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LOD2 Plenary Meeting Vienna – 2012/03/21 – Page 1 http://lod2.euCreating Knowledge out of Interlinked Data
LOD2 Presentation . 02.09.2010 . Page http://lod2.euFreie Universität Berlin
Translate heterogeneous data from the Web of Linked Data into a clean local target representationProvide open-source software components for:– Link Generation– Vocabulary Mapping– Linked Data quality assessment– Linked Data Fusion
First Linking Assist/Silk Workbench (D4.1.1) has been delivered in February 2012– Define Data Sources (e.g. SPARQL endpoint, RDF dump)– Specify the types of resources which should be interlinked– Build linkage rules supported by maching learning– Evaluate the quality of linkage rules
Preliminary work on Korean Resource Linking Assist– Transformed test datasets into RDF.– This data will be an input to Korean resource linking module. – Finished preliminary design of the Korean resource linking
Use Active learning to reduce the manual effort and required expertise to interlink data sources– Automating the generation of a linkage rule.– The user only confirms or declines a set of example links.
Improving the usability based on user-feedbackFirst results for the Y2 review meetingFinal deliverable D4.1.2 (Second Linking Assist Release) in February 2013
Partners: NUIGGoals:– To research and develop LATC well beyond 2012 into 2014– Interlinking recommendations– Interaction with data linkage validator from WP3
Progress:– First version of Data Interlinking Environment (D4.2.1)
submitted in December 2011– Combines Analytics Graph produced from Sindice data
New Sindice datasource for the linking of datasets.Dataset suggestion based on keywords, classes, and datasets Autocompletion for data types when executing linking tasks.A retrieval method for entity properties to also aid in the execution of linking tasks.
Survey on the State of the Art in Mapping, Quality Assessment and Data Fusion (D4.3.1) finished in February 2011Conceptual Design and Implementation of Metrics (D4.3.2) finished in February 2012Released first prototype of Sieve, a Linked Data Quality Assessment and Fusion framework– Allows Web data to be filtered according to different data quality
assessment policies – Provides for fusing Web data according to different conflict
resolution methods.– http://sieve.wbsg.de– D4.3.2: Release of the data quality assessment tool (August 2012)
Specification of the Mapping Publication and Discovery Language (D4.4.1) finished in June 2011Implementation of the Mapping Publication and Discovery Framework (D4.4.2 ) finished in February 2012.– Adapted the R2R Framework based on the use cases in LOD2. – Conducted various experiments to demonstrate the
performance and scaling behavior for translating data sets (http://www.assembla.com/spaces/ldif/wiki/Benchmark)
– Implementation published under the terms of the Apache License
Task 4.4a: Schema Mapping Robust to Modeling Style
Partners: UEPGoal: Extend the methods and tools of schema matching discovery (from the original Task 4.4) by ontology transformation methods implemented within the (enhanced) PatOMat framework Start: March 2012First deliverable in December 2012
Partners: FUB, ULEIGoal:– Build a Data Fusion Component which fuses data from
multiple sources– Fuse multiple entities representing the same real-world object
into a single, consistent and clean representationFirst deliverable:– Initial release of Data Fusion Component (D4.5.1). – Deadline: 31.08.12– Integrating the data quality assessment module (Sieve)
5 Deliverables submitted in the last 6 months:ULEI and FUB submitted the First Linking Assist (D4.1.1)NUIG submitted the first version of the Data Linking Environment Release (D4.2.1)FUB finished the Conceptual Design and Implementation of Quality Assessment Metrics (D 4.3.2)FUB finished the Implementation of the Mapping Publication and Discovery Framework (D4.4.2)Zemanta submitted the first release of the LOD-enabled version of Google Refine for review (D4.6.1)