Name
Abstract | ||
|---|---|---|
An attribute dictionary is a set of attributes together with a set of common values of each attribute. Such dictionaries are valuable in understanding unstructured or loosely structured textual descriptions of entity collections, such as product catalogs. Dictionaries provide the supervised data for learning product or entity descriptions. In this demonstration, we will present AutoDict, a system that analyzes input data records, and discovers high quality dictionaries using information theoretic techniques. To the best of our knowledge, AutoDict is the first end-to-end system for building attribute dictionaries. Our demonstration will showcase the different information analysis and extraction features within AutoDict, and highlight the process of generating high quality attribute dictionaries. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDE.2012.126 | ICDE |
Keywords | Field | DocType |
different information analysis,entity collection,data record,learning product,autodict,high quality attribute dictionary,entity description,high quality,information retrieval,dictionaries,information analysis,information theoretic technique,product catalog,auto dict,high quality dictionaries,information extraction,automated dictionary discovery,analyzes input data record,unstructured textual description,text analysis,loosely structured textual description,attribute dictionary,end-to-end system,cataloguing,data mining,tv,common value,data model,data models,hidden markov model,hidden markov models | Data mining,Data modeling,Information retrieval,Computer science,Hidden Markov model,Database,Data records | Conference |
ISSN | ISBN | Citations |
1063-6382 | 978-1-4673-0042-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Chiang | 1 | 256 | 19.02 |
| Periklis Andritsos | 2 | 441 | 33.29 |
| Erkang Zhu | 3 | 26 | 3.61 |
| Renée J. Miller | 4 | 3545 | 373.59 |