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Visual Querying LOD sources with LODeX

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Fabio Benedetti
Fabio Benedetti

This document describes LODeX, a tool for exploring and querying Linked Open Data (LOD) sources. LODeX aims to make LOD discovery and consumption easier for both skilled and unskilled users. It has two main modules: an extraction and summarization module that analyzes LOD datasets and generates schema summaries; and a visualization and querying module that allows users to browse schema summaries and build visual queries without SPARQL knowledge. The visual queries are compiled into SPARQL and executed over LOD endpoints. LODeX indexes information from the LOD cloud and aims to provide a standardized way to understand LOD dataset structures and query LOD sources.

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DBGroup@UNIMO Fabio Benedetti, Sonia Bergamaschi, Laura Po Department of Engineering “Enzo Ferrari” University of Modena & Reggio Emilia K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA
DBGroup@UNIMO 3 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 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]
DBGroup@UNIMO 4 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 4 *Only 570 datasets belong to the LOD cloud, the remaining datasets do not contain ingoing/outgoing links to the LOD Cloud. 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 2009 Domain Cross-domain Geographic Government Life sciences Media Publications Social web 2014
DBGroup@UNIMO 5 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 5 The Open Access trends encourage the publication of Open Data in form of Linked Data But Discovering and consuming LOD sources is a complex task for both skilled and unskilled user
DBGroup@UNIMO 6 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 6 • There does not exist any standard for documenting a dataset • A great number of datasets is published without a real documentation that could help on revealing their structure. To understand if a dataset really contains interesting information a user have to manually explore it using SPARQL queries. Unskilled user A user with no SPARQL knowledge cannot become a consumer of Linked Data Skilled user The task of exploring a dataset can be time consuming without having any knowledge of its structure
DBGroup@UNIMO 7 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 7 A tool for that promotes the understanding, navigation and querying of LOD sources Requirements • portable to the LOD Cloud • provide a synthetic representation of the structure of the dataset • provide visual query building functionalities hiding the complexity of Semantic Web technologies
DBGroup@UNIMO 8 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 8 Two main modules • Extraction & Summarization – Index Extraction (IE) – Post Processing (PP) LOD Cloud SPARQL Queries LODeX Post- processing Statistical Indexes LODeX Indexes Extraction Endpoint URLs Schema Summary NoSQL SPARQL Queries Schema Summary Query Orchestrator Schema Summary Visualizzation Basic QueryResults • Visualization & Querying – Schema Summary Visualization – Query Orchestrator
DBGroup@UNIMO 9 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 9 Index Extraction [1] The IE process is able to generate the SPARQL queries used to extract the different indexes. • Pattern Strategy technique – It is a technique able to produce an higher number of less complex SPARQL query Post Processing An algorithm combines the information contained in the Statistical Indexes to produce and store the Schema Summary
DBGroup@UNIMO 10 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 10 The Schema Summary is a pseudograph composed by: • C - Classes (nodes) • P - Properties (edges) And additional elements and function: • A - Attributes associated to each class – Each attribute represent the existence of a Datatype property from the instances of the class • 𝒍 - labels • l – labeling function • count - count function The Schema Summary is inferred by the distribution of the instances of a dataset
DBGroup@UNIMO 11 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 11 ex:Sector foaf:Organization owl:Class ex:sector “sector” rdf:type rdf:type rdf:Propertyrdf:type owl:ObjectProperty rdf:type sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge Intensional Knowledge ex:activity “Village electrification in the Pacific” “+41331231” rdfs:label rdfs:label rdfs:domain rdf:type ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName The information contained in the Intensional knowledge can be incomplete or absent
DBGroup@UNIMO 12 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 12 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class.
DBGroup@UNIMO 13 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 13 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class. ex:Sector foaf:Organization sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge “Village electrification in the Pacific” “+41331231” ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName
DBGroup@UNIMO 14 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 14 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class. ex:Sector foaf:Organization sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge “Village electrification in the Pacific” “+41331231” ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName
DBGroup@UNIMO 15 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 15 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class. ex:Sector foaf:Organization sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge “Village electrification in the Pacific” “+41331231” ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName
DBGroup@UNIMO 16 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 16 We use an algorithm for combining these indexes and produce a Schema Summary Name Values SC (foaf:Organization,ex:ceo,1), (foaf:Organization,ex:sector,2) SCl (foaf:Person,foaf:firstName,1), (foaf:Person,foaf:lastName,1), (foaf:Organization,ex:dbpedia:fax,1), (ex:Sector,dc:title,1), (foaf:Organization,ex:activity,1), (foaf:Organization,dbpedia:fax,1) OC (ex:Sector,ex:sector,1) (ex:Person,ex:ceo,1)
DBGroup@UNIMO 17 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 17 foaf:Organizzation 2 ex:Sector 1 ex:sector 2foaf:Person 1 ex:ceo 1 dc:title 1foaf:firstName 1 foaf:lastName 1 ex:activity 1 dbpedia:fax 1 We use an algorithm for combining these indexes and produce a Schema Summary Name Values SC (foaf:Organization,ex:ceo,1), (foaf:Organization,ex:sector,2) SCl (foaf:Person,foaf:firstName,1), (foaf:Person,foaf:lastName,1), (foaf:Organization,ex:dbpedia:fax,1), (ex:Sector,dc:title,1), (foaf:Organization,ex:activity,1), (foaf:Organization,dbpedia:fax,1) OC (ex:Sector,ex:sector,1) (ex:Person,ex:ceo,1)
DBGroup@UNIMO 18 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 18 Schema Summary Visualization Front end of the Web Application composed by three panel: • List of datasets indexed in LODeX • Schema Summary and query building panel • Refinement panel Query Orchestrator • It manages the interaction between the User and the GUI • It contains a SPARQL compiler able to compile the visual query in a SPARQL one
DBGroup@UNIMO 19 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 19
DBGroup@UNIMO 20 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 20
DBGroup@UNIMO 21 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 21 Schema Summary SPARQL compiler SPARQL query Basic Query The Visual Query has a tree structure A SPARQL compiler exploits a recursive algorithm to generate the corresponding SPARQL query Operators supported by the compiler: • AND • Optional • Filter The query is sent to the SPARQL endpoint and the results can be visualized in a tabular view • ORDER BY • LIMIT • OFFSET
DBGroup@UNIMO 22 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 22 We performed 3 different kinds of evaluation to inspect: • Portability of LODeX to SPARQL endpoints • SPARQL expressiveness • Usability of LODeX – to verify if the graph visualization of the SS is clear in representing the structure of a dataset – to prove if the visual query panel is a powerful and adequate way for generating SPARQL queries
DBGroup@UNIMO 23 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 23 We evaluate the complexity of the graph visualization with a group of group of 5 students. • Task: find a node in graphs of increasing size The test set is composed by 185 datasets taken from Datahub Result portability test Number of datasets % Huge Schema Summary (more than 80 nodes) 40 21% Offline endpoints 7 4% Not standard response 28 15% Pass the test 110 60%
DBGroup@UNIMO 24 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 24 We analyzed what kind of SPARQL query LODeX is able to generate We used as reference the queries contained in the Berlin SPARQL Benchmark [3] • LODeX is able to generate 6 of 10 queries contained in BSBM
DBGroup@UNIMO 25 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 25 We analyzed what kind of SPARQL query LODeX is able to generate We used as reference the queries contained in the Berlin SPARQL Benchmark [3] • LODeX is able to generate 6 of 10 queries contained in BSBM • UNION queries • CONSTRUCT queries • ASK queries
DBGroup@UNIMO 26 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 26 We analyzed what kind of SPARQL query LODeX is able to generate We used as reference the queries contained in the Berlin SPARQL Benchmark [3] • LODeX is able to generate 6 of 10 queries contained in BSBM • UNION queries • CONSTRUCT queries • ASK queries • All JOIN acyclic queries • All FILTER queries • All ORDER queries
DBGroup@UNIMO 27 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 27 We performed an online survey where has been enrolled 27 users The survey is divided in two parts having different goals: • Evaluate the clarity of Schema Summary • Evaluate the functionality of visual query building For each part has been designed some tasks and a SUS [4] questionnaires
DBGroup@UNIMO 28 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 28 Tasks: Datasets: • (T1)Indicate the topic of the dataset • (T2)Find out the class with the largest number of instances • (T3)Find out the classes connected to a given class chosen by us • (T4)Find out the most used attribute of a class chosen by us • Bio2RDF - INOH - pathway database of model organisms • Linked Open Aalto Data Service - Open data published by Aalto University Task Number n Correct % T1 54 48 89% T2 54 48 89% T3 27 23 89% T4 27 27 100% Total 162 148 91%
DBGroup@UNIMO 30 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 30 Tasks: Dataset: • (Q1)Return all the different category of Nobel prizes • (Q2) Return a table containing the list of winners of a Nobel prizes ordered by the name of the winner; the table has to contain the date of birth of the winner. • (Q3) Find the award files related to the award of Peter W. Higgs • (Q4) Find the organizations that won a Nobel prize after the 1999 Nobel Prizes - Linked Open Data about every Nobel Prize Task Number n Correct % Q1 27 27 100% Q2 27 26 96% Q3 27 22 81% Q4 27 23 85% Total 108 98 90%
DBGroup@UNIMO 31 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 31 We obtained a median SUS score of 85.5 • No remarkable differences between skilled and unskilled user • This score classifies the usability of LODeX as “Excellent” [5] Feedback Unskilled users write their SPARQL query for the first time “LODeX is cognitively less demanding that write SPARQL query” Browser rendering difference Starting a query can be difference and keyword search techniques could be helpful
DBGroup@UNIMO 32 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 32 Conclusion • LODeX is portable with the 60% of the datasets tested – 19% a failure induced by endpoint issues • Both skilled and unskilled users appreciated LODeX Future works • Modify the interface of LODeX according to the results of the online survey • Define clustering and new techniques of browsing to reduce the complexity of the Summary for huge dataset • Extend the group of operators supported by the SPARQL compiler
DBGroup@UNIMO 33 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 33 • [1] F. Benedetti, S. Bergamaschi, and L. Po, A visual summary for linked open data sources. 2014, International Semantic Web Conference (Posters & Demos). • [2] F. Benedetti, S. Bergamaschi, and L. Po. Online index extraction from linked open data sources. Linked Data for Information Extraction (LD4IE) Workshop held at International Semantic Web Conference, 2014. • [3] C. Bizer and A. Schultz. Benchmarking the performance of storage systems that expose sparql endpoints. • [4] J. Brooke. Sus-a quick and dirty usability scale. Usability evaluation in industry, 189(194):4–7, 1996. • [5] A. Bangor, P. Kortum, and J. Miller. Determining what individual sus scores mean: Adding an adjective rating scale.
DBGroup@UNIMO 34 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 34
DBGroup@UNIMO 35 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Thanks for your attention! Try LODeX at: http://dbgroup.unimo.it/lodex2

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Visual Querying LOD sources with LODeX

  • 1. DBGroup@UNIMO Fabio Benedetti, Sonia Bergamaschi, Laura Po Department of Engineering “Enzo Ferrari” University of Modena & Reggio Emilia K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA
  • 2. DBGroup@UNIMO 3 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 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]
  • 3. DBGroup@UNIMO 4 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 4 *Only 570 datasets belong to the LOD cloud, the remaining datasets do not contain ingoing/outgoing links to the LOD Cloud. 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 2009 Domain Cross-domain Geographic Government Life sciences Media Publications Social web 2014
  • 4. DBGroup@UNIMO 5 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 5 The Open Access trends encourage the publication of Open Data in form of Linked Data But Discovering and consuming LOD sources is a complex task for both skilled and unskilled user
  • 5. DBGroup@UNIMO 6 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 6 • There does not exist any standard for documenting a dataset • A great number of datasets is published without a real documentation that could help on revealing their structure. To understand if a dataset really contains interesting information a user have to manually explore it using SPARQL queries. Unskilled user A user with no SPARQL knowledge cannot become a consumer of Linked Data Skilled user The task of exploring a dataset can be time consuming without having any knowledge of its structure
  • 6. DBGroup@UNIMO 7 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 7 A tool for that promotes the understanding, navigation and querying of LOD sources Requirements • portable to the LOD Cloud • provide a synthetic representation of the structure of the dataset • provide visual query building functionalities hiding the complexity of Semantic Web technologies
  • 7. DBGroup@UNIMO 8 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 8 Two main modules • Extraction & Summarization – Index Extraction (IE) – Post Processing (PP) LOD Cloud SPARQL Queries LODeX Post- processing Statistical Indexes LODeX Indexes Extraction Endpoint URLs Schema Summary NoSQL SPARQL Queries Schema Summary Query Orchestrator Schema Summary Visualizzation Basic QueryResults • Visualization & Querying – Schema Summary Visualization – Query Orchestrator
  • 8. DBGroup@UNIMO 9 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 9 Index Extraction [1] The IE process is able to generate the SPARQL queries used to extract the different indexes. • Pattern Strategy technique – It is a technique able to produce an higher number of less complex SPARQL query Post Processing An algorithm combines the information contained in the Statistical Indexes to produce and store the Schema Summary
  • 9. DBGroup@UNIMO 10 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 10 The Schema Summary is a pseudograph composed by: • C - Classes (nodes) • P - Properties (edges) And additional elements and function: • A - Attributes associated to each class – Each attribute represent the existence of a Datatype property from the instances of the class • 𝒍 - labels • l – labeling function • count - count function The Schema Summary is inferred by the distribution of the instances of a dataset
  • 10. DBGroup@UNIMO 11 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 11 ex:Sector foaf:Organization owl:Class ex:sector “sector” rdf:type rdf:type rdf:Propertyrdf:type owl:ObjectProperty rdf:type sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge Intensional Knowledge ex:activity “Village electrification in the Pacific” “+41331231” rdfs:label rdfs:label rdfs:domain rdf:type ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName The information contained in the Intensional knowledge can be incomplete or absent
  • 11. DBGroup@UNIMO 12 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 12 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class.
  • 12. DBGroup@UNIMO 13 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 13 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class. ex:Sector foaf:Organization sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge “Village electrification in the Pacific” “+41331231” ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName
  • 13. DBGroup@UNIMO 14 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 14 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class. ex:Sector foaf:Organization sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge “Village electrification in the Pacific” “+41331231” ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName
  • 14. DBGroup@UNIMO 15 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 15 These indexes belong to extensional group of the Statistical Indexes [2]: • SC (Subject Class) contains the pairs (p,c) where p is an object property and c is its domain class. • SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype property and c is its domain class. • OC (Object Class) contains the pairs (p,c) where p is an object property and c is its range class. ex:Sector foaf:Organization sector1 organization1ex:sector dc:title “Energy” organization2 Extensional Classes Extensional Knowledge “Village electrification in the Pacific” “+41331231” ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” “Rossi” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName
  • 15. DBGroup@UNIMO 16 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 16 We use an algorithm for combining these indexes and produce a Schema Summary Name Values SC (foaf:Organization,ex:ceo,1), (foaf:Organization,ex:sector,2) SCl (foaf:Person,foaf:firstName,1), (foaf:Person,foaf:lastName,1), (foaf:Organization,ex:dbpedia:fax,1), (ex:Sector,dc:title,1), (foaf:Organization,ex:activity,1), (foaf:Organization,dbpedia:fax,1) OC (ex:Sector,ex:sector,1) (ex:Person,ex:ceo,1)
  • 16. DBGroup@UNIMO 17 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 17 foaf:Organizzation 2 ex:Sector 1 ex:sector 2foaf:Person 1 ex:ceo 1 dc:title 1foaf:firstName 1 foaf:lastName 1 ex:activity 1 dbpedia:fax 1 We use an algorithm for combining these indexes and produce a Schema Summary Name Values SC (foaf:Organization,ex:ceo,1), (foaf:Organization,ex:sector,2) SCl (foaf:Person,foaf:firstName,1), (foaf:Person,foaf:lastName,1), (foaf:Organization,ex:dbpedia:fax,1), (ex:Sector,dc:title,1), (foaf:Organization,ex:activity,1), (foaf:Organization,dbpedia:fax,1) OC (ex:Sector,ex:sector,1) (ex:Person,ex:ceo,1)
  • 17. DBGroup@UNIMO 18 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 18 Schema Summary Visualization Front end of the Web Application composed by three panel: • List of datasets indexed in LODeX • Schema Summary and query building panel • Refinement panel Query Orchestrator • It manages the interaction between the User and the GUI • It contains a SPARQL compiler able to compile the visual query in a SPARQL one
  • 18. DBGroup@UNIMO 19 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 19
  • 19. DBGroup@UNIMO 20 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 20
  • 20. DBGroup@UNIMO 21 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 21 Schema Summary SPARQL compiler SPARQL query Basic Query The Visual Query has a tree structure A SPARQL compiler exploits a recursive algorithm to generate the corresponding SPARQL query Operators supported by the compiler: • AND • Optional • Filter The query is sent to the SPARQL endpoint and the results can be visualized in a tabular view • ORDER BY • LIMIT • OFFSET
  • 21. DBGroup@UNIMO 22 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 22 We performed 3 different kinds of evaluation to inspect: • Portability of LODeX to SPARQL endpoints • SPARQL expressiveness • Usability of LODeX – to verify if the graph visualization of the SS is clear in representing the structure of a dataset – to prove if the visual query panel is a powerful and adequate way for generating SPARQL queries
  • 22. DBGroup@UNIMO 23 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 23 We evaluate the complexity of the graph visualization with a group of group of 5 students. • Task: find a node in graphs of increasing size The test set is composed by 185 datasets taken from Datahub Result portability test Number of datasets % Huge Schema Summary (more than 80 nodes) 40 21% Offline endpoints 7 4% Not standard response 28 15% Pass the test 110 60%
  • 23. DBGroup@UNIMO 24 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 24 We analyzed what kind of SPARQL query LODeX is able to generate We used as reference the queries contained in the Berlin SPARQL Benchmark [3] • LODeX is able to generate 6 of 10 queries contained in BSBM
  • 24. DBGroup@UNIMO 25 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 25 We analyzed what kind of SPARQL query LODeX is able to generate We used as reference the queries contained in the Berlin SPARQL Benchmark [3] • LODeX is able to generate 6 of 10 queries contained in BSBM • UNION queries • CONSTRUCT queries • ASK queries
  • 25. DBGroup@UNIMO 26 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 26 We analyzed what kind of SPARQL query LODeX is able to generate We used as reference the queries contained in the Berlin SPARQL Benchmark [3] • LODeX is able to generate 6 of 10 queries contained in BSBM • UNION queries • CONSTRUCT queries • ASK queries • All JOIN acyclic queries • All FILTER queries • All ORDER queries
  • 26. DBGroup@UNIMO 27 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 27 We performed an online survey where has been enrolled 27 users The survey is divided in two parts having different goals: • Evaluate the clarity of Schema Summary • Evaluate the functionality of visual query building For each part has been designed some tasks and a SUS [4] questionnaires
  • 27. DBGroup@UNIMO 28 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 28 Tasks: Datasets: • (T1)Indicate the topic of the dataset • (T2)Find out the class with the largest number of instances • (T3)Find out the classes connected to a given class chosen by us • (T4)Find out the most used attribute of a class chosen by us • Bio2RDF - INOH - pathway database of model organisms • Linked Open Aalto Data Service - Open data published by Aalto University Task Number n Correct % T1 54 48 89% T2 54 48 89% T3 27 23 89% T4 27 27 100% Total 162 148 91%
  • 28. DBGroup@UNIMO 30 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 30 Tasks: Dataset: • (Q1)Return all the different category of Nobel prizes • (Q2) Return a table containing the list of winners of a Nobel prizes ordered by the name of the winner; the table has to contain the date of birth of the winner. • (Q3) Find the award files related to the award of Peter W. Higgs • (Q4) Find the organizations that won a Nobel prize after the 1999 Nobel Prizes - Linked Open Data about every Nobel Prize Task Number n Correct % Q1 27 27 100% Q2 27 26 96% Q3 27 22 81% Q4 27 23 85% Total 108 98 90%
  • 29. DBGroup@UNIMO 31 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 31 We obtained a median SUS score of 85.5 • No remarkable differences between skilled and unskilled user • This score classifies the usability of LODeX as “Excellent” [5] Feedback Unskilled users write their SPARQL query for the first time “LODeX is cognitively less demanding that write SPARQL query” Browser rendering difference Starting a query can be difference and keyword search techniques could be helpful
  • 30. DBGroup@UNIMO 32 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 32 Conclusion • LODeX is portable with the 60% of the datasets tested – 19% a failure induced by endpoint issues • Both skilled and unskilled users appreciated LODeX Future works • Modify the interface of LODeX according to the results of the online survey • Define clustering and new techniques of browsing to reduce the complexity of the Summary for huge dataset • Extend the group of operators supported by the SPARQL compiler
  • 31. DBGroup@UNIMO 33 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 33 • [1] F. Benedetti, S. Bergamaschi, and L. Po, A visual summary for linked open data sources. 2014, International Semantic Web Conference (Posters & Demos). • [2] F. Benedetti, S. Bergamaschi, and L. Po. Online index extraction from linked open data sources. Linked Data for Information Extraction (LD4IE) Workshop held at International Semantic Web Conference, 2014. • [3] C. Bizer and A. Schultz. Benchmarking the performance of storage systems that expose sparql endpoints. • [4] J. Brooke. Sus-a quick and dirty usability scale. Usability evaluation in industry, 189(194):4–7, 1996. • [5] A. Bangor, P. Kortum, and J. Miller. Determining what individual sus scores mean: Adding an adjective rating scale.
  • 32. DBGroup@UNIMO 34 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia 34
  • 33. DBGroup@UNIMO 35 Visual Querying LOD sources with LODeX K-Cap 2015 - The 8th International Conference on Knowledge Capture October 7-10, 2015, Palisades, NY, USA Fabio Benedetti Dip. Ing. “Enzo Ferrari” – University of Modena e Reggio Emilia Thanks for your attention! Try LODeX at: http://dbgroup.unimo.it/lodex2