Ontologies and the Semantic Web

Coupling Semi-Supervised Learning of Categories and RelationsbyAndrew Carlson, Justin Betteridge, Estevam R. Hruschka Jr. and Tom M. MitchellSchool of Computer ScienceCarnegie Mellon University

presented byThomas Packer20 minutes in length, allowing 10 minutes of discussion. Present the main ideas and results of the paper. 35 points total.

Prepare two good questions about each other paper in advance, write them down, and print them out. Add at least one more question during the presentation. Used for class discussion. Due on the date the paper is presented, 5 points for each set of questions for a paper.

http://osm.cs.byu.edu/CS652s09/projects.html

1Bootstrapped Information ExtractionSemi-Supervised:Seed knowledge (predicate instances & patterns)Pattern learners (uses learned instances)Instance learners (uses learned patterns)Feedback Loop:Rel1(X, Y)Sent1(X, Y), Rel0(X, Y) Pat1Pat1: Sent2(A, B) Rel1(A, B)

5 points for the overview -- introduce the work and state the goals and objectives of the authors;2Challenges and Previous SolutionsSemantic drift: Feedback loop amplifies error and ambiguities.Semi-Supervised learning often suffers from being under-constrained.

Multiple mutually-exclusive predicate learning: Positive examples of one predicate are also negative examples of others.Category and predicate learning: Arguments must be of certain types.

5 points for the overview -- introduce the work and state the goals and objectives of the authors;3Does More Look Harder?

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 4ApproachSimultaneous bootstrapped training of multiple categories and multiple relations.Growing related knowledge provides constraints to guide continued learning.Ontology Constraints:Mutually exclusive predicates imply negative instances and patterns.Hypernyms imply positive instances.Relation argument type constraints imply positive category and negative relation instances.

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 5Mutual Exclusion Constraintcity and scientist categories are mutually exclusive.If Boston is an instance of city, then it is also a negative instance of scientist.If mayor of arg1 is a pattern for city, then it is also a negative pattern for scientist.

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 6Hypernym Constraintsathlete is a hyponym of person.If John McEnroe is a positive instance of athlete, then it is also a positive instance of person.

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 7Type Checking ConstraintsThe ceoOf() relation must have arguments of type person and company.If bicycle is not a person then ceoOf(bicycle, Microsoft) is a negative instance of ceoOf().If ceoOf(Steve Ballmer, Microsoft) is true, then Steve Ballmer is a positive instance of person. Microsoft handled similarly.

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 8Coupled Bootstrap Learner

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 9Knowledge Constraints Makes Extraction Easier

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 10Knowledge Constraints Makes Extraction Easier

15 points for the approach -- explain the basic problem and how the authors attacked and solved the problem; 11ConclusionClearly shows improvements based on constraints.Could probably benefit byadding probabilistic reasoninglarger corpushigher thresholdsmore contrastive categoriesother techniques discussed in this class10 points for the conclusion -- interpret the results and give your assesment of the strengths, weakness, and possible future work; 12

Questions5 points for answers to questions -- answer the questions as posed by the class. 13