Web of Data Usage Mining

1. Web of Data Usage Mining Markus Luczak-Roesch @mluczak | http://markus-luczak.de

2. What you should learn: •  describe the architectural differences between content negotiation and Linked Data queries; •  develop applications that use different strategies to consume Linked Data; •  develop usage mining methods that exploit the atomic parts of the SPARQL query language.

3. Linked Data principles 1.  Use URIs as names for “Things” (resources). 2.  Use HTTP URIs to allow the access to resources on the Web. 3.  On resource access, deliver meaningful information conforming to Web standards (RDF, SPARQL). 4.  Set RDF links to resources published by other parties to allow the discovery of more resources. http://dbpedia.org/resource/Berlin http://dbpedia.org/page/Berlin http://dbpedia.org/data/Berlin yago-res:Berlin S owl:sameAs P dbpedia:Berlin O h"p://www.w3.org/DesignIssues/LinkedData.html Content Negotiation

4. Linked Data principles 1.  Use URIs as names for “Things” (resources). 2.  Use HTTP URIs to allow the access to resources on the Web. 3.  On resource access, deliver meaningful information conforming to Web standards (RDF, SPARQL). 4.  Set RDF links to resources published by other parties to allow the discovery of more resources. h"p://www.w3.org/DesignIssues/LinkedData.html http://dbpedia.org/resource/Berlin http://dbpedia.org/page/Berlin http://dbpedia.org/data/Berlin yago-res:Berlin S owl:sameAs P dbpedia:Berlin O Content Negotiation

8. Linked Data exploits RDF h"p://markus-luczak.de#me “Markus Luczak-Roesch“ foaf:name h"p://markus-luczak.de#me h"p://hannes.muehleisen.org#me foaf:knows

9. Linked Data vocabularies •  Vocabulary reuse: –  Geo –  FOAF –  GoodRelations –  SIOC –  DOAP –  … •  Vocabulary development: –  Thing •  Person –  OfﬁceHolder –  … •  … http://dbpedia.org/ontology/Person http://dbpedia.org/ontology/OfﬁceHolder http://xmlns.com/foaf/0.1/knows

10. Linked Data vocabularies •  Mixing: – Geo – FOAF – Dublin Core – DBpedia Ontology –  ... http://xmlns.com/foaf/0.1/Person http://www.w3.org/2003/01/geo/wgs84_pos#lat http://dbpedia.org/ontology/leader http://dbpedia.org/ontology/City

11. Linked Data is self-descriptive Instance level Schema level int:resA ont:ClassA owl:sameAs „ABC“ foaf:name ext:resA int:resB rdf:type owl:equivalentClass rdf:type foaf:name rdf:type rdf:type rdf:type rdfs:subClassOf foaf:Agent rdf:type foaf:Person rdfs:subClassOf owl:sameAs owl:equivalentClass

12. h"p://markus-luczak.de#me “Markus Luczak-Roesch“ rdf:type u_id ﬁrstname surname 45 Markus Luczak-Roesch … … … foaf:name foaf:Person

13. “3.375.222“ dbpedia:Berlin c_id city country inhabitants 67 Berlin Germany 3.375.222 … … … dbp:populaVon

14. h"p://markus-luczak.de#me “Markus Luczak-Roesch“ rdf:type foaf:name dbp:birthPlace foaf:Person “3.375.222“ dbp:populaVon dbpedia:Berlin

15. h"p://markus-luczak.de#me foaf:basedNear dbp:birthPlace h"p://markus-luczak.de/res/Soton dbpedia:CiVes_in_Europe skos:subject dbpedia:Berlin skos:subject dbpedia:Southampton

16. h"p://markus-luczak.de#me foaf:basedNear dbp:birthPlace h"p://markus-luczak.de/res/Soton dbpedia:CiVes_in_Europe skos:subject dbpedia:Berlin skos:subject dbpedia:Southampton rdfs:seeAlso

17. h"p://markus-luczak.de#me foaf:basedNear h"p://markus-luczak.de/res/Soton rdfs:seeAlso rdf:type foaf:Person owl:equivalentClass dbp:Person rdf:type dbpedia:Southampton dbp:birthPlace dbpedia:Benny_Hill

18. Linked Data Infrastructure Image source: Tom Heath and ChrisVan Bizer (2011) Linked Data: Evolving the Web into a Global Data Space (1st ediVon). Synthesis Lectures on the SemanVc Web: Theory and Technology, 1:1, 1-136. Morgan & Claypool.

19. Consuming Linked Data •  stateless •  request-response t Client Server request response TCP life cycle derived from R. Tolksdorf Open connection Close connection

20. Consuming Linked Data GET / HTTP/1.1 User-Agent: Mozilla/5.0 … Firefox/10.0.3 Host: markus-luczak.de:80 Accept: */* HTTP/1.1 200 OK Server: Apache/2.0.49 Content-Language: en Content-Type: text/html Content-length: 2990 <!DOCTYPE html> <html xml:lang="en" … Client Server derived from R. Tolksdorf

21. Server Consuming Linked Data Representation 1 index.html Representation 2 index.rdf Information Resource http://example.com/content/index Client HTTP GET

22. Consuming Linked Data •  Discover URIs – Lookup services •  http://rkbexplorer.com – Web of Data search engines •  http://sindice.com •  http://ws.nju.edu.cn/falcons/objectsearch/index.jsp

23. Consuming Linked Data •  Discover additional data for the resource at hand •  follow links („follow your nose“) –  rdfs:seeAlso –  owl:sameAs •  Co-Reference services –  http://sameas.org •  Web of Data search engines

24. Linked Data Source: http://wifo5-03.informatik.uni-mannheim.de/bizer/pub/LinkedDataTutorial/ The server can trace this usage.

25. Linked Data is queryable ?s “Markus Luczak-Roesch“ foaf:name h"p://markus-luczak.de#me ?o foaf:knows

26. SPARQL-recap •  Basic principle: pattern matching – describe pattern – query RDF triple set („RDF graph“) – matching subset comes into results ?s http://dbpedia.org/resource/Berlin

27. SPARQL-recap ?s dbp:Klaus_Wowereit dbp:Reinhard_Mey dbp:Klaus_Wowereit dbp:Berlin dbp:birthPlace dbp:Reinhard_Mey Berlino dbp:Axel_Springer

28. SPARQL queries on the Web •  RESTful service endpoint GET /sparql?query=PREFIX+rdf… HTTP/1.1 Host: dbpedia.org h"p://www.w3.org/TR/rdf-sparql-XMLres/ h"p://www.w3.org/TR/rdf-sparql-json-res/

29. Querying Linked Data dbp:Klaus_Wowereit dbp:Berlin dbp:birthPlace dbp:Reinhard_Mey http://www.markus-luczak.de/me dbp:birthPlace

30. Querying Linked Data •  distribution of data creates challenges for querying them •  Query approaches –  follow-up queries ß application-dependent, proprietary –  query a central data repository (e.g. LOD cache) ß trivial –  federated queries ß more interesting •  idea: query a mediator that distributes the sub-queries and returns aggregated result (as of SPARQL 1.1) –  link traversal ß very interesting •  idea: follow links in the results retrieved from a source to expand the data dynamically

31. Dataset User Client/ApplicaVon Query Pa"ern Access Resource Centered Access HTTP Query Processing Graph CreaVon and Content NegoVaVon GET /resource/resA GET /sparql?query=SELECT… applicaVon/rdf+xml, … Evaluate and perform query, create result set Process and select result text/xml, … Data Publisher Data Consumer Data Publisher Data Consumer

32. h"p://www.ﬂickr.com/photos/therichbrooks/4040197666/, CC-BY 2.0, h"ps://creaVvecommons.or A game of pairs with SPARQL

33. SPARQL queries are self-descriptive data themselves { ?s1 foaf:name “Markus Luczak-Roesch”. ?s1 rdf:type dbp:Person } TP TP BGP

34. SPARQL queries are self-descriptive data themselves { ?s1 foaf:name “Markus Luczak-Roesch”. ?s1 rdf:type dbp:Person } h"p://markus-luczak.de#me “Markus Luczak-Roesch“ rdf:type foaf:name foaf:Person ✔ ✗ ✗

35. SPARQL queries are self-descriptive data themselves { dbpedia:Benny_Hill dbp:birthPlace ?o1 . ?s dbp:basedNear ?o1 . ?s foaf:name ?o2 } ✔ ✗ ✗ ✗

36. SPARQL queries are self-descriptive data themselves { dbpedia:Benny_Hill dbp:birthPlace ?o1 } ✔

37. SPARQL queries are self-descriptive data themselves { ?s dbp:basedNear ?o1 } ✔

38. SPARQL queries are self-descriptive data themselves { ?s foaf:name ?o2 } ✔

39. all TP all TP in successful BGP all TP in successful queries all TP in failing queries all TP in failing BGP

40. Statistical analysis missing facts inconsistent data •  ns:Band ns:knownFor ?x •  ns:Band ns:naVonality ?y •  ns:Band ns:instrument ?x •  ns:Band ns:genre ?y •  ns:Band ns:associatedBand ?z

41. Statistical analysis (a) SWC (b) DBpedia (c) LGD Abbildung 20: Nutzung der Konzepte der Multi-Ontologien (Kanten sind ausgeblendet) Quelle: eigene Darstellung dieser Datensets besitzt noch ein großes Verbesserungspotential. Beispielsweise sind di Möglichkeiten gegeben, eine höhere Anzahl an speziellen Konzepten zu nutzen. Eben so können theoretisch mehr Konzepte aus anderen Bereichen als Personen, Orte unSource: Masterthesis of Markus Bischoff

42. Estimating the effects of change o be added to the DBpedia 3.4 data set conforming to our approach16 . able 7.14: Recommended predicates to be added to the data set and the estimate ↵ects of change. Primitive to add E↵ects of change Exists in data set dbp:manufacturer 0.004505372 x dbp:ﬁrstFlight 0.004505372 x dbp:introduced 0.004505372 x dbp:nationalOrigin 0.004505372 dbo:thumbnail 0.021986718 x dbo:director 0.025047524 dbp:director 0.02503915 x dbp:abstract 0.025797024 x dbo:starring 0.034066643 dbp:starring 0.034066643 x dbp:stars 0.034066643 x skos:Concept 0.040946128 x skos:broader 0.04116386 x dbp:redirect 0.066441677 x

43. Log ﬁles Selected log ﬁles Preprocessed queries Decomposed queries and transac<on tables Pa=erns Change recommenda<ons [0,1]

44. What’s in your SPARQL shopping bag? { ?s1 foaf:name “Markus Luczak-Roesch”. ?s1 rdf:type dbp:Person } { dbpedia:Benny_Hill dbp:birthPlace ?o1 . ?s dbp:basedNear ?o1 . ?s foaf:name ?o2 } { ?s1 foaf:name “Markus Luczak-Roesch”. ?s1 rdf:type dbp:Person } { dbpedia:Benny_Hill dbp:birthPlace ?o1 . ?s dbp:basedNear ?o1 . ?s foaf:name ?o2 } { ?s1 foaf:name “Markus Luczak-Roesch”. ?s1 rdf:type dbp:Person } { dbpedia:Benny_Hill dbp:birthPlace ?o1 . ?s dbp:basedNear ?o1 . ?s foaf:name ?o2 } T1 T2 T1 … … 30 mins., same IP, same user agent … … …

45. LGD

46. Linked Data Source: http://wifo5-03.informatik.uni-mannheim.de/bizer/pub/LinkedDataTutorial/ The server can trace this usage.

47. SPARQL 7. Evaluation The visualization shows how primitives on the left hand side (LHS) of a rule imply particular ones on the right hand side (RHS) and which likelihood such an association has. In our specific case this allows us to analyze which primitives are queried together frequently in failing queries. We spot two characteristic usage patterns: (1) the properties and classes queried in the context of http://dbpedia.org/ontology/ Aircraft; (2) the properties and classes queried in the context of an object variable. These can be further analyzed by exporting the association rules to GraphML and vi- sualizing the network by use of a network visualization and analysis tool like Gephi15 for example. Figure 7.13 depicts one filtered network representation for our example case. Nodes with a degree lower than 5 are filtered out (k-core network with k = 5) to derive a well-arranged visualization of the most important primitives in failing queries. Nodes represent LHS and RHS of the computed rules. Edges point from the LHS to the RHS of the particular rules. Figure 7.13: Filtered visualization of the association rule network (k-core 5 filter applied to reduce nodes with degree lower than 5). Table 7.14 lists the an exemplary set of primitives which would be recommended 15http://gephi.org/ 177 { ?s1 foaf:name “Markus Luczak-Roesch”. ?s1 rdf:type dbp:Person } h"p://markus-luczak.de#me “Markus Luczak-Roesch“ rdf:type foaf:name foaf:Person ✔ ✗ ✗ query applied to dataset The server can trace detailed usage.

48. Linked Data Fragments Querying Datasets on the Web with High Availability 5 generic requests high client effort high server availability specific requests high server effort low server availability data dump Linked Data document sparql result triple pattern fragments various types of Linked Data Fragments Fig. 1: All http triple interfaces offer Linked Data Fragments of a dataset. They differ in the specificity of the data they contain, and thus the effort needed to create them. 3.2 Formal definitions As a basis for our formalization, we use the following concepts of the rdf data model [16] and the sparql query language [12]. We write U, B, L, and V to denote the sets of all uris, blank nodes, literals, and variables, respectively. Then, T = (U [ B) ⇥ U ⇥ (U [ B [ L) is the (infinite) set of all rdf triples. Any tuple tp 2 (U [ V) ⇥ (U [ V) ⇥ (U [ L [ V) is a triple pattern. Any finite set of such triple patterns is a basic graph pattern (bgp). Any more complex sparql graph pattern, typically denoted by P, combines triple patterns (or bgps) using specific operators [12,20]. The standard (set-based) query semantics for sparql defines the query result of such a graph pattern P over a set of rdf triples G ✓ T as a set that we denote by [[P]]G and that consists of partial mappings µ : V ! (U [ B [ L), which are called solution mappings. An rdf triple t is a matching triple for a triple pattern tp if there exists a solution mapping µ such that t = µ[tp], where µ[tp] denotes the triple (pattern) that we obtain by replacing the variables in tp according to µ. For the sake of a more straightforward formalization, in this paper, we as- sume without loss of generality that every dataset G published via some kind of fragments on the Web is a finite set of blank-node-free rdf triples; i.e., G ✓ T ⇤ where T ⇤ = U ⇥ U ⇥ (U [ L). Each fragment of such a dataset contains triples that somehow belong together; they have been selected based on some condition, which we abstract through the notion of a selector: T xxx.xxx.xxx.xxx - - [17/Oct/2014:07:43:02 +0000]   "GET /2014/en?subject=&predicate=&object=dbpedia%3AAustin HTTP/1.1" 200 1309 "http://fragments.dbpedia.org/2014/en" … fetches the first page of the corresponding ldf. This page contains the cnt meta- data, which tells us how many matches the dataset has for each triple pattern. The pattern is then decomposed by evaluating it using a) a triple pattern iterator for the triple pattern with the smallest number of matches, and b) a new bgp iterator for the remainder of the pattern. This results in a dynamic pipeline for each of the mappings of its predecessor, as visualized in Fig. 2. Each pipeline is optimized locally for a specific mapping, reducing the number of requests. To evaluate a sparql query over a triple pattern fragment collection, we pro- ceed as follows. For each bgp of the query, a bgp iterator is created. Dedicated iterators are necessary for other sparql constructs such as UNION and OPTIONAL, but their implementation need not be ldf-specific; they can reuse the triple pattern fragment bgp iterators. The predecessor of the first iterator is a start iterator. We continuously pull solution mappings from the last iterator in the pipeline and output them as solutions of the query, until the last iterator re- sponds with nil. This pull-based process is able to deliver results incrementally. ... B00 = { Drago_Ibler a Architect. } Alen_Peternac Drago_Ibler Juraj_Neidhardt ... ?person birthPlace Zagreb. B0 = { ?person a Architect. ?person birthPlace Zagreb. } Zagreb Budapest Rome ... ?city subject Capitals_in_Europe. B = { ?person a Architect. ?person birthPlace ?city. ?city subject Capitals_in_Europe. } Fig. 2: A bgp iterator decomposes a bgp B = {tp1, . . . , tpn} into a triple pattern iterator for an optimal tpi and, for each resulting solution mapping µ of tpi, creates a bgp iterator for the remaining pattern B0 = {tp | tp = µ[tpj] ^ tpj 2 B} {µ[tpi]}. Pre-print of a paper accepted to the International Semantic Web Conference 2014 (ISWC 2014). The final publication is available at link.springer.com. Querying Datasets on the Web with High Av 4.2 Dynamic iterator pipelines A common approach to implement query execution in database sy iterators that are typically arranged in a tree or a pipeline, based results are computed recursively [10]. Such a pipelined approac studied for Linked Data query processing [13,15]. In order to en results and allow the straightforward addition of sparql oper ment a triple pattern fragments client using iterators. The previous algorithm, however, cannot be implemented by pipeline. For instance, consider a query for architects born in Eu SELECT ?person ?city WHERE { ?person a dbpedia-owl:Architect. # tp1 ?person dbpprop:birthPlace ?city. # tp2 ?city dc:subject dbpedia:Capitals_in_Europe. # tp3 } LIMIT 100 Suppose the pipeline begins by finding ?city mappings for tp to choose whether it will next consider tp1 or tp2. The optimal differs depending on the value of ?city: – For dbpedia:Paris, there are ±1,900 matches for tp2, and for tp1, so there will be less http requests if we continue w – For dbpedia:Vilnius, there are 164 matches for tp2, and ±1 tp1, so there will be less http requests if we continue with With a static pipeline, we would have to choose the pipeline stru and subsequently reuse it. In order to generate an optimized pipeline for each (sub-)qu a divide-and-conquer strategy in which a query is decomposed d

49. Wikidata •  API access to •  items •  edit history •  items’ discussions •  items’ access statistics •  and more •  Linked Data interface •  MediaWiki API •  Wikidata Query •  SPARQL •  Linked Data Fragments Access to more than “just” usage.

50. Thank you very much! @mluczak | http://markus-luczak.de h"p://www.ﬂickr.com/photos/therichbrooks/4040197666/, CC-BY 2.0, h"ps://creaVvecommons.or

51. References •  Luczak-Rösch, M., & Bischoff, M. (2011). Statistical analysis of web of data usage. In Joint Workshop on Knowledge Evolution and Ontology Dynamics (EvoDyn2011), CEUR WS. •  Luczak-Rösch, M. (2014). Usage-dependent maintenance of structured Web data sets (Doctoral dissertation, Freie Universität Berlin, Germany), http://edocs.fu-berlin.de/diss/receive/FUDISS_thesis_000000096138. •  Elbedweihy, K., Mazumdar, S., Cano, A. E., Wrigley, S. N., & Ciravegna, F. (2011). Identifying Information Needs by Modelling Collective Query Patterns. COLD, 782. •  Elbedweihy, K., Wrigley, S. N., & Ciravegna, F. (2012). Improving Semantic Search Using Query Log Analysis. Interacting with Linked Data (ILD 2012), 61. •  Raghuveer, A. (2012). Characterizing machine agent behavior through SPARQL query mining. In Proceedings of the International Workshop on Usage Analysis and the Web of Data, Lyon, France. •  Arias, M., Fernández, J. D., Martínez-Prieto, M. A., & de la Fuente, P. (2011). An empirical study of real-world SPARQL queries. arXiv preprint arXiv:1103.5043. •  Hartig, O., Bizer, C., & Freytag, J. C. (2009). Executing SPARQL queries over the web of linked data (pp. 293-309). Springer Berlin Heidelberg. •  Verborgh, R., Hartig, O., De Meester, B., Haesendonck, G., De Vocht, L., Vander Sande, M., ... & Van de Walle, R. (2014). Querying datasets on the web with high availability. In The Semantic Web–ISWC 2014 (pp. 180-196). Springer International Publishing. •  Verborgh, R., Vander Sande, M., Colpaert, P., Coppens, S., Mannens, E., & Van de Walle, R. (2014, April). Web-Scale Querying through Linked Data Fragments. In LDOW.

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