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1 CIS607, Fall 2005 Semantic Information Integration Presentation by Enrico Viglino Week 3 (Oct. 12)

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1 1 CIS607, Fall 2005 Semantic Information Integration Presentation by Enrico Viglino Week 3 (Oct. 12)

2 2 Questions from Homework 1 About the data and ontologies: – The data source in practice might not be “regular” (structured ?) as DBs. We need an engine to process those unstructured data. How far from there -- Zebin – How to specify and use domain knowledge is critical in iMAP. Why not the user directly digests the database schema and make decision thereafter? --Zebin – The mapping accuracies look impressive for these examples, but what about examples where there is almost nothing related between ontologies, or very little of the two ontologies correspond. Are the mappings still accurate? -- Amanda

3 3 Questions from Homework 1 (cont ’ d) About machine learning and searching approach: – Please explain the Distribution Estimator (Section 4.1). They are not doing an n-squared comparison, right? -- Paea – How is the neighborhood constraint different from the “ most-specific- parent ” similarity meature? Shouldn ’ t accuracy go up with more data to learn from? -- Paea – For IMAP, it is over-simplified as match generator. What ’ s the other considerations than concatenations? Is the complexity overwhelming ? How can then process shared information and JOIN in databases – Paea – What characteristics of the base learners and taxonomies determine the weights of the base learners for the meta learner? How would you decide that? -- Amanda

4 4 Questions from Homework 1 (cont ’ d) About machine learning approach: – In the figure 5, the Matching accuracy graph is shown. Are these accuracy of GLUE calculated manually or automatically? -- DH – Can you explain the difference between (1) stopping when the mappings in two consecutive itereations do not change and (2) when the probability do not change? -- DH – If an instance s in the group that belong to B in U2 is not classified as belong to A, is it necessary that s really contributes to the probability of P(~A, B)? -- Jiawei – A meta-learner is more like a decision fusion. What ’ s the exact definition of meta-learner? --Jiawei

5 5 Questions from Homework 1 (cont ’ d) Other questions about GLUE or iMAP: – Is there some way we can obtain these systems to see them in action -- Shiwoong


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