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MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates
- 1. MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates Kemele M. Endris, Mikhail Galkin, I. Lytra, M. Mami, M.E. Vidal, Sören Auer DEXA 2017 - August 28-31, 2017 Lyon, France
- 2. 2 Geonames Motivating Example NYTimes SWDF Federated Query Engine
- 3. 3 SPARQL Query #triples of Triple patterns in each Data Source TP1 TP2 TP3 32,581 117,915 0 Geonames TP1 TP2 TP3 31,763 0 1,761 NYTimes TP1 TP2 TP3 319 1,112 0 SWDF SELECT DISTINCT ?s WHERE { ?s foaf:page ?page . ?s owl:sameAs ?same. ?s geo:inCountry ?inCountry } TP1 TP2 TP3 Motivating Example
- 4. 4 SELECT DISTINCT ?s WHERE { ?s foaf:page ?page . ?s owl:sameAs ?same. ?s geo:inCountry ?inCountry } TP1 Data Sources Selected by Federated Engines ANAPSID SSGS ANAPSID SSGM TP2 TP3 TP1 TP2 TP3 TP1 TP2 TP3 @SWDF, Geonames @SWDF, Geonames, NYTimes @NYTimes @NYTimes @NYTimes @SWDF @SWDF, Geonames Motivating Example
- 5. 5 FedX ANAPSID SSGS ANAPSID SSGM 32,900 371 32,900 150,790 1,249 1,761 1,761 239.4 sec 0.338 sec 88.9 sec 20 0 19 Execution time: #Triples: SELECT DISTINCT ?s WHERE { ?s foaf:page ?page . ?s owl:sameAs ?same. ?s geo:inCountry ?inCountry } TP1 TP2 TP3 #Triples Found & Execution Time Complete! Motivating Example TP1 TP2 TP2 TP3TP3 TP1
- 6. 6 FedX ANAPSID SSGS ANAPSID SSGM SWDF + Geonames SWDF SWDF + Geonames SWDF + Geonames + NYTimes SWDF NYTimes NYTimes NYTimes NYTimes TP1 TP2 TP3 Data Sources Selected by Federated Engines 239.4 sec 0.338 sec 88.9 sec 20 0 19 Execution time: #Triples: ● The best decomposition: TP1 -----> Geonames TP2 -----> NYTimes & Geonames TP3 -----> NYTimes Complete! TP1 TP2 TP3 Motivating Example
- 7. 7 Source Selection & Decomposition Query Planning Query Execution Query Result SPARQL Query Query Federation Basic Components Source Descriptions
- 8. 8 ● MULDER relies on RDF molecule templates for source selection, and query decomposition and optimization MULDER: Architecture
- 9. 9 • RDF Molecule Templates (RDF-MTs) • describe the set of properties associated with same type of RDF Molecules • RDF Molecule - a set of triples that share same subject MULDER: Source Description Model dbo:Fictional_ Character dbo:occupation dbo:series dbo:portrayer dbo:City owl:sameAs dbo:birthplace geo:Feature geo:population dbo:Person dbo:occupation dbo:series dbo:portrayer owl:sameAs
- 10. 10 • RDF-MTs define a community of RDF molecules that share same characteristics, e.g., having same rdf:type, or wikidata:P31 (instance of) • RDF-MTs are defined in terms of: • Web service API interfaces • Type of Molecule • Set of predicates • Intra and inter links between RDF-MTs RDF Molecule Templates
- 12. 12 RDF Molecule Templates (RDF-MTs) (1) dbo:Fictional _Character dbo:occupation dbo:series dbo:portrayer dbo:Person dbo:occupation dbo:series dbo:portrayer dbr:Fox_ Mulder dbr:David_ Duchovny dbr:FBI dbr:X_Files dbo:Fictional_ Character dbo:Person rdf:type rdf:type dbo:occupation dbo:series dbo:portrayer DBpedia
- 19. 19 RDF-MTs: Intra and Inter Links owl:sameAs geo:Feature dbr:Fox_ Mulder dbr:David_ Duchovny dbr:FBI dbr:X_Files dbo:Fictional_ Character dbo:Person rdf:type rdf:type dbo:occupation dbo:series dbo:portrayer dbr:New_York _Citydbo:City dbo:birthplace rdf:type rdf:type DBpedia Geonames 8175133 geo:population rdf:type geo:5128581 owl:sameAs owl:sameAs mdb:141 dbo:City geo:Feature geo:population dbo:Fictional _Character dbo:occupation dbo:series dbo:Person dbo:occupation dbo:series dbo:portrayer owl:sameAs dbo:portrayer dbo:birthplace
- 20. 20 • MULDER creates a query decomposition with service graph patterns (SGPs) of star-shaped subqueries built according to RDF-MTs • Star-shaped subqueries (SSQs) are a set of triple patterns that share the same subject (variable or constant) • minimize execution time and maximize answer completeness by selecting only relevant sources MULDER: Decomposition & Source Selection
- 21. 21 SELECT DISTINCT ?rper ?city ?ppl WHERE { ?fc dbo:series dbr:X_Files . ?fc dbo:occupation ?occup . ?fc dbo:portrayer ?rper . ?rper owl:sameAs ?same . ?rper dbo:birthplace ?city . ?city owl:sameAs ?feat . ?feat geo:population ?ppl . } Query Decomposition
- 22. 22 ?rper ?fc ?occup dbr:X_Files SSQ1 SELECT DISTINCT ?rper ?city ?ppl WHERE { ?fc dbo:series dbr:X_Files . ?fc dbo:occupation ?occup . ?fc dbo:portrayer ?rper . ?rper owl:sameAs ?same . ?rper dbo:birthplace ?city . ?city owl:sameAs ?feat . ?feat geo:population ?ppl . } t1 t2 t3 t4 t5 t6 t7 SSQ1 SSQ2 SSQ3 SSQ4 Star-shaped Subqueries (SSQs)
- 23. 23 t1 t2 t3 t4 t5 t6 t7 dbo:portrayer dbo:birthplace owl:sameAs SELECT DISTINCT ?rper ?city ?ppl WHERE { ?fc dbo:series dbr:X_Files . ?fc dbo:occupation ?occup . ?fc dbo:portrayer ?rper . ?rper owl:sameAs ?same . ?rper dbo:birthplace ?city . ?city owl:sameAs ?feat . ?feat geo:population ?ppl . } t1 t2 t3 t4 t5 t6 t7 SSQ1 SSQ2 SSQ3 SSQ4 Joinable SSQs
- 24. 24 Matching SSQs Vs RDF-MTs dbo:Fictional _Character dbo:occupation dbo:series dbo:portrayer dbo:City owl:sameAs dbo:birthplace geo:Feature geo:population dbo:Person dbo:occupation dbo:seriesdbo:portrayer owl:sameAs t1 t2 t3 t4 t5 t6 t7 dbo:portrayer dbo:birthplace owl:sameAs t1 t2 t3 dbo:portrayer t6 owl:sameAs ? dbo:Person dbo:Person dbo:Person dbo:Fictional_ Character dbo:City geo:Feature
- 25. 25 Matching SSQs Vs RDF-MTs dbo:Fictional _Character dbo:occupation dbo:series dbo:portrayer dbo:City owl:sameAs dbo:birthplace geo:Feature geo:population dbo:Person dbo:occupation dbo:seriesdbo:portrayer owl:sameAs t1 t2 t3 t4 t5 t6 t7 dbo:portrayer dbo:birthplace owl:sameAs t1 t2 t3 dbo:portrayer t6 owl:sameAs ? dbo:Person dbo:Person dbo:Person dbo:Fictional_ Character dbo:City geo:Feature
- 26. 26 • Sources associated to SSQs determined from RDF-MTs metadata • An SSQ that matches more than one RDF-MT in the same dataset will be decomposed to a single service endpoint • An SSQ that have matching RDF-MTs from more than one dataset will be decomposed to each service endpoint of datasets Source Selection
- 27. 27 t1 t2 t3 t4 t5 t6 t7 dbo:portrayer dbo:birthplace owl:sameAs dbo:portrayer Person@DBpedia t1 t2 t3 Person@DBpedia Fictional_Character@DBpedia Feature@GeonamesCity@DBpedia Source Selection
- 29. 29 • MULDER implements a greedy heuristic based approach to generate a bushy plan • leaves correspond to SSQs • number of joins between SSQs is maximized while the plan height is minimized Query Planning t1 t2 t3 t4 t5 t6 t7 DBpedia DBpedia GeonamesDBpedia JOIN JOIN JOIN
- 30. 30 • Research Questions: RQ1) Do different source descriptions have impact on query processing in terms of efficiency and effectiveness? RQ2) Are RDF-MT based query processing techniques able to enhance query execution time and completeness? Experimental Study
- 31. 31 • Metrics • Execution time: elapsed time between the submission of a query to an engine and the delivery of the answers (timeout: 300 sec) • Cardinality: number of answers returned • Completeness: query result percentage w.r.t answers from union of all datasets Experimental Setup
- 32. 32 ➢ Goal: assess query performance of MULDER utilizing different source descriptions: RDF-Molecule, METIS, and SemEP based source descriptions • METIS and SemEP are community detection algorithms used for graph partitioning • METIS and SemEP based Molecule templates are composed of predicates with similar co-occurrence values • Each predicate is assigned to only one community Experiment I
- 33. 33 • BSBM1 : Berlin SPARQL Benchmark • Builds around an e-commerce use case where a set of products is offered by different vendors and consumers have posted reviews about products. • supports the creation of arbitrarily large datasets • Eight RDF classes • Product, ProductType, ProductFeature, Vendor, Person, Review, Publisher, and Offer • Data Generated: • 200M triples • Queries: • 12 queries, with 20 query mixes Experiment I: Benchmark 1. http://wifo5-03.informatik.uni-mannheim.de/bizer/berlinsparqlbenchmark/spec/
- 34. 34 Results: Exp I Performance of MULDER source descriptions ★ RDF-MT based source descriptions allow MULDER to identify query decompositions and plans that speed up query processing, while answer completeness is not affected (RQ1) ○ RDF-MTs help MULDER reduce intermediate results by selecting only relevant sources
- 35. 35 ➢ Goal: Compares MULDER with state of the art federated query engines: FedX and ANAPSID • FedX: • Sends ASK queries to discover the structure of data sources at query time • implements blocking join operators; i.e., results delivered after all data received from sources • ANAPSID: • Sources described in terms of set of RDF properties in each data source • source descriptions computed beforehand • implements non-blocking operator; i.e., results delivered as soon as they arrive from sources Experiment II
- 36. 36 • Setup I: • Data: BSBM 200M triples • Query: 12 queries, with 20 query mixes • Setup II: • Data: FedBench - 10 datasets, in two collections: • Cross-domain: LinkedMDB, DBPedia, GeoNames, NYTimes, SWDF, Jamendo • Life Science: Drugbank, DBpedia, KEGG, CheBI • Query: 35 queries • 25 FedBench queries, includes Cross Domain (CD), Linked Data (LD) and Life Science (LS) queries • 10 complex (C) queries over FedBench datasets (M. Vidal, et.al) Experiment II: Benchmarks
- 37. 37 FedBench: Connection between RDF-MTs
- 38. 38 Results: Exp II-I ● Performance of MULDER compared to other Federated Engines on synthetic dataset (BSBM) ● ANAPSID returns query answers fast but at the cost of completeness, ● FedX is slower than MULDER and ANAPSID ★ MULDER better identify decomposition and plan that minimizes execution time and answer completeness by utilizing RDF-MTs, compared to FedX and ANAPSID! (RQ2)
- 39. 39 Results: Exp II-II ● Performance of MULDER compared to other Federated Engines on FedBench dataset ○ Answer completeness and execution time ● Quadrants: ○ I and III: indicates bad performance and incomplete results ○ II: (almost) complete but slower execution time ○ IV: indicates best execution time and (almost) complete results ★ MULDER - RDF-MTs performs better in terms of execution time and answer completeness compared to FedX and ANAPSID! (RQ2)
- 40. 40 • MULDER is a query engine for federated access to SPARQL endpoints: • uses RDF-MTs to describe data source interfaces • RDF-MTs enable MULDER decomposition and planning methods to identify efficient and effective query plans compared to METIS and SemeEP based source descriptions (RQ1) • MULDER significantly reduces query execution time and increases answer completeness compared to FedX and ANAPSID, by selecting relevant sources and creating best execution plan (RQ2) Conclusion
- 41. 41 • Integrate additional web access interfaces: such as TPFs and RESTful APIs • Empower RDF-MTs with additional information such as: link selectivity, statistics, etc Future Work
- 42. 42 Thank you!! Follow me @KemeleM endris@cs.uni-bonn.de kemele.endris@gmail.com University of Bonn, Fraunhofer IAIS Germany Full experimental data: https://github.com/EIS-Bonn/MULDER
- 43. Thank you for your attention! wdaqua.eu wdaqua.eu 43