The document discusses using machine learning techniques on semantic web data represented in RDF triples. It describes representing the RDF triples as a vector space to find relationships between subjects. Dimensionality reduction techniques like latent semantic analysis can be applied to cluster similar subjects based on predicate relationships. Both supervised and unsupervised machine learning approaches are applicable, such as classification to map between ontologies or clustering for ontology learning.

1. Machine Learning
Techniques for the
Semantic Web
Paul Dix
http://pauldix.net
paul@pauldix.net

5. Machine Learning

6. Semantic Web

9. What is Semantic Web?

12. Ontology

13. RDF

15. Machine Learning is
about Data

16. actually...

17. Making Predictions
Based on Data

21. FOAF
Simple Example

22. Marco Neumann
<http://www.marconeumann.org/foaf.rdf>
<http://xmlns.com/foaf/0.1/knows>
<http://community.linkeddata.org/dataspace/person/
kidehen2/about.rdf> .
<http://www.marconeumann.org/foaf.rdf>
<http://xmlns.com/foaf/0.1/knows>
<http://www.johnbreslin.com/foaf/foaf.rdf> .
<http://www.marconeumann.org/foaf.rdf>
<http://xmlns.com/foaf/0.1/knows>
<http://swordfish.rdfweb.org/people/libby/rdfweb/
webwho.xrdf> .
<http://www.marconeumann.org/foaf.rdf>
<http://xmlns.com/foaf/0.1/knows>
<http://danbri.org/foaf.rdf> .

24. Marco only knows 4
people?

25. Two Degrees Out
4 - <http://www.w3.org/People/Connolly/home-smart.rdf>
4 - <http://jibbering.com/foaf.rdf>
2 - <http://sw.deri.org/~haller/foaf.rdf>
2 - <http://sw.deri.org/~knud/knudfoaf.rdf>
2 - <http://www-cdr.stanford.edu/~petrie/foaf.rdf>

26. Three Degrees
9 - <http://sw.deri.org/~knud/knudfoaf.rdf>
8 - <http://www.w3.org/People/Connolly/home-smart.rdf>
7 - <http://jibbering.com/foaf.rdf>
6 - <http://www.aaronsw.com/about.xrdf>
5 - <http://sw.deri.org/~aharth/foaf.rdf>

27. but that’s not really
machine learning

28. Short

30. Machine Learning is
• How you formulate the problem
• How you represent the data

31. • Graphical Models
• Vector Space Models

32. Back to FOAF
Convert RDF triples to vector space

33. We Want to Find
Groups of People

34. To make predictions on
their interests...

35. (subject) (predicate) (object)
Paul knows Jeff
Paul knows Joe
Paul knows Marco
Jeff knows Joe

36. Vector Space
Representation
Jeff Joe Marco Paul
Jeff 1 1
Joe 1 1
Marco 1
Paul 1 1 1

37. Latent Factors Analysis
• Used in Latent Semantic Indexing (LSI)
• Good for finding synonyms
• Good for finding “genres”

38. Latent Factors Methods
• Principle Component Analysis (PCA)
• Singular Value Decomposition (SVD)
• Restricted Boltzmann Machines (RBM)

39. Considerations for
Semantic Web Data
• Large Data Sets
• Sparse Data Sets

40. Netflix Prize Research
• Movie Review Data set has similar
problems
• Generalized Hebbian Algorithm for
Dimensionality Reduction in NLP (Gorrell
’06.)

41. Reduce Dimensions
• 1m x 1m matrix with 1m people
• Reduce to 1m x 100

42. 100 Latent Factors
Represent different groups of people based on who
they know.

43. What the Data Might
Look Like
Factor 1 Factor 2
Paul 0.678 0.311
Joe 0.455 0.432
Jeff 0.476 0.398
Marco 0.203 0.789

44. Find Similar People
k Nearest Neighbors

45. Pick a Similarity Metric
• Euclidean Distance
• Jaccard index
• Cosine Similarity

46. Joe’s Similarity to Paul
(Paul (f1) - Joe (f1))^2 + (Paul (f2) - Joe (f2))^2)^1/2

47. Once We’ve Calculated
Similarities
• Fill In Missing Interests
• Target Ads, Content, Products
• ???
• Profit!

48. Generalizing RDF
Triples to Vector Space

49. • Subjects are Rows
• Objects are Columns
• Predicates are values

50. Object 1 Object 2
Subject 1 Predicate
Subject 2

51. Predicates Should be
Mutually Exclusive
• Paul likes Ruby
• Paul hates PHP
• Paul loves PHP

52. Assign Values to
Predicates
• 1 = Hates
• 2 = Dislikes
• 3 = Neutral
• 4 = Likes
• 5 = Loves

53. More Applications

54. Supervised Learning
• Classifiers
• Ontology Mapping
• Assigning Instances to Concepts

55. Ontology Mapping
• Examples from Ontology A
• Examples from Ontology B

57. Train Classifiers
• One Classifier for each Concept in A
• One Classifier for each Concept in B

58. Classify Instances
• Use A Classifiers to predict which concepts
B instances map to
• Use B Classifiers to predict which concepts
A instances map to

59. Use Classified Instances
• Predict Concept Mappings
• Which in A match ones in B

61. Limitations
• One Classifier per Concept
• Large Ontologies Could be a Problem
• Ontologies should be a little similar

62. Unsupervised Learning
• Clustering
• Hierarchical Clustering
• Learning Ontologies from Text

63. Machine Learning as
Triage
• Automatically tag or recommend Examples
the algorithm is Certain About
• Send uncertain examples to human for
review

64. Thank You
Paul Dix
paul@pauldix.net
http://pauldix.net