Name
Abstract | ||
|---|---|---|
In this paper, we propose an attribute retrieval approach which extracts and ranks attributes from HTML tables. We distinguish between class attribute retrieval and instance attribute retrieval. On one hand, given an instance (e.g. University of Strathclyde) we retrieve from the Web its attributes (e.g. principal, location, number of students). On the other hand, given a class (e.g. universities) represented by a set of instances, we retrieve common attributes of its instances. Furthermore, we show we can reinforce instance attribute retrieval if similar instances are available. Our approach uses HTML tables which are probably the largest source for attribute retrieval. Three recall oriented filters are applied over tables to check the following three properties: (i) is the table relational, (ii) has the table a header, and (iii) the conformity of its attributes and values. Candidate attributes are extracted from tables and ranked with a combination of relevance features. Our approach is shown to have a high recall and a reasonable precision. Moreover, it outperforms state of the art techniques. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2063576.2063654 | CIKM |
Keywords | Field | DocType |
attribute retrieval approach,candidate attribute,common attribute,instance attribute retrieval,high recall,ranks attribute,class attribute retrieval,html table,attribute retrieval,similar instance,information retrieval | Data mining,Information retrieval,Ranking,Computer science,Variable and attribute,Header,Conformity,Recall,Visual Word,Attribute domain | Conference |
Citations | PageRank | References |
7 | 0.50 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arlind Kopliku | 1 | 64 | 9.45 |
| Mohand Boughanem | 2 | 923 | 109.00 |
| Karen Pinel-Sauvagnat | 3 | 175 | 24.64 |