USE OF E-PARTICIPATION TOOLS FOR SUPPORT OF POLICY MODELLING AT REGIONAL LEVEL

This paper describes application-specific and technology specifications related to ICT-based approach for the support of policy modelling as proposed in the EU funded FP7 ICT OCOPOMO project. In this particular approach strategic planning is supported by a combination of narrative scenarios, agent-based modelling, and e-Participation tools (all integrated via an ICT e-Governance platform). The policy model for a given domain is created iteratively using cooperation of several stakeholder groups (decision makers, analysts, companies, civic society, and the general public). In this paper we will provide principles and key concepts of collaborative policy modelling, but the main focus is on the discussion of high-level architecture of ICT tools and software components, envisioned platform functionality and preliminary view of detailed architecture and technological details for implementation and integration of software components. An overall approach is presented also from the view of a particular pilot application, built around development of a strategy of renewable energy use. The process of development of a new strategy is described using standard BPMN. The process models correspond to AS-IS and TO-BE (i.e. after incorporation of scenario generation and policy modelling) situations

KP-LAB Knowledge Practices Laboratory -- Specifications for the Knowledge Matchmaker (V.2.0), the Knowledge Synthesizer (V.1.0) and the Analytical and Knowledge Mining Services (V.1.0)

deliverablesThis deliverable presents specifications of three components responsible for advanced manipulation with the knowledge stored in the KP-Lab Semantic Web Knowledge Middleware (SWKM). It starts with motivating scenarios defined within various Working Knots (WKs), extracting relevant functional requirements and mapping them on the high-level requirements, of particular driving objectives and user tasks (described in deliverable [D2.4]). The first component is Knowledge Matchmaker (V2.0), which utilizes various text mining, information extraction, and heuristic methods for advanced access to and manipulation with shared knowledge artefacts according to the explicit meaning of artefacts expressed by their textual content, as well as metadata, including semantic tagging. This second version presents a set of completely new services supporting miscellaneous functionalities such as support for semantic tagging process, search for similar artefacts, information extraction capabilities, as well as recommendation services. Next two components are completely new. The Knowledge Synthesizer (V1.0) can be used to combine information found in multiple sources; this feature is necessary to allow automated merging of the conceptualizations modeled in independently edited conceptualizations. The Analytical and Knowledge Mining Services (V1.0) provide means for analyzing participation and activities within past or running knowledge creation processes, as well as for support of knowledge evolution analysis (e.g. via identification of critical patterns in selected knowledge creation processes)

ELLIOT Project Presentation #4

44 pages, Delivrable D6.3.4 (M32)The fourth and final version of the FP7 ICT ELLLIOT Project Public presentation is provided in this deliverable. Its aim is to serve for facilitating the dissemination efforts of all consortium partners, by providing common background information on the project objectives and expected resul

ELLIOT Project Presentation #4

44 pages, Delivrable D6.3.4 (M32)The fourth and final version of the FP7 ICT ELLLIOT Project Public presentation is provided in this deliverable. Its aim is to serve for facilitating the dissemination efforts of all consortium partners, by providing common background information on the project objectives and expected resul

KP-LAB Knowledge Practices Laboratory -- Specification of the SWKM Architecture (V1.0) and Core Services

deliverablesThe objective of WP5 is to develop a generic middleware supporting knowledge management services for trialogical learning applications. More precisely, the KPLab Semantic Web Knowledge Middleware (SWKM) aims to facilitate knowledge creation processes by supporting advanced interactions of collaborating learners (or workers) with knowledge artefacts (i.e. discovery, access, evolution, recommendation and mining). In this deliverable we present the high-level functionality of SWKM along with the Service-Oriented Software Architecture of the prototype system that will be developed by M12 (V 1.0) and the subsequent phases of the project. The proposed architecture broadly distinguishes three generic modules of the SWKM, i.e. the Knowledge Repository responsible for the provision of scalable persistence services for large volumes of knowledge artefacts descriptions and ontologies; the Knowledge Mediator responsible for the provision of services handling the main registry, discovery and evolution for KP-Lab knowledge artefacts; and the Knowledge Matchmaker responsible for the provision of services that support interactions of KP-Lab users with knowledge artefacts employing their semantic descriptions. The services corresponding to each one of these modules are described along with the proposed functionality for each one, based upon the motivating scenarios and the subsequent definition of the key concepts of Trialogical Learning

KP-LAB Knowledge Practices Laboratory -- Specification of the SWKM Knowledge Evolution, Recommendation, and Mining services

deliverablesThis deliverable presents the deep-level specification for the second release (M24) of the components responsible for advanced manipulation with the knowledge stored in the KP-Lab Semantic Web Knowledge Middleware (SWKM). The two components were defined in [D5.1] as Knowledge Mediator and Knowledge Matchmaker. The Knowledge Mediator services (change, comparison, versioning and registry) aim at providing functionalities to support evolving ontologies and RDF Knowledge Bases (KBs). Upon a change request, the change service will automatically determine the effects and side-effects of the request and present it to the caller for validation. A comparison service is necessary to allow one to compare two versions of an ontology or RDF KB and identify their differences. The above functionalities are coupled with a versioning system, which is used to make different versions of the same ontology (or RDF KB) persistent, and with the registry service, which allows the user to classify the stored ontologies, using some related metadata for easy access and manipulation. The Knowledge Matchmaker supports advanced mining and notification services for knowledge artefacts. It essentially enables to cluster/classify available information resources with respect to the employed ontologies, as well as, to notify about changes to content items produced/consumed within a group of learners according to explicitly subscribed preferences [DoWB]. The Notification service supports access to the knowledge repository for KP-Lab users (i.e. individual human users as well as various tools or software components) by keeping them aware of changes. Users will be able to subscribe their preferences to the KP-Lab system in order to be notified about the changes in the knowledge repository. Events (modifications) in the repository are matched with the subscriptions and notifications are propagated automatically to the users. Text Mining services are used to assist users when creating or updating the semantic descriptions of KP-Lab knowledge artefacts. The Classification Service, after a software-training period, will classify the artefacts under some pre-defined set of categories (e.g., ontology concepts), resulting in a semi-automatic generation of semantic descriptions. The Clustering Service will look for clusters of similar artefacts and automatically acquire conceptual maps from knowledge artefacts. This can lead to the update or even the creation of (new) KP-Lab ontologies managed in the sequel by the Knowledge Mediator. The services are described along with the proposed functionality for each one, based upon the motivating scenarios and the subsequent functional requirements. The functionality of the services is presented from the end-user perspective and divided into parts that form the major components of the SWKM knowledge evolution, recommendation and mining services architecture

KP-LAB Knowledge Practices Laboratory -- Specification of the SWKM Knowledge Evolution, Recommendation, and Mining services

deliverablesThis deliverable presents the deep-level specification for the second release (M24) of the components responsible for advanced manipulation with the knowledge stored in the KP-Lab Semantic Web Knowledge Middleware (SWKM). The two components were defined in [D5.1] as Knowledge Mediator and Knowledge Matchmaker. The Knowledge Mediator services (change, comparison, versioning and registry) aim at providing functionalities to support evolving ontologies and RDF Knowledge Bases (KBs). Upon a change request, the change service will automatically determine the effects and side-effects of the request and present it to the caller for validation. A comparison service is necessary to allow one to compare two versions of an ontology or RDF KB and identify their differences. The above functionalities are coupled with a versioning system, which is used to make different versions of the same ontology (or RDF KB) persistent, and with the registry service, which allows the user to classify the stored ontologies, using some related metadata for easy access and manipulation. The Knowledge Matchmaker supports advanced mining and notification services for knowledge artefacts. It essentially enables to cluster/classify available information resources with respect to the employed ontologies, as well as, to notify about changes to content items produced/consumed within a group of learners according to explicitly subscribed preferences [DoWB]. The Notification service supports access to the knowledge repository for KP-Lab users (i.e. individual human users as well as various tools or software components) by keeping them aware of changes. Users will be able to subscribe their preferences to the KP-Lab system in order to be notified about the changes in the knowledge repository. Events (modifications) in the repository are matched with the subscriptions and notifications are propagated automatically to the users. Text Mining services are used to assist users when creating or updating the semantic descriptions of KP-Lab knowledge artefacts. The Classification Service, after a software-training period, will classify the artefacts under some pre-defined set of categories (e.g., ontology concepts), resulting in a semi-automatic generation of semantic descriptions. The Clustering Service will look for clusters of similar artefacts and automatically acquire conceptual maps from knowledge artefacts. This can lead to the update or even the creation of (new) KP-Lab ontologies managed in the sequel by the Knowledge Mediator. The services are described along with the proposed functionality for each one, based upon the motivating scenarios and the subsequent functional requirements. The functionality of the services is presented from the end-user perspective and divided into parts that form the major components of the SWKM knowledge evolution, recommendation and mining services architecture