RDF data clustering

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RDF data clustering

  1. 1. Towards a unied framework for distributed data management across the Semantic Web Silvia Giannini (Supervisor: Prof. Eugenio Di Sciascio) Dipartimento di Ingegneria Elettrica e dell'Informazione (DEI), Politecnico di Bari, Bari, Italy s.giannini@deemail.poliba.it 8th ICCL Summer School Workshop (ICCL 2013) Semantic Web - Ontology Languages and Their Use Dresden, Germany | 26 August, 2013
  2. 2. The scenario RDF clustering Proposal Preliminary Results Conclusions Outline 1 The scenario 2 RDF clustering Motivations State of Art 3 Proposal 4 Preliminary Results 5 Conclusions Silvia Giannini RDF data clustering
  3. 3. The scenario RDF clustering Proposal Preliminary Results Conclusions Outline 1 The scenario 2 RDF clustering 3 Proposal 4 Preliminary Results 5 Conclusions Silvia Giannini RDF data clustering
  4. 4. The scenario RDF clustering Proposal Preliminary Results Conclusions The Linking Open Data (LOD) project A global Uniform Resource Identier for each entity on the web (URIs) A standardized access mechanism (HTTP URIs) A machine-readable, open and standardized data format (RDF) A mechanism for linking dierent data sources (RDF-links) Relationship Links Identity Links Vocabulary Links Silvia Giannini RDF data clustering
  5. 5. The scenario RDF clustering Proposal Preliminary Results Conclusions The Linking Open Data (LOD) project As of September 2011 Music Brainz (zitgist) P20 Turismo de Zaragoza yovisto Yahoo! Geo Planet YAGO World Fact- book El Viajero Tourism WordNet (W3C) WordNet (VUA) VIVO UF VIVO Indiana VIVO Cornell VIAF URI Burner Sussex Reading Lists Plymouth Reading Lists UniRef UniProt UMBEL UK Post- codes legislation data.gov.uk Uberblic UB Mann- heim TWC LOGD Twarql transport data.gov. uk Traffic Scotland theses. fr Thesau- rus W totl.net Tele- graphis TCM Gene DIT Taxon Concept Open Library (Talis) tags2con delicious t4gm info Swedish Open Cultural Heritage Surge Radio Sudoc STW RAMEAU SH statistics data.gov. uk St. Andrews Resource Lists ECS South- ampton EPrints SSW Thesaur us Smart Link Slideshare 2RDF semantic web.org Semantic Tweet Semantic XBRL SW Dog Food Source Code Ecosystem Linked Data US SEC (rdfabout) Sears Scotland Geo- graphy Scotland Pupils Exams Scholaro- meter WordNet (RKB Explorer) Wiki UN/ LOCODE Ulm ECS (RKB Explorer) Roma RISKS RESEX RAE2001 Pisa OS OAI NSF New- castle LAAS KISTI JISC IRIT IEEE IBM Eurécom ERA ePrints dotAC DEPLOY DBLP (RKB Explorer) Crime Reports UK Course- ware CORDIS (RKB Explorer) CiteSeer Budapest ACM riese Revyu research data.gov. ukRen. Energy Genera- tors reference data.gov. uk Recht- spraak. nl RDF ohloh Last.FM (rdfize) RDF Book Mashup Rådata nå! PSH Product Types Ontology Product DB PBAC Poké- pédia patents data.go v.uk Ox Points Ord- nance Survey Openly Local Open Library Open Cyc Open Corpo- rates Open Calais OpenEI Open Election Data Project Open Data Thesau- rus Ontos News Portal OGOLOD Janus AMP Ocean Drilling Codices New York Times NVD ntnusc NTU Resource Lists Norwe- gian MeSH NDL subjects ndlna my Experi- ment Italian Museums medu- cator MARC Codes List Man- chester Reading Lists Lotico Weather Stations London Gazette LOIUS Linked Open Colors lobid Resources lobid Organi- sations LEM Linked MDB LinkedL CCN Linked GeoData LinkedCT Linked User Feedback LOV Linked Open Numbers LODE Eurostat (Ontology Central) Linked EDGAR (Ontology Central) Linked Crunch- base lingvoj Lichfield Spen- ding LIBRIS Lexvo LCSH DBLP (L3S) Linked Sensor Data (Kno.e.sis) Klapp- stuhl- club Good- win Family National Radio- activity JP Jamendo (DBtune) Italian public schools ISTAT Immi- gration iServe IdRef Sudoc NSZL Catalog Hellenic PD Hellenic FBD Piedmont Accomo- dations GovTrack GovWILD Google Art wrapper gnoss GESIS GeoWord Net Geo Species Geo Names Geo Linked Data GEMET GTAA STITCH SIDER Project Guten- berg Medi Care Euro- stat (FUB) EURES Drug Bank Disea- some DBLP (FU Berlin) Daily Med CORDIS (FUB) Freebase flickr wrappr Fishes of Texas Finnish Munici- palities ChEMBL FanHubz Event Media EUTC Produc- tions Eurostat Europeana EUNIS EU Insti- tutions ESD stan- dards EARTh Enipedia Popula- tion (En- AKTing) NHS (En- AKTing) Mortality (En- AKTing) Energy (En- AKTing) Crime (En- AKTing) CO2 Emission (En- AKTing) EEA SISVU educatio n.data.g ov.uk ECS South- ampton ECCO- TCP GND Didactal ia DDC Deutsche Bio- graphie data dcs Music Brainz (DBTune) Magna- tune John Peel (DBTune) Classical (DB Tune) Audio Scrobbler (DBTune) Last.FM artists (DBTune) DB Tropes Portu- guese DBpedia dbpedia lite Greek DBpedia DBpedia data- open- ac-uk SMC Journals Pokedex Airports NASA (Data Incu- bator) Music Brainz (Data Incubator) Moseley Folk Metoffice Weather Forecasts Discogs (Data Incubator) Climbing data.gov.uk intervals Data Gov.ie data bnf.fr Cornetto reegle Chronic- ling America Chem2 Bio2RDF Calames business data.gov. uk Bricklink Brazilian Poli- ticians BNB UniSTS UniPath way UniParc Taxono my UniProt (Bio2RDF) SGD Reactome PubMed Pub Chem PRO- SITE ProDom Pfam PDB OMIM MGI KEGG Reaction KEGG Pathway KEGG Glycan KEGG Enzyme KEGG Drug KEGG Com- pound InterPro Homolo Gene HGNC Gene Ontology GeneID Affy- metrix bible ontology BibBase FTS BBC Wildlife Finder BBC Program mes BBC Music Alpine Ski Austria LOCAH Amster- dam Museum AGROV OC AEMET US Census (rdfabout) Media Geographic Publications Government Cross-domain Life sciences User-generated content Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/ Silvia Giannini RDF data clustering
  6. 6. The scenario RDF clustering Proposal Preliminary Results Conclusions RDF: the big picture DBpedia1 extract dbpedia:Dresden dbpedia-owl:country 328.8 dbpedia-owl:areaTotal dbpedia:Germany Graph-structured knowledge representation (data-model) Resource: concrete or abstract entity of the real world, identied by dereferenceable URI Description: representation of properties or relationships among resources Framework: combination of web based protocols and formal semantics Facts in Triple-form: subject - predicate - object http://dbpedia.org/resource/Dresden http://dbpedia.org/property/country http://dbpedia.org/resource/Germany. 1http://dbpedia.org Silvia Giannini RDF data clustering
  7. 7. The scenario RDF clustering Proposal Preliminary Results Conclusions RDF: the big picture DBpedia extract dbpedia:Dresden dbpedia-owl:country 328.8 dbpedia-owl:areaTotal rdf:type rdf:type rdf:type rdfs:rangerdfs:domain dbpedia-owl:country RDF data model RDF Schema dbpedia:Germany dbpedia-owl:PopulatedPlace dbpedia-owl:Country owl:ObjectProperty RDF Schema: Explicit semantics of content and links Silvia Giannini RDF data clustering
  8. 8. The scenario RDF clustering Proposal Preliminary Results Conclusions Outline 1 The scenario 2 RDF clustering Motivations State of Art 3 Proposal 4 Preliminary Results 5 Conclusions Silvia Giannini RDF data clustering
  9. 9. The scenario RDF clustering Proposal Preliminary Results Conclusions Motivations RDF Data Management Challenges LOD cloud statistic: 31 billions facts, 500 million links, at October 2011 How to eciently: Develop services on the top of the RDF data-model for browsing data; query answering; supporting expressive search (approximate matching); Speed up data access and query response times over distributed machines CLUSTERING Silvia Giannini RDF data clustering
  10. 10. The scenario RDF clustering Proposal Preliminary Results Conclusions Motivations Contributions Clustering semantic web resources (RDF graphs) Discovering homogeneous groups of resources Summarizing the original graph content in a meaningful way Revealing possible hierachies of clusters Identing a concept description or discriminating features for each cluster Silvia Giannini RDF data clustering
  11. 11. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art What is a cluster: data-based approach A set of resources with large intra-cluster similarity and large inter-cluster dissimilarity Data clustering methods pairwise distance metric agglomerative partitional (K-Means) - Number or size of clusters to be set Silvia Giannini RDF data clustering
  12. 12. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art What is a cluster: data-based approach A set of resources with large intra-cluster similarity and large inter-cluster dissimilarity Data clustering methods pairwise distance metric agglomerative partitional (K-Means) - Number or size of clusters to be set Silvia Giannini RDF data clustering
  13. 13. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art What is a cluster: data-based approach A set of resources with large intra-cluster similarity and large inter-cluster dissimilarity Data clustering methods pairwise distance metric agglomerative partitional (K-Means) - Number or size of clusters to be set Silvia Giannini RDF data clustering
  14. 14. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art What is a cluster: data-based approach A set of resources with large intra-cluster similarity and large inter-cluster dissimilarity Data clustering methods pairwise distance metric agglomerative partitional (K-Means) - Number or size of clusters to be set RDF data-model not suited for traditional data-clustering techniques application over real-life RDF datasets! Silvia Giannini RDF data clustering
  15. 15. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art What is a cluster: graph-based approach A set of resources with large intra-cluster similarity and large inter-cluster dissimilarity Graph clustering methods vertex connectivity neighborhood similarity spectral analysis of the adjacency matrix - Number or size of clusters to be set http://sydney.edu.au/engineering/it/~shhong/img/cluster1.png Silvia Giannini RDF data clustering
  16. 16. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art RDF clustering: literature Instance extraction Subgraph relevant for a resource representation (DESCRIBE SPARQL2 -query) 1 Immediate Properties + simple, quick - loss of information 2 Concise Bounded Description (CBD) + better body of knowledge - domain dependent (use of blank nodes) 3 Depth Limited Crawling + stable over input data with well limiting subgraph - nd a tradeo between size and information content (data dependent) G.A. Grimnes, P. Edwards, and A. Preece. Instance based clustering of semantic web resources. The Semantic Web: Research and Applications. Springer Berlin Heidelberg, 2008. 303-317. 2http://www.w3.org/TR/rdf-sparql-query/ Silvia Giannini RDF data clustering
  17. 17. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art RDF clustering: literature Instance extraction Subgraph relevant for a resource representation (DESCRIBE SPARQL2 -query) 1 Immediate Properties + simple, quick - loss of information 2 Concise Bounded Description (CBD) + better body of knowledge - domain dependent (use of blank nodes) 3 Depth Limited Crawling + stable over input data with well limiting subgraph - nd a tradeo between size and information content (data dependent) G.A. Grimnes, P. Edwards, and A. Preece. Instance based clustering of semantic web resources. The Semantic Web: Research and Applications. Springer Berlin Heidelberg, 2008. 303-317. 2http://www.w3.org/TR/rdf-sparql-query/ Silvia Giannini RDF data clustering
  18. 18. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art RDF clustering: literature Instance extraction Subgraph relevant for a resource representation (DESCRIBE SPARQL2 -query) 1 Immediate Properties + simple, quick - loss of information 2 Concise Bounded Description (CBD) + better body of knowledge - domain dependent (use of blank nodes) 3 Depth Limited Crawling + stable over input data with well limiting subgraph - nd a tradeo between size and information content (data dependent) G.A. Grimnes, P. Edwards, and A. Preece. Instance based clustering of semantic web resources. The Semantic Web: Research and Applications. Springer Berlin Heidelberg, 2008. 303-317. 2http://www.w3.org/TR/rdf-sparql-query/ Silvia Giannini RDF data clustering
  19. 19. The scenario RDF clustering Proposal Preliminary Results Conclusions State of Art RDF clustering: literature Instances distance computation Comparing two RDF graphs with the resources as root nodes 1 feature-vector based mappings: (feature → shortest path; value → set of reachable nodes) similarity measure: e.g., Dice coecient 2 graph based conceptual similarity: overlapping of nodes relational similarity: overlapping of edges 3 ontology based3 (well dened ontology and conforming instance data) taxonomy similarity: semantic distance between metadata in a concept hierarchy relation similarity: similarity of the instances related to the two considered resources attribute similarity: similarity of attribute values (numeric, literal, etc.) Determine the appropriate number of clusters 3A. Maedche, and V. Zacharias. Clustering ontology-based metadata in the semantic web. Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2002. 348-360. Silvia Giannini RDF data clustering
  20. 20. The scenario RDF clustering Proposal Preliminary Results Conclusions Outline 1 The scenario 2 RDF clustering 3 Proposal 4 Preliminary Results 5 Conclusions Silvia Giannini RDF data clustering
  21. 21. The scenario RDF clustering Proposal Preliminary Results Conclusions Requirements Ideal clustering of graph-structured data: cohesive intra-cluster structure homogeneous intra-cluster properties Parameter free algorithm: number and size of partitions extracted from data Silvia Giannini RDF data clustering
  22. 22. The scenario RDF clustering Proposal Preliminary Results Conclusions How does community detection algorithms behave over RDF(S) graphs? Community Discovery Algorithms Graph mining techniques for extracting knowledge from large graphs Exploit native graph features (topology ) of the RDF model Why: If two sets of entities are strongly related, they exhibit more connections than other sets of entities Benets: + Automatically discover the number and size of modules + Can handle uncertainty in clustering (overlapping communities) + Faster than data-clustering inspired techniques (no instances extraction) Silvia Giannini RDF data clustering
  23. 23. The scenario RDF clustering Proposal Preliminary Results Conclusions What is a community A subgraph of a network whose nodes are more tightly connected with each other than with nodes outside the subgraph. Similarity : cohesion degree of subsets of vertices - No overlapping capabilities C = {C1, . . . , Cn}, Ci ∩ Cj = ∅ ∀i, j ∈ {1, . . . , n}, i = j In labeled graphs (like RDF graphs), each link models only one specic relation Overlapping Communities Analysis Silvia Giannini RDF data clustering
  24. 24. The scenario RDF clustering Proposal Preliminary Results Conclusions From Node to Link Perspective Community : A set of nodes with more external than internal connections, i.e., a set of closely interrelated links. Benets: + Captures multiple memberships between nodes + Unies hierarchical and overlapping clustering It is always possible to move from a link partition P = {P1, . . . , Pm}, Pi ∩ Pj = ∅ ∀i, j ∈ {1, . . . , m}, i = j to m nodes clusters, with possible overlapping. Silvia Giannini RDF data clustering
  25. 25. The scenario RDF clustering Proposal Preliminary Results Conclusions Datasets SP2 Bench4 : A SPARQL Performance Benchmark data generator for arbitrarily large DBLP-like RDF documents creation mirrors key characteristics and social-world distributions of original DBLP dataset publicy available 4M. Schmidt, et al. SP2Bench: SPARQL performance benchmark. Semantic Web Information Management. Springer Berlin Heidelberg, 2010. 371-393. Silvia Giannini RDF data clustering
  26. 26. The scenario RDF clustering Proposal Preliminary Results Conclusions Node communities SP2 Bench: 720 triples Paul_ErdoesPaul_Erdoes ArticleArticle PersonPerson ArticleArticle Paul_ErdoesPaul_Erdoes PersonPerson V.D. Blondel, et al. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008.10 (2008): P10008. Tool: Gephi (https://gephi.org) Silvia Giannini RDF data clustering
  27. 27. The scenario RDF clustering Proposal Preliminary Results Conclusions Link Communities Given an undirected graph G = (V, E), the set of neighbors of node i is Ni = {j ∈ V|eij ∈ E}. Similarity 5 : S(eik, ejk) = |Ni∩Nj | |Ni∪Nj | Link Dendrogram: hierarchical agglomerative algorithm Optimization of Partition density : cut level optimizes link density inside communities DP = 2 M c mc mc−(nc−1) (nc−2)(nc−1) , 5Y.Y. Ahn, J.P. Bagrow, and S. Lehmann. Link communities reveal multiscale complexity in networks. Nature 466.7307 (2010): 761-764. Silvia Giannini RDF data clustering
  28. 28. The scenario RDF clustering Proposal Preliminary Results Conclusions Outline 1 The scenario 2 RDF clustering 3 Proposal 4 Preliminary Results 5 Conclusions Silvia Giannini RDF data clustering
  29. 29. The scenario RDF clustering Proposal Preliminary Results Conclusions RDF clustering6 Article1 _:x1 dc:creator Adamanta Schlitt foaf:name dc:title richer dwelling scrapped swrc:pages 140 _:x1 _:x2 _:x3 foaf:Person rdf:type rdf:type rdf:type rdf:type rdf:type swrc:journal swrc:journal rdf:type rdf:type swrc:journal dc:creator dc:creator dc:creator SIGNATURE: subject SIGNATURE: (predicate, object) SIGNATURE: {(predicate_1, object_1), ... (predicate_n, object_n)} Different background colours reveal the hierarchy of clusters REPLICATED NODES REVEALING OVERLAPPING CLUSTERS LINKS BELONGING TO OTHER CLUSTERS rdf:type Article20 Article13 Paul_Erdoes swrc:journal swrc:journal Article3 Article2 Article1 Journal1 bench:Article TYPE 1. CLUSTER (a) TYPE 2. CLUSTER (b) TYPE 3. CLUSTER (c) 6S. Giannini, RDF Data Clustering. Springer Berlin Heidelberg, 2013. BIS 2013 Workshop, LNBIP 160: 220231. Silvia Giannini RDF data clustering
  30. 30. The scenario RDF clustering Proposal Preliminary Results Conclusions RDF clustering Cluster of type 1. Instance extraction (xed subject) Cluster of type 2. Aggregation of resources (xed predicate - xed object) Mixed-type clusters Set of clusters of type 1. (or equivalently, of type 2.) Silvia Giannini RDF data clustering
  31. 31. The scenario RDF clustering Proposal Preliminary Results Conclusions RDF clustering Cluster of type 1. Instance extraction (xed subject) ex:Article15 swrc:pages 139 ex:Article15 dc:title equalled bewitchment cheaters ex:Article15 dc:creator ex:node17r3ptqpmx16 ex:Article15 rdfs:seeAlso http://www.skeins.tld/sandwiching/bewitchment.html ex:Article15 foaf:homepage http://www.sandwiching.tld/cheaters/ried.html Cluster of type 2. Aggregation of resources (predicate - object) Mixed-type clusters Set of clusters of type 1. (or equivalently, of type 2.) Silvia Giannini RDF data clustering
  32. 32. The scenario RDF clustering Proposal Preliminary Results Conclusions RDF clustering Cluster of type 1. Instance extraction (xed subject) Cluster of type 2. Aggregation of resources (xed predicate - xed object) ex:Article9 swrc:journal http://localhost/publications/journals/Journal1/1945 ex:Article8 swrc:journal http://localhost/publications/journals/Journal1/1945 ex:Article7 swrc:journal http://localhost/publications/journals/Journal1/1945 ex:Article3 swrc:journal http://localhost/publications/journals/Journal1/1945 ex:Article2 swrc:journal http://localhost/publications/journals/Journal1/1945 ex:Article1 swrc:journal http://localhost/publications/journals/Journal1/1945 ex:Article10 swrc:journal http://localhost/publications/journals/Journal1/1945 Mixed-type clusters Set of clusters of type 1. (or equivalently, of type 2.) Silvia Giannini RDF data clustering
  33. 33. The scenario RDF clustering Proposal Preliminary Results Conclusions RDF clustering Cluster of type 1. Instance extraction (xed subject) Cluster of type 2. Aggregation of resources (xed predicate - xed object) Mixed-type clusters Set of clusters of type 1. (or equivalently, of type 2.) ex:Article8 dc:creator http://localhost/persons/Paul_Erdoes ex:Article8 rdf:type http://localhost/vocabulary/bench/Article ex:Article8 swrc:journal http://localhost/publications/journals/Journal1/1942 ex:Article5 dc:creator http://localhost/persons/Paul_Erdoes ex:Article5 rdf:type http://localhost/vocabulary/bench/Article ex:Article5 swrc:journal http://localhost/publications/journals/Journal1/1942 ex:Article4 dc:creator http://localhost/persons/Paul_Erdoes ex:Article4 rdf:type http://localhost/vocabulary/bench/Article ex:Article4 swrc:journal http://localhost/publications/journals/Journal1/1942 ex:Article3 dc:creator http://localhost/persons/Paul_Erdoes ex:Article3 rdf:type http://localhost/vocabulary/bench/Article ex:Article3 swrc:journal http://localhost/publications/journals/Journal1/1942 ex:Article2 dc:creator http://localhost/persons/Paul_Erdoes ex:Article2 rdf:type http://localhost/vocabulary/bench/Article ex:Article2 swrc:journal http://localhost/publications/journals/Journal1/1942 ex:Article1 dc:creator http://localhost/persons/Paul_Erdoes ex:Article1 rdf:type http://localhost/vocabulary/bench/Article ex:Article1 swrc:journal http://localhost/publications/journals/Journal1/1942 Silvia Giannini RDF data clustering
  34. 34. The scenario RDF clustering Proposal Preliminary Results Conclusions Advantages and Emerging issues Tests over 266, 720, and 5362 triples datasets Number of obtained clusters: 53, 277, 3437 + Good behaviour in presence of blank nodes http://localhost/vocabulary/bench/PhDThesis rdfs:subClassOf foaf:Document http://localhost/vocabulary/bench/Www rdfs:subClassOf foaf:Document http://localhost/vocabulary/bench/Book rdfs:subClassOf foaf:Document _:node17rocfnblx296 rdf:_3 misc:UnknownDocument_c _:node17rocfnblx296 rdf:_2 misc:UnknownDocument_b _:node17rocfnblx296 rdf:_1 misc:UnknownDocument_a misc:UnknownDocument_c rdf:type foaf:Document misc:UnknownDocument_b rdf:type foaf:Document misc:UnknownDocument_a rdf:type foaf:Document http://localhost/vocabulary/bench/MastersThesis rdfs:subClassOf foaf:Document - A post-processing phase is needed (links replication) If Paul Erdoes is a Person included in a type 2. cluster with signature (rdf:type - prex:Person), this property will not appear in the cluster of type 1. describing the resource Paul_Erdoes Silvia Giannini RDF data clustering
  35. 35. The scenario RDF clustering Proposal Preliminary Results Conclusions Outline 1 The scenario 2 RDF clustering 3 Proposal 4 Preliminary Results 5 Conclusions Silvia Giannini RDF data clustering
  36. 36. The scenario RDF clustering Proposal Preliminary Results Conclusions Conclusions and Future Works Community detection algorithms are a promising candidate for: semantic web resources clustering instances extraction from RDF graphs Ongoing and future works: A more comprehensive experimental evaluation on dierent datasets Analysis of cut threshold Better denition of post-processing phase Comparison with existing approaches Combination of (1) graph clustering techniques, and (2) reasoning services 1 Identify communities of closely related resources 2 Extract a semantic description of them Experimentation of property-driven clustering Dynamics and evolution of clusters Silvia Giannini RDF data clustering
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