1. Web Object Retrieval Zaiqing Nie, Yunxiao Ma, Shuming Shi, Ji-Rong Wen, Wei-Ying Ma, MSRA

2. Main Topic  how to treat the information on the Web as different structured objects which have attributes with different weights in terms of their importance  how to improve the performance of information retrieval based on these structured objects, especially for the precision

3. Proposed Models  Unstructured Model Each record is a bag of words Take into account the record extraction accuracy when merging different records  Structured Model Each is an structured object with multiple attributes Attributes have different weights in terms of importance Take into account both record extraction accuracy and attribute extraction accuracy  Hybrid Model A tradeoff between the two models above

4. Criticism  Advantages Shows clearly that either absolutely unstructured model or absolutely structured model is not the best way of information retrieval, with respect to the retrieval precision, so a tradeoff does have to be made Addresses how to handle multiple records corresponding to a same object and how to handle the large amount of inconsistent information on the web  Disadvantages and Problems It’s not really a novel idea in this paper since a lot of works in this area have been done to take advantage of the existing structures within the information on the web. This paper only uses the term “object” to make itself seem new The formula used in this paper to estimate the query relevance of a document (or an object or attribute) seems not reasonable enough, since TF/IDF measure can be much better than this kind of linearly combination.

5. Correlations  Information Retrieval  Object Retrieval Bag of words (Jaccard) Take advantage of structured-data NBC-like measure  Information Integration  Record Merging

6. Web Object Retrieval