4 th Linked Data on the Web Workshop (LDOW 2011)
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1 WWW th March 2011, Hyderabad, India 4 th Linked Data on the Web Workshop (LDOW 2011) Christian Bizer, Freie Universität Berlin, Germany Tom Heath, Talis, UK Tim Berners-Lee, W3C/MIT, USA Michael Hausenblas, Richard Cyganiak, DERI, Irland
2 Programme Introduction 9:00-9:25: Introduction to the Workshop and Overview of the State of the Web of Data (Christian Bizer, Tom Heath, Tim Berners-Lee, Michael Hausenblas) Session 1: Publishing Linked Data 9:25-9:45: A Privacy Preference Ontology (PPO) for Linked Data (Owen Sacco, Alexandre Passant) 9:45-10:10: Publishing Provenance Information on the Web using the Memento Datetime Content Negotiation (Sam Coppens, Erik Mannens, Davy Van Deursen, Patrick Hochstenbach, Bart Janssens, Rik Van De Walle) Coffee Break 10:10-10:40
3 Programme Session 2: Infrastructure and Architectures 10:40-11:00: Augmenting the Web of Data using Referers (Hannes Mühleisen, Anja Jentzsch) 11:00-11:25: RESTful writable APIs for the web of Linked Data using relational storage solutions (Antonio Garrote, María N. Moreno García) 11:25-11:50: How Caching Improves Efficiency and Result Completeness for Querying Linked Data (Olaf Hartig) 11:50-12:15: A Main Memory Index Structure to Query Linked Data (Olaf Hartig, Frank Huber) Lunch Break 12:15-14:00
4 Programme Session 3: Linked Data Applications 14:00-14:20: LiDDM: A Data Mining System for Linked Data (Venkata Narasimha Pavan Kappara, Ryutaro Ichise, Vyas O.P.) 14:20-14:45: Talash: Friend Finding In Federated Social Networks (Ruturaj Dhekane, Brion Vibber) 14:45-15:05: Automatically Annotating Text with Linked Open Data (Delia Rusu, Blaz Fortuna, Dunja Mladenic) 15:05-15:30: Coffee Break
5 Programme Session 4: Exploiting the Web of Data as a Whole 15:30-15:50: Identifying Relevant Sources for Data Linking using a Semantic Web Index (Andriy Nikolov, Mathieu D'Aquin) 15:50-16:15: Re-using Cool URIs: Entity Reconciliation Against LOD Hubs (Fadi Maali, Richard Cyganiak, Vassilios Peristeras) 16:15-16:40: Open ebusiness Ontology Usage: Investigating Community Implementation of GoodRelations (Jamshaid Ashraf, Richard Cyganiak, Sean O'Riain, Maja Hadzic) Discussion 16:40-17:40: Next Steps and Research Challenges for Linked Data LOD Gathering / Workshop Dinner 19:00: La Cantina (next to the pool)
6 LDOW th March 2011, Hyderabad, India State of the Web of Data Christian Bizer, Freie Universität Berlin, Germany Anja Jentzsch, Freie Universität Berlin, Germany Richard Cyganiak, DERI, Irland
7 Statistics based on LOD Data Set Catalog on CKAN LOD Dataset Page in ESW Wiki LinkingOpenData/DataSets Detailed statistics available at Guidelines for adding your own datasets LinkingOpenData/DataSets/CKANmetainformation
8 1. Growth
9 Growth of the Web of Data November 2010
10 The Growth in Numbers Year Datasets Triples Growth % % %
11 The Growth by Domain Domain Triples (June 2009) Triples (Nov 2010) Growth Geographic % Libraries % Media % Life sciences % Cross-domain % User-generated % Government Total %
12 Uptake in the Government Domain The EU is pushing Linked Data (LOD2, LATC, EuroStat) W3C egovernment Interest Group
13 Uptake in the Libraries Community Institutions publishing Linked Data Library of Congress (subject headings) German National Library (PND dataset and subject headings) Swedish National Library (Libris - catalog) Hungarian National Library (OPAC and Digital Library) Deutschen Zentralbibliothek für Wirtschaftswissenschaften (subject headings) The Europeana project is moving towards Linked Data W3C Library Linked Data Incubator Group
14 2. Compliance with Best Practices
15 RDF Links between Datasets Number of outgoing links per data set Number of linked target data sets
16 Provenance and Licensing Metadata Licensing Metadata 18 (9.05 %) out of the 207 data sources provide machine-readable licensing information. 181 (90.95 %) out of the 207 data sources do not provide machine-readable licensing information. Provenance Metadata 50 (25.25 %) out of the 207 data sources provide machine-readable provenance information. 148 (74.75 %) out of the 207 data sources do not provide machine-readable provenance information.
17 Usage of Common Vocabularies Prefix Namespace Used by dc 66 (31.88 %) foaf 55 (26.57 %) dcterms 38 (18.36 %) skos 29 (14.01 %) akt 17 (8.21 %) geo 14 (6.76 %) mo 13 (6.28 %) bibo 8 (3.86 %) vcard 6 (2.90 %) frbr 5 (2.42 %) sioc 4 (1.93 %)
18 Publish Vocabulary Mappings on the Web Map proprietary terms to other vocabularies using owl:equivalentclass, owl:equivalentproperty rdfs:subclassof, rdfs:subpropertyof < owl:equivalentclass < Currently 9 (7.32 %) out of the 123 data sources that use proprietary terms provide mappings to other vocabularies for their terms.
19 3. Conclusions
20 For Data Publishers Make your data easily consumable by following Best Practices concerning RDF Links Licensing and Provenance Metadata Widely-used Vocabularies Publication of Vocabulary Mappings on the Web Problem: This requires effort
21 Effort Distribution between Publisher and Consumer Consumer data mines links Effort Distribution Publisher provides links Links as hints
22 Effort Distribution between Publisher and Consumer Consumer data mines mappings Effort Distribution Publisher reuses vocabularies and provides mappings Self-descriptive Data
23 Somebody-Pays-As-You-Go The overall data integration effort is split between the data publisher, the data consumer and third parties. Data Publisher publishes data as RDF publishes data in a self-descriptive fashion sets links and publishes mappings Fix Overall Data Integration Effort Third Parties set links pointing at your data publish mappings to the Web Publisher s Effort Third Party Effort Data Consumer has to do the rest using data mining techniques for identity resolution and schema matching Consumer s Effort
24 Thanks! References State of the LOD Cloud Document Linked Data - Evolving the Web into a Global Data Space Book
25 Open Discussion Lessons Learned and Next Steps
26 Topics 1. Application Architectures (Summary: Tim) Lessons Learned Future Directions 2. Ontology and Vocabulary Deployment (Summary: Ivan) Lessons Learned Future Directions 3. Studying the Web of Data (Summary: Nigel) What approaches should we use? What does Web Science contribute? 4. Is Linked Data over-engineered and too complicated for the real-world? (Summary: Hugh) Should the standards be simplified? Should the expectations concerning data providers be lowered?
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