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ODE: Ontology-Assisted Data Extraction

ODE: Ontology-Assisted Data Extraction. Weifeng Su, Jiying Wang, Frederick H. Lochovsky Summarized by Joseph Park. Overview. “Web databases…compose what is referred to as the deep Web” The goal of data extraction:

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ODE: Ontology-Assisted Data Extraction

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  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 sectionidentification - 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

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