Machine Learning Techniques for the Semantic Web

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

Machine Learning

Semantic Web

What is Semantic Web?

Ontology

Machine Learning is about Data

actually...

Making Predictions Based on Data

FOAF Simple Example

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> .

Marco only knows 4 people?

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>

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>

but that’s not really machine learning

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

• Graphical Models • Vector Space Models

Back to FOAF Convert RDF triples to vector space

We Want to Find Groups of People

To make predictions on their interests...

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

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

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

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

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

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

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

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

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

Find Similar People k Nearest Neighbors

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

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

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

Generalizing RDF Triples to Vector Space

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

Object 1 Object 2 Subject 1 Predicate Subject 2

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

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

More Applications

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

Ontology Mapping • Examples from Ontology A • Examples from Ontology B

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

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

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

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

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

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

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

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Machine Learning Techniques for the Semantic Web

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