R for the semantic web, Quesada useR 2009
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This document discusses using R to analyze and work with semantic web data represented as graphs. It introduces key concepts like ontologies, RDF, and linked data. It also describes how graph analysis techniques like centrality measures and PageRank can be applied to semantic web data to identify important nodes. Developing tools for semantic web data in R is challenging because current software uses an object-oriented approach while semantic data is represented as triples in graphs; an R package is needed to directly handle RDF.
![Pagerank
• The pagerank vector is
the stationary
distribution of a markov 1 3
chain in a link matrix
• Some assumptions to 2 4
warrant convergence
• The typical value of d
is .85
norm <- function(x) x/sum(x)
norm(eigen(0.15/nVertices + 0.85 * t(A))$vectors[,1])](https://image.slidesharecdn.com/quesadauser2009-090711061346-phpapp01/85/R-for-the-semantic-web-Quesada-useR-2009-22-320.jpg)
![Problems
• Graphs have advantages over
RDBMS/tables[1]. But we are used to think in
tables
• There is no direct way to handle RDF in R.
worth an R package?](https://image.slidesharecdn.com/quesadauser2009-090711061346-phpapp01/85/R-for-the-semantic-web-Quesada-useR-2009-25-320.jpg)
R for the semantic web, Quesada useR 2009
- 1. From relational databases to linked data:R for the semantic web Jose Quesada, Max Planck Institute, Berlin
- 2. Who this talk targets • You have big data; you use a database • You have an evolving schema definition. Sometimes at runtime • You are interested in alternative ways to present your data • You would thrive by using data out there, if only they were more accessible
- 3. Semantic web
- 6. Credit: Jim Hendler THE TWO TOWERS
- 7. The Semantic web • Ontology as Barad-dur (Sauron’s tower) – Extremely powerful – Patrolled by Orcs • Let one little hobbit in it, and the whole thing could come crashing down – OWL
- 8. The Semantic web • Ontology as Barad-dur (Sauron’s tower) – Extremely powerful Decidable logic basis – Patrolled by Orcs inconsistency • Let one little hobbit in it, and the whole thing could come crashing down – OWL
- 10. The semantic web • The tower of Babel – We will build a tower to reach the sky – We only need a little ontological agreement • Who cares if we all speak different languages? This is RDFS Statistics matter here Web-scale Lots of data; finding anything in the mess can be a win
- 11. Approaches to data representation • Objects • Tables (relational databases) • Non-relational databases • Tables (data.frame) • Graphs
- 12. What one can do with semantic web data, now: People that died in Nazi Germany and if possible, any notable works that they might have created SELECT * WHERE { ?subject dbpprop:deathPlace <http://dbpedia.org/resource/Nazi_Germany> . OPTIONAL { ?subject dbpedia-owl:notableworks ?works } }
- 13. subject works :Anne_Frank :The_Diary_of_a_Young_Girl :Martin_Bormann - :Ir%C3%A8ne_N%C3%A9mirovsky - :Erich_Fellgiebel - :Friedrich_Ferdinand%2C_Duke_of_Schleswig-Holstein - :Friedrich_Olbricht - :Ludwig_Beck - :Erwin_Rommel - :Maurice_Bavaud - :Early_Years_of_Adolf_Hitler - :Emil_Zegad%C5%82owicz - :Friedrich_Fromm - :Helmuth_James_Graf_von_Moltke
- 14. • Scale to the entire web • Use cases: – Real time city – Cancer monographs for • Do reasoning with open WHO word assumption – Gene expression finding • Retrieval in real-time • Go beyond logics
- 15. RDF is a graph • We have lots of interesting statistics that run on graphs • In many Semantic Web (SW) domains a tremendous amount of statements (expressed as triples) might be true but, in a given domain, only a small number of statements is known to be true or can be inferred to be true. It thus makes sense to attempt to estimate the truth values of statements by exploring regularities in the SW data with machine learning
- 16. Scale • You cannot use the entire thing at once: subsetting • Are there patterns in knowledge structures that we can use for subsetting?
- 18. Idea • Graph theory applied to subsetting large graphs • Developing Semantic Web applications requires handling the RDF data model in a programming language • Problem: current software is developed in the object-oriented paradigm, programming in RDF is currently triple-based.
- 19. Data IMDB is a big graph: – 1.4 m movies – 1.7 m actors – 11 M connections • Movies have votes – Bipartite network Packages: igraph: – Nice functions that you cannot find anywhere else – Uses Sparse Matrices – Implemented in C – Some support for bipartite networks Rmysql, Matrix (sparse m)
- 20. Centrality
- 21. Centrality
- 22. Pagerank • The pagerank vector is the stationary distribution of a markov 1 3 chain in a link matrix • Some assumptions to 2 4 warrant convergence • The typical value of d is .85 norm <- function(x) x/sum(x) norm(eigen(0.15/nVertices + 0.85 * t(A))$vectors[,1])
- 24. Top movies by pageRank in the actor->movie network degree pagerank cluster imdbID title rank votes 0.000243688 1298 252192870 0 822609Around the World in Eighty Days (1956) 40031 6134 0.000103540 313 862390464 0 76352Beyond Our Control" (1968)" 0 0 0.000091669 291 0099912811 0 993780Gone to Earth (1950) 7.0 291 0.000089025 285 5923652847 0 915626Deadlands 2: Trapped (2008) 39971 15 0.000083882 424 328163772 0 1282574Stuck on You (2003) 6.0 19709 0.000080824 629 1101098043 0 622100Shortland Street" (1992)" 39850 225
- 25. Problems • Graphs have advantages over RDBMS/tables[1]. But we are used to think in tables • There is no direct way to handle RDF in R. worth an R package?
- 26. Linked data are out there for the grabs We need to start thinking in terms of graphs, and slowly move away from tables Thanks for your attention Jose Quesada, quesada@workingcogs.com, http://josequesada.name Twitter: @Quesada