]> The Knowledge Engineering Group at the University of Economics, Prague (UEP) has been involved in semantic web research since 2001, focusing on ontology matching, ontological engineering, as well as approaches based on data mining and text mining. Its recent activities address applications of Linked Data in government and e-commerce. In early 2010 it initiated a Czech and Slovak Linked Data initiative called Semanti-CS, which aims to promote Linked Data publication and usage. The Knowledge Engineering Group at the University of Economics, Prague (UEP) has been involved in semantic web research since 2001, focusing on ontology matching, ontological engineering, as well as approaches based on data mining and text mining. Its recent activities address applications of Linked Data in government and e-commerce. In early 2010 it initiated a Czech and Slovak Linked Data initiative called Semanti-CS, which aims to promote Linked Data publication and usage. It also maintains close contacts with the Czech Statistical Office and with many of the developers of Czech government information systems. UEP participated in the first mainstream semantic web EU projects, OntoWeb and Knowledge Web, and in 2006-2008 as full partner in the K-Space FP6 NoE on multimedia semantics. The group was also involved in several FP7 proposals (incl. one NoE and one IP that passed to the hearings in Call 5), but eventually has not succeeded in getting funded in the Programme. 12 2012-08-31 2012-05-31 D4.4.2a – Implementation of the mapping publication and discovery framework with advanced handling of style heterogeneity Zemanta builds a contextual engine that takes text, analyzes it, and finds related content that compliments the original text (images, related stories, links and tags). The engine is built around contextual analysis and semantics, runs in real time, and is quite scalable. Zemanta was founded in 2007 and in March 2008 launched a service for bloggers. It now serves 100.000 monthly active bloggers. Zemanta developed additional services for professional media, an API for third party developers, and a contextual service for email (gMail and Yahoo Mail). Zemanta builds a contextual engine that takes text, analyzes it, and finds related content that compliments the original text (images, related stories, links and tags). The engine is built around contextual analysis and semantics, runs in real time, and is quite scalable. Zemanta was founded in 2007 and in March 2008 launched a service for bloggers. It now serves 100.000 monthly active bloggers. Zemanta developed additional services for professional media, an API for third party developers, and a contextual service for email (gMail and Yahoo Mail). Zemanta's current focus is a novel business model of advertising tied to the process of content creation. To achieve that Zemanta is pursuing cutting edge technologies in natural language processing and information retrieval and at the same time exploring new grounds in user interfaces for web content authoring applications. Zemanta indexes 100.000 online sources that are pooled together and matched against user generated content. On top of that we link to multiple specialized databases such as Wikipedia, Amazon, Flickr, IMDB, and Getty images to recommend relevant stories and images, part of the engine relies on LOD. Zemanta relies on the contextual engine being state of the art since its competition is international. Zemanta does constant research to improve its engine both by increasing precision through better natural language processing and by including additional data sources into the knowledge base to increase recall. Zemanta's technologies are available exclusively as SaaS, so research and development can be quickly incorporated into our main product and rolled out to all users simultaneously. Zemanta is a company funded by UK and US venture capital firms, its base is in Ljubljana, Slovenia where all the research and development is done. Our primary markets are English speaking areas especially US. 11 The general aim of this work package is to establish a worldwide focal point for academic and industry parties interested in contributing to or taking advantage of the novel Linked Data methodologies and components, which will emerge in the project. 11 WP10 – Training, Dissemination, Community Building, Fertilization Realizing the vision of LOD2 together with the ones of the three use cases will have significant socio-economic impact. Standardization of such an architecture and exploitation of knowledge and technical results (and related IPR) is covered in this work package. 12 The project management will entail strategic, project-wide as well as day-to-day central management and coordination activities. The several different management boards which will be established in the consortium will be responsible for decisions and activities of different scope and level according to their function. 13 WP3 contains tasks focused on the transformation of legacy data to RDF and Linked Data and furthermore on the improvement of existing or extracted data especially with respect to schema enrichment and ontology repair. It is complementary to WP4, which is concerned with interlinking several knowledge bases and providing unified views of them. Tasks concerning the triplification of data will be grounded on existing techniques and know-how of the consortium and will be refined during the lifetime of this project and integrated into the LOD2 Stack. Legacy data triplification represents the entry point for legacy systems to participate in the LOD cloud. The members of the Consortium are leading in the development of transformational tools such as Virtuoso Sponger, RDF Views, D2R server, Triplify, and the DBpedia framework, which have received high acceptance in the Linked Data community. 3 WP3 – Knowledge Base Creation, Enrichment and Repair While WP3 is concerned with making legacy data available via URLs – a prerequisite – and enrichment of knowledge bases, this WP addresses automatic and semi-automatic link creation with minimal human interaction, evolvement of knowledge bases under the aspect of linkage and schema mapping combined with Data Fusion. 4 WP4 – Reuse, Interlinking and Knowledge Fusion This work package will continue the prototyping activity under WP1/Task 1.4 by fully integrating the individual components developed in WP2-5 into a ready-to-use LOD2 Stack and associated APIs. The primary goal of the LOD2 Stack integration is to enable communities of practice to rapidly create domain specific Linked Data applications. Consequently, the LOD2 Stack will support the whole life cycle of Linked Data from creation over enrichment, interlinking, fusing to maintenance. The stack will be very versatile, for all functionality we will define clear interfaces, which enable the plugging in of alternative third-party implementations. We will also provide a stack configurator, which enables potential user to create their own personalized version of the LOD2 Stack, which contains only those functions relevant for their usage scenario. 6 WP6 – Interfaces, Component Integration & LOD2 Stack The objective of this use case is to explore and demonstrate the application of linked data principles for procuring contracts in the public sector. As can be seen by the “Use of eProcurement services” indicator of the Digital agenda there is a large gap between advanced countries (e.g. Ireland 35%) and countries with low online participation of enterprises in public tenders (e.g. Czech Republic with only 10%). Consequently, the overall aim of the work package is to have a business impact and achieve an effective resource allocation through emulating the market process of meeting supply and demand operating in a linked data infrastructure. This distributed approach does not depend on the existence of one place where the commercial transaction happens, such as an e-shop or a dedicated application for tenders. Instead, the matchmaking of public sector demand with commercial suppliers, which enables to find a match and initiate a commercial transaction, operates on data from multiple sources distributed throughout the Web. The objective of this use case is to explore and demonstrate the application of linked data principles for procuring contracts in the public sector. As can be seen by the “Use of eProcurement services” indicator of the Digital agenda there is a large gap between advanced countries (e.g. Ireland 35%) and countries with low online participation of enterprises in public tenders (e.g. Czech Republic with only 10%). Consequently, the overall aim of the work package is to have a business impact and achieve an effective resource allocation through emulating the market process of meeting supply and demand operating in a linked data infrastructure. This distributed approach does not depend on the existence of one place where the commercial transaction happens, such as an e-shop or a dedicated application for tenders. Instead, the matchmaking of public sector demand with commercial suppliers, which enables to find a match and initiate a commercial transaction, operates on data from multiple sources distributed throughout the Web. 13 13 10 10 WP9a – LOD2 for a Distributed Marketplace for Public Sector Contracts WP9a – LOD2 for a Distributed Marketplace for Public Sector Contracts