1. ODE: Ontology-Assisted Data Extraction Weifeng Su, Jiying Wang, Frederick H. Lochovsky Summarized by Joseph Park

2. Overview “Web databases…compose what is referred to as the deep Web” The goal of data extraction: – (1) Query result section identification - decides what section in a dynamically generated query result page contains the data that need to be extracted. – (2) Record segmentation - segments the query result section into records and extracts them. – (3) Data value alignment - aligns the data values from multiple records that belong to the same attribute so that they can be arranged into a table. – (4) Label assignment - assigns a suitable, meaningful label (i.e., an attribute name) to each column in an aligned table.

3. Problems Automatically extract data from query results Limitations of other systems: – Incapable of processing either zero or few query results. – Vulnerable to optional and disjunctive attributes. – Incapable of processing nested data structures. – No label assignment.

4. Approach ODE – Ontology-assisted data extraction PADE wrapper Query result annotation Attribute matching Ontology construction

5. Approach continued Query result section identification Record segmentation Data value alignment and label assignment – MaxEnt model is used

6. Experimental Results Extraction performed using DeLa

7. Conclusion Can only label attributes that appear in query result pages References a few DEG papers – DKE99, Tisp, TANGO Could take advantage of MaxEnt for pre- labeling data Need to look into DeLa for data extraction