]> Project coordination; develop knowledge structuring and enrichment algorithms as well as browsing, visualization and authoring interfaces; collaborate with OKFN in order to employ LOD2 results in the PublicData.eu use case. Universität Leipzig is one of the oldest (founded 1409) and largest (30.000 students) universities in Germany and focuses on interdisciplinary research in the life sciences, cognitive sciences and linguistics as well as mathematics and computer science. The Institute for Applied Computer Science (InfAI) at Universität Leipzig hosts world class research groups in service sciences, knowledge engineering and management as well as natural language processing. The approximately 20 researchers of the Agile Knowledge Engineering and Semantic Web (AKSW) research group at InfAI are establishing theoretical results and scalable implementations for the field. Particular emphasis is given to areas such as ontology creation and manipulation, knowledge extraction, ontology learning and information & data integration on the Semantic Web. The implemented tools and services developed by the group enjoy considerable popularity, the open-source semantic web framework OntoWiki for example is downloaded more than 500 times each month and applied in usage scenarios ranging from the authoring of bio-medical ontologies to knowledge management for the enterprise. InfAI is leading several large-scale collaborative research projects on the national and EU levels such as OntoWiki (EU FP7), Internet Media Businesses (EU SMART), PreBIS (German BmbF) and Factory ServNet (Eureka) and participates in many more. 1 2011-10-31 D10.1.2 – LOD2 PhD workshop and summer school 2010-11-30 D10.2.1 – Continuously updated project website 2011-08-31 2011-06-30 2012-08-31 D11.4.1 – Organization of a workshop and networking event targeted at other EU-funded projects 2013-08-31 D11.4.2 – Organization of a workshop and networking event targeted at other EU-funded projects 2014-08-31 D11.4.3 – Organization of a workshop and networking event targeted at other EU-funded projects 2011-11-30 D12.1.1 – Updated Implementation Plan Including the Updated Dissemination Plan 2013-02-28 D12.1.2 – Updated Implementation Plan Including the Updated Dissemination Plan 2011-02-28 D12.2.1 – Intermediate Project Reports 2013-02-28 D12.2.3 – Intermediate Project Reports 2014-02-28 D12.2.4 – Intermediate Project Reports 2011-08-31 D12.3.1 – Yearly Cost Statement Including Yearly Project Report 2012-08-31 D12.3.2 – Yearly Cost Statement Including Yearly Project Report 2013-08-31 D12.3.3 – Yearly Cost Statement Including Yearly Project Report 2014-08-31 D12.3.4 – Yearly Cost Statement Including Final Project Report 2010-09-30 D12.4.1 – Website and Online Collaboration Platform 2010-11-30 D12.5.1 – Project Fact Sheet Version 1 2011-08-31 D12.5.2 – Project Fact Sheet Version 2 2012-08-31 D12.5.3 – Project Fact Sheet Version 3 2013-08-31 D12.5.4 – Project Fact Sheet Version 4 2014-08-31 The results of T2.2 and T2.3 are complete and production strength. The database adapts to workload and configuration changes and creates and discards auxiliary structures as needed. 2012-02-29 D2.4 – Adaptive caching of intermediate joins and inferences 2011-02-28 D3.1.1 – State-of-the-Art Report for Extraction from Structured Sources 2012-04-30 2011-04-30 2011-04-30 2011-08-31 D3.3.1 – Release of Knowledge Base Enrichment Algorithms 2012-08-31 D3.3.2 – Release of Knowledge Base Enrichment User Interface 2013-08-31 D3.3.3 – Evaluation of Knowledge Base Enrichment 2011-02-28 D3.4.1 – Report on Automatically Detectable Modelling Errors and Problems 2012-12-31 2013-12-31 2012-02-29 2013-02-28 D4.1.2 – Second Linking Assist Release 2011-08-31 D5.1.1 – Initial release faceted spatial-semantic browsing component 2012-10-31 D5.1.3- Final release faceted spatial-semantic browsing component 2012-04-30 2013-02-28 2012-12-31 D9.2.1 – Release of PublicData.eu including personalization features CWI will be primarily involved in WP2 and work together with OpenLink on improving RDF data management with state-of-the-art database research approaches. CWI will be involved with a minor stake in WP5 in order to evaluate and adapt browsing and navigation in large-scale knowledge bases. The Stichting Centrum voor Wiskunde en Informatica (CWI) is the Dutch national research institute for mathematics and computer science. It is a private, non-profit organization located at the Science Park Amsterdam. CWI’s mission is twofold: To perform frontier research in mathematics and computer science, and to transfer new knowledge in these fields to society. This is realized by several means. In addition to the standard ways of disseminating scientific knowledge, CWI actively pursues joint projects with external partners, provides consulting services, and stimulates the creation of spin-off companies. Special efforts are made to make research results known to non-specialist circles, ranging from researchers in other disciplines to the public at large. CWI also manages the Benelux Office of the W3C and hosts both the Semantic Web Activity Lead and the chair of the XHTML and XForms Working Group. CWI has always been very successful in participating in European research programmes (e.g. VITALAS, K-SPACE, QAP, CREDO, MUSCLE, and others) and large-scale national research programmes (e.g., programmes BRICKS, MultimediaN, and VL-e; NWO Veni, Vidi, Vici grants). It has extensive experience in managing these collaborative research efforts. CWI is also strongly embedded in Dutch university research: about twenty-five of its permanent senior researchers hold part-time positions as professors at universities and many projects are carried out in cooperation with university research groups. CWI receives a basic funding from the Netherlands Organization for Scientific Research (NWO), amounting to about two third of the institute’s total income. The remaining third is obtained through national research programmes, international programmes, and contract research commissioned by industry. CWI hosts a staff of 235 full time employees, 50 permanent scientific staff, 135 temporary scientific staff, and 50 support staff. The Information Systems (INS) group led by Prof. Dr. Martin Kersten is participating in the LOD2 proposal. 2 DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. It currently already contains a tremendous amount of valuable knowledge extracted from Wikipedia. The DBpedia knowledge base will be used for evaluation LOD2’s interlinking, fusing, aggregation and visualization components. The DBpedia multi-domain ontology will be used as background-knowledge for the LOD2 applications (WP7, WP8 and WP9), and as an alignment and annotation ontology for LOD in general. DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. It currently already contains a tremendous amount of valuable knowledge extracted from Wikipedia. The DBpedia knowledge base will be used for evaluation LOD2’s interlinking, fusing, aggregation and visualization components. The DBpedia multi-domain ontology will be used as background-knowledge for the LOD2 applications (WP7, WP8 and WP9), and as an alignment and annotation ontology for LOD in general. DL-Learner is a tool for supervised Machine Learning in OWL and Description Logics. It can learn concepts in Description Logics (DLs) from user-provided examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. It extends Inductive Logic Programming to Descriptions Logics and the Semantic Web. The goal of DL-Learner is to provide a DL/OWL-based machine learning tool to solve supervised learning tasks and support knowledge engineers in constructing knowledge and learning about the data they created. The DL-Learner software learns concepts in Description Logics (DLs) from examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. It extends Inductive Logic Programming to Descriptions Logics and the Semantic Web. The goal of DL-Learner is to provide a DL/OWL based machine learning tool to solve supervised learning tasks and support knowledge engineers in constructing knowledge and learning about the data they created. Purposes of Class Expression Learning: <ol> <li>Learn Definitions for Classes: Based on existing instances of an OWL class, DL-Learner can make suggestions for class definitions to be included as an owl:equivalentClass or rdfs:subClassOf Axiom. As the algorithm is biased towards short and human readable definitions, a knowledge engineer can be supported when editing the TBox of an ontology (see <a href="http://dl-learner.org/wiki/ProtegePlugin">Protege Plugin</a>).</li> <li>Find similar instances: DL-Learner's suggested class expressions can be used to find similar instances via retrieval (Concept definitions as search). Scalable methods allow the generation of recommendations on the fly, e.g. in a web scenario (see <a href="http://navigator.dbpedia.org/">DBpedia Navigator</a> – in experimental stage).</li> <li>Classify instances: The learned class descriptions can be used in a typical classification scenario, i.e. to decide for unknown instances whether they belong to a certain class. Common ILP benchmarks have been tested with DL-Learner. On the <a href="http://dl-learner.org/wiki/Carcinogenesis">Carcinogenesis page</a>, DL-Learner competes with other state-of-the-art ILP algorithms.</li> The DL-Learner software learns concepts in Description Logics (DLs) from examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. It extends Inductive Logic Programming to Descriptions Logics and the Semantic Web. The goal of DL-Learner is to provide a DL/OWL based machine learning tool to solve supervised learning tasks and support knowledge engineers in constructing knowledge and learning about the data they created. Purposes of Class Expression Learning: <ol> <li>Learn Definitions for Classes: Based on existing instances of an OWL class, DL-Learner can make suggestions for class definitions to be included as an owl:equivalentClass or rdfs:subClassOf Axiom. As the algorithm is biased towards short and human readable definitions, a knowledge engineer can be supported when editing the TBox of an ontology (see <a href="http://dl-learner.org/wiki/ProtegePlugin">Protege Plugin</a>).</li> <li>Find similar instances: DL-Learner's suggested class expressions can be used to find similar instances via retrieval (Concept definitions as search). Scalable methods allow the generation of recommendations on the fly, e.g. in a web scenario (see <a href="http://navigator.dbpedia.org/">DBpedia Navigator</a> – in experimental stage).</li> <li>Classify instances: The learned class descriptions can be used in a typical classification scenario, i.e. to decide for unknown instances whether they belong to a certain class. Common ILP benchmarks have been tested with DL-Learner. On the <a href="http://dl-learner.org/wiki/Carcinogenesis">Carcinogenesis page</a>, DL-Learner competes with other state-of-the-art ILP algorithms.</li>   </ol> OntoWiki is a tool providing support for agile, distributed knowledge engineering scenarios. It facilitates the visual presentation of a knowledge base as an information map, with different views on instance data. It enables intuitive authoring of semantic content, with an inline editing mode for editing RDF content, similar to WYSIWIG for text documents. OntoWiki is a tool providing support for agile, distributed knowledge engineering scenarios. It facilitates the visual presentation of a knowledge base as an information map, with different views on instance data. It enables intuitive authoring of semantic content, with an inline editing mode for editing RDF content, similar to WYSIWIG for text documents. OntoWiki provides sophisticated means for navigating, visualising and authoring of RDF-based Knowledge Bases. It serves and consumes Linked Data and comprises a comprehensive middleware API for building custom Semantic Web applications. We refer to it as a Wiki, since our focus is on simplicity, adaptability and collaboration. However, other than annotating text-based Wiki pages with a special syntax (as suggested by text-based Semantic Wiki approaches), Onto Wiki uses RDF in the first place to represent information. For human users, Onto Wiki allows to create different views on data, such as tabular representations or maps. For machine consumption it supports various RDF serialisations as well as RDFa, Linked Data and SPARQL interfaces. Since its introduction in 2006, the application has evolved into a framework for building Semantic Web applications and was recently updated to support the collaboration across multiple domains and application via Semantic Pingback and RDFauthor Triplify provides a building block for the “semantification” of Web applications. As a plugin for Web applications, it reveals the semantic structures encoded in relational databases by making database content available as RDF, JSON or Linked Data. Triplify makes Web applications easier mashable and lays the foundation for next-generation, semantics-based Web searches. Triplify provides a building block for the “semantification” of Web applications. Triplify is a small plugin for Web applications, which reveals the semantic structures encoded in relational databases by making database content available as RDF, JSON or Linked Data. Triplify is very lightweight: It consists of only a few files with less than 500 lines of code. For a typical Web application a configuration for Triplify can be created in less than one hour and if this Web application is deployed multiple times (as most open-source Web applications are), the configuration can be reused without modifications. Triplify makes Web applications easier mashable and lays the foundation for next-generation, semantics-based Web searches. Objectives of this work package are: (1) to develop use case specifications and to collect user requirements by consulting the communities of practice relevant for the LOD2 use cases and additional prospective application scenarios, (2) to identify technical constraints as well as standards, (3) to produce the architecture and the LOD2 Stack design and (4) to produce an early prototype of the LOD2 Stack in the first year. 1 WP1 – Requirements, Design and LOD2 Stack Prototype 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. 10 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. 11 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. 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. 48 48 1 1 12 12 WP12 – Project Management WP12 – Project Management This work package implements the LOD2 knowledge store component needed for managing the Web of Linked Data as a vast database. The starting point is OpenLink Virtuoso and MonetDB on the database side and Sindice on the information retrieval side. The present data volumes handled are around 10 billion RDF triples and the target is over the 1 trillion triples. The approach is scale-out out (physical) complemented with significant scalability improvements in the RDF engine (logical): Physically, when data grows, servers can be added and data redistributed without interruption of service, ibid for server failure. Logically, there is no point answering questions nobody is asking. Therefore the base data is kept as RDF with text indexing and search ranking. Additional inference results or indices for caching joins are made as a by-product of querying; further exploitation of such (partially) materialized inferences through the graph at run-time is to exploit structural correlations in the graphs. 2 WP2 – Storing and Querying Very Large Knowledge bases 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. 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. 28 28 1 1 3 3 WP3 – Knowledge Base Creation, Enrichment and Repair 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 The objectives of WP5 are to develop new browsing, visualization and authoring interfaces for LOD, which support a wide range of devices (from mobile phones to desktop PCs), which integrate heterogeneous information from various sources and support the evolution of both instance data as well as information structures over time. In order to achieve these objectives we will explore new browsing and visualization paradigms. 5 WP5 – Adaptive Linked Data Visualization, Browsing and Authoring 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 purpose of this PublicData.eu use case is to increase public access to high-value, machine-readable data sets generated by the European, national as well as regional governments and public administrations. Although this effort will be similar to developments in other parts of the world, for the case of Europe it will be more challenging due to the larger organizational and linguistic diversity and thus represent an ideal application scenario for Linked Data technologies. 9 WP9 – Use Case 3: LOD2 for Citizen – PublicData.eu