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Machine Learning Techniques for the Semantic Web
1.
Machine Learning Techniques for
the Semantic Web Paul Dix http://pauldix.net paul@pauldix.net
2.
3.
4.
5.
Machine Learning
6.
Semantic Web
7.
8.
9.
What is Semantic
Web?
10.
11.
12.
Ontology
13.
RDF
14.
15.
Machine Learning is
about Data
16.
actually...
17.
Making Predictions Based
on Data
18.
19.
20.
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> .
23.
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
29.
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
56.
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
60.
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
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