resource online at the portal in appropriate class of the resources in the ontology: – professor; – student; – journal paper with the link to publisher; – computer class; – etc.
Use case scenario (3) • Ministry of Education (for example) creates (through appropriate interface of the portal) new criteria for the universities ranking: – (A) Amount of full-time professors per student; – (B) Amount of papers published in international journals per professor; – (C) Amount of computer classes per student • and these criteria will be automatically transformed to the appropriate formal queries to registered resources at the portal. Based on calculations on queries output results, the values to the new criteria will be obtained, saved and constantly updated in the portal for each university.
Use case scenario (4) • Ministry of Education also creates (through appropriate interface of the portal) new formula for university ranking based on the criteria: Rank = 0. 4 * A + 0. 5 * B + 0. 1 * C • and this formula will be automatically transformed to appropriate formal procedure applied to each university and their valid criteria values. Based on calculations the values of current ranks will be obtained, saved and constantly updated in the portal for each university and each registered ranking procedure.
Use case scenario (5) • Transparency of the procedure: • Anyone who has access to the resources registered at the portal can easily check content behind every value of every parameter of every criteria of every university • For example if the value of parameter: – amount of journal papers in international publishers = 35 • Then by clicking 35 one obtains the list of the papers with reference to the publisher; further clicks result to opportunity to see full text paper and the publisher web page, etc.
Other possible scenarios • • • Ranking Accreditation Licensing Monitoring Reporting …
Modules of the system Ontology store system The interface making module The Web-interface making system The reports making system Ontology server System's kernel Access control system Audit system Common managing system Tasks module Resources` registration Accreditation Ranking The ontological knowledge base structure
The interface structure Task area (e. g. “Resource registration”) The objects type selection area (e. g. “Department”) The object filter (e. g. “Kharkov University”) The document and parameters type selection area e. g. “Contingent of students” Content area Tips area
Properties of the resources Each resource registered at the portal may contain properties of two types: – computable (their value is calculated automatically based on the description joined to the field and can not be modified manually); • For example, amount of full professors, average of personnel, etc. – atomic (their values are being set evidently pointing out at some information resource or literal). • For example, name of department, title of paper, telephone number etc.
Computable properties • The computable properties are divided into 3 types: – The fields containing the summing operation; – The fields containing a typical formula; – The combined fields; • For each of the types its own information presentation system is performed.
Ongoing projects • EU Tempus Tacis SCM Project T 020 B 06 (2007 -2008) Title: “Towards Transparent Ontology-Based Accreditation” • Ministry of Science and Education of Ukraine project (2006 - …) Title: “Ontology-Based Portal for Management and Evaluation of National Scientific and Educational Resources”
More details about Ukrainian portal • Full presentation about the portal: – http: //www. cs. jyu. fi/ai/Onto. Portal-2007. ppt • Seminar with the portal presentation: – Tomorrow, 2 November, 13: 00, University of Jyvaskyla, Agora (Mattilanniemi 2), Room C 301 (“Aquarium”)
Summary “Ask not what the Semantic Web Can do for you, ask what you can do for the Semantic Web” Hans-Georg Stork, European Union http: //lsdis. cs. uga. edu/Sem. NSF • Presentation slides can be downloaded from: – http: //www. cs. jyu. fi/ai/Terziyan_Klymova. ppt