Machine Learning Techniques for the Semantic Web
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Transcript of Machine Learning Techniques for the Semantic Web
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Machine Learning
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Semantic Web
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What is Semantic Web?
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Ontology
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RDF
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Machine Learning is about Data
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actually...
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Making Predictions Based on Data
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FOAFSimple Example
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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> .
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Marco only knows 4 people?
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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>
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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>
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but that’s not really machine learning
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Short
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Machine Learning is
• How you formulate the problem
• How you represent the data
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• Graphical Models
• Vector Space Models
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Back to FOAFConvert RDF triples to vector space
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We Want to Find Groups of People
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To make predictions on their interests...
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(subject) (predicate) (object)Paul knows JeffPaul knows JoePaul knows MarcoJeff knows Joe
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Vector Space Representation
Jeff Joe Marco Paul
Jeff 1 1
Joe 1 1
Marco 1
Paul 1 1 1
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Latent Factors Analysis
• Used in Latent Semantic Indexing (LSI)
• Good for finding synonyms
• Good for finding “genres”
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Latent Factors Methods
• Principle Component Analysis (PCA)
• Singular Value Decomposition (SVD)
• Restricted Boltzmann Machines (RBM)
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Considerations for Semantic Web Data
• Large Data Sets
• Sparse Data Sets
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Netflix Prize Research
• Movie Review Data set has similar problems
• Generalized Hebbian Algorithm for Dimensionality Reduction in NLP (Gorrell ’06.)
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Reduce Dimensions
• 1m x 1m matrix with 1m people
• Reduce to 1m x 100
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100 Latent FactorsRepresent different groups of people based on who
they know.
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Factor 1 Factor 2
Paul 0.678 0.311
Joe 0.455 0.432
Jeff 0.476 0.398
Marco 0.203 0.789
What the Data Might Look Like
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Find Similar Peoplek Nearest Neighbors
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Pick a Similarity Metric
• Euclidean Distance
• Jaccard index
• Cosine Similarity
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Joe’s Similarity to Paul(Paul (f1) - Joe (f1))^2 + (Paul (f2) - Joe (f2))^2)^1/2
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• Fill In Missing Interests
• Target Ads, Content, Products
• ???
• Profit!
Once We’ve Calculated Similarities
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Generalizing RDF Triples to Vector Space
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• Subjects are Rows
• Objects are Columns
• Predicates are values
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Object 1 Object 2
Subject 1 Predicate
Subject 2
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Predicates Should be Mutually Exclusive
• Paul likes Ruby
• Paul hates PHP
• Paul loves PHP
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Assign Values to Predicates
• 1 = Hates
• 2 = Dislikes
• 3 = Neutral
• 4 = Likes
• 5 = Loves
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More Applications
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Supervised Learning
• Classifiers
• Ontology Mapping
• Assigning Instances to Concepts
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Ontology Mapping
• Examples from Ontology A
• Examples from Ontology B
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Train Classifiers
• One Classifier for each Concept in A
• One Classifier for each Concept in B
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Classify Instances
• Use A Classifiers to predict which concepts B instances map to
• Use B Classifiers to predict which concepts A instances map to
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Use Classified Instances
• Predict Concept Mappings
• Which in A match ones in B
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Limitations
• One Classifier per Concept
• Large Ontologies Could be a Problem
• Ontologies should be a little similar
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Unsupervised Learning
• Clustering
• Hierarchical Clustering
• Learning Ontologies from Text
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Machine Learning as Triage
• Automatically tag or recommend Examples the algorithm is Certain About
• Send uncertain examples to human for review