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1 CIS607, Fall 2004 Semantic Information Integration Presentation by Julian Catchen Week 3 (Oct. 13)

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Presentation on theme: "1 CIS607, Fall 2004 Semantic Information Integration Presentation by Julian Catchen Week 3 (Oct. 13)"— Presentation transcript:

1 1 CIS607, Fall 2004 Semantic Information Integration Presentation by Julian Catchen Week 3 (Oct. 13)

2 2 Questions from Homework 1 About ontologies and the Semantic Web: – The relationship between ontology-mapping and database- integration is obvious. The role of ontology-mapping in the Semantic Web, though intuitive, is not precise or clear. What exactly is the nature of the Semantic Web, how exactly is data structured and where exactly are these ontologies that describe the semantics of their data? (I realize that I can learn the answers to this question by doing some follow up reading on my own, but I wanted to note it anyway for my own reference.) -- Paea – This method assumes a well defined, thorough ontology hierarchy has been defined. How labor intensive is this step, and is it a reasonable assumption, considering the massive amount of data on the internet already? -- Kevin – Are the authors getting ahead of themselves? How likely is it that a web developer will define a valid ontology for the data on the web site, and organize it accordingly? It seems to me that annotating web sites with a valid ontology would be a significant undertaking. --Kevin

3 3 Questions from Homework 1 (cont ’ d) About machine learning approach: – The paper mentioned a multi-strategy learning approach, i.e. using some base learners and a meta learner. But how to combine the base learners together to get a good meta learners? Is there a way to use machine learning to do this? Or better to use heuristics? -- Xiangkui – The multiple-learning technique seems quite ingenious. Could the same concept be applied to the similarity estimator to further expand and improve results? That is, could they have used the Jaccard similarity measure concurrently with the most-specific-parent measure? Would that even make sense? -- Paea – Given that GLUE requires manual input for the Meta-Learner weights, commonsense knowledge, domain constraints and general heuristics means that extensive knowledge must be previously known about the two Ontology's before they can be mapped. Can GLUE really claim that this is an efficient mapping system to integrate data from disparate Ontology's? -- Kim

4 4 Questions from Homework 1 (cont ’ d) Other questions about GLUE: – What are other types of Learners that can be used in multi- stage learning? – What could be the reasons that matching accuracy for A to B is different from B to A (fig. 5)? -- Vikash – Can GLUE work well on two radically different ontologies? For example,ontology A may be a very simpleontology with only 20 concepts, but ontology B has 200 concepts. Then howto map between A and B? -- Xiaofang


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