Online Index Extraction from Linked Open Data Sources
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The document discusses a method for online index extraction from Linked Open Data (LOD) sources, focusing on automating the generation of statistical indexes to describe dataset structures. It highlights the challenges of poor documentation, limitations in existing catalogs, and the intricacies involved in manually exploring datasets. The process is proven efficient using various extraction techniques and demonstrates the scalability of querying multiple datasets simultaneously.
Online Index Extraction from Linked Open Data Sources
- 1. DB Group @ UNIMO Fabio Benedetti Sonia Bergamaschi Laura Po Department of Engineering “Enzo Ferrari” University of Modena & Reggio Emilia LD4IE 2014 – Riva Del Garda, Italy Online Index Extraction from Linked Open Data Sources Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 1
- 2. DB Group @ UNIMO 2 • Selection of a relevant LOD source • Statistical indexes • Architecture Overview • Performance Evaluation • LODeX & Conclusions LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 3. DB Group @ UNIMO 3 Schmachtenberg, Max, Christian Bizer, and Heiko Paulheim. "Adoption of the Linked Data Best Practices in Different Topical Domains." The Semantic Web–ISWC 2014. Springer International Publishing, 2014. 245-260. LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 4. DB Group @ UNIMO 4 2009 2014* Domain Number % Number % Cross-domain 41 13.95% 41 4.04% Geographic 31 10.54% 21 2.07% Government 49 16.67% 183 18.05% Life sciences 41 13.95% 83 8.19% Media 25 8.50% 22 2.17% Publications 87 29.59% 96 9.47% Social web 0 0.00% 520 51.28% User-generated content 20 6.80% 48 4.73% Total 294 1014 *Only 570 datasets belong to the LOD cloud, the remaining datasets do not contain ingoing/outgoing links to the LOD Cloud. LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources 2009 Domain Cross-domain Geographic Government Life sciences Media Publications Social web 2014
- 5. DB Group @ UNIMO 5 1. The documentation of the dataset – The documentation can be poor or absent – There are no standard to provide the documentation – Sometime it is provided as an RDF file in XML format 2. Searching features of existing catalogs (i.e. Datahub) – The metadata contain poor information – None information about the structure of the dataset is used by the search engine 3. The manual exploration of the Dataset – It is required a good knowledge of SPARQL language – It is a time consuming task LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 6. DB Group @ UNIMO 6 To automatically extract a set of indexes able to describe the structure of a LOD dataset How to describe the dataset LOD datasets can have different purpose and structure: • Ontology/Vocabulary (OWL & RDFS constraints) • Open Data (i.e. generated from existing RDBMS) The indexes should maximize the value of the information extraction from heterogeneous datasets Online & Automatic extraction • It does not require any additional information by the user • It works with SPARQL endpoints – We have to handle the bad performance issues of these Datasets LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 7. DB Group @ UNIMO 7 We can think the entire set of RDF triples partitioned between: • Intensional Knowledge • Extensional Knowledge The Intensional knowledge • It contains the RDFS or OWL constraints of the Ontology • It represents the T-Box components of the knowledge base The Extensional knowledge • It contains the entities of the real word described in the dataset • It represents the A-Box components of the knowledge base • its triples cover most of the dataset Instantiated classes act as a bridge between the two type of knowledge LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 8. DB Group @ UNIMO 8 ex:sector rdf:label rdf:Property owl:Class rdfs:domain rdf:type rdf:type ex:Sector ex:Organization sector rdf:type rdf:type rdf:type ex:sector Intensional Knowledge Instantiated LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources rdfs:range rdf:label rdf:type owl:ObjectProperty rdf:type sector1 organization1 ex:sector dc:name “Energy” organization2 Classes Extensional Knowledge
- 9. DB Group @ UNIMO 9 The Statistical Indexes are grouped in three categories: • Generic • Intensional • Extensional Name Description Structure Category t Number of Triples Integer LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources Generic c Number of Classes Integer I Number of Instances Integer Cl Class List List(name, n. Instances) Pl Property List List(name, n. occurrence) IK Intensional K. triples List(s, p, o) Intensional Sc Subject Class List(c, p, n. occurrence) SCl Subject Class to literal List(c, p, n. occurrence) Extensional Oc Object Class List(c, p, n. occurrence)
- 10. DB Group @ UNIMO 10 ex:Sector ex:Organization rdf:type sector1 rdf:type Subject Class ex:sector rdf:type Subject Class to literal ex:Sector ex:Organization rdf:type sector1 rdf:type LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources organization1 ex:sector dc:name “Energy” organization2 Sc - Subject Class SCl - Subject Class to literal Oc -Object Class S ex:Organization ex:Sector ex:Sector P ex:sector dc:name ex:sector n 2 1 1 organization1 ex:sector dc:name “Energy” ex:sector Object Class
- 11. DB Group @ UNIMO 11 It takes in input a list of URLs of SPARQL endpoints A set of Statistical Indexes for each endpoint is the output • The IE process dynamically generates the SPARQL query used to extract the Statistical Indexes • It works in parallel querying different datasets • Partial results and the Statistical Indexes are stored in a NoSQL DB LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 12. DB Group @ UNIMO 12 General Statistic Extraction • It uses 6 different queries to extract the indexes of this group Intensional Knowledge Extraction • The extraction of the Intensional knowledge is performed through an iterative algorithm • The algorithm traverses the graph starting from the instantiated classes Extensional Schema Extraction • It uses different SPARQL aggregation query to extract SC, SCl and OC • Use a technique called Pattern Strategy to complete the extraction – It is a technique able to produce an higher number of less complex SPARQL query – It is used when the endpoint is not able to answer an aggregation query and it throws a timeout error A complete list of the 24 query patterns is available at http://dbgroup.unimo.it/lodexQueries LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 13. DB Group @ UNIMO 13 LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 14. DB Group @ UNIMO 14 LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources Reachable datasets 244 SPARQL 1.1 compatible 137 Extraction completed 107 Extraction completed 33 Without PS Total triples (107 datasets) 3,45 b AVG time extraction 6,12 m Total time (single process) 11,15 h Total time (9 processes) 3,35 h The test has been performed on a list of 469 Datasets • More than the 90 % completed the extraction in less than 500 s • The PS technique has proved its worth • from 33 to 107 completed the extraction • The IE process is scalable • linear correlation between number of triples and time
- 15. DB Group @ UNIMO LODeX is an online tool able to shows a visual Schema Summary for a LOD source • We made use of the statistical indexes for the generation of the Schema F. Benedetti, S. Bergamaschi, and L. Po, “A visual summary for linked open data sources” 2014, International Semantic Web Conference (Posters & Demos). 17 Summary. • Users can interact with the Schema Summary dataset and focus on the information that they are more interested in. The tool is accessible at: www.dbgroup.unimo.it/lodex Come to attend the LODeX demo at the ISWC demo session! LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 16. DB Group @ UNIMO 18 Conclusion • We are able to extract valuable indexes from a LOD dataset taking advantage of the definition of Intensional and Extensional knowledge • The process of extraction is been tested with an huge number of dataset and its efficiency and effectiveness has been proven Future Works • To extend VOID vocabulary with our descriptors • We want propose LODeX as assistance tool for LOD portals. • We are extending LODeX in order to support the automatic SPARQL query generation LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 17. DB Group @ UNIMO 19 LD4IE 2014 – Riva Del Garda, Italy Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Online Index Extraction from Linked Open Data Sources
- 18. DB Group @ UNIMO 20 Thanks for your attention! LD4IE 2014 – Riva Del Garda, Italy Online Index Extraction from Linked Open Data Sources Dot. Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia