When big data leads to lost data
Proceedings of the 5th Ph.D. workshop on Information and knowledge
| Hi-index | 0.00 |
A large fraction of the useful web is comprised of specification documents that largely consist of \langleattribute name, numeric value\rangle pairs embedded in text. Examples include product information, classified advertisements, resumes, etc. The approach taken in the past to search these documents by first establishing correspondences between values and their names has achieved limited success because of the difficulty of extracting this information from free text. We propose a new approach that does not require this correspondence to be accurately established. Provided the data has “low reflectivity”, we can do effective search even if the values in the data have not been assigned attribute names and the user has omitted attribute names in the query. We give algorithms and indexing structures for implementing the search. We also show how hints (i.e., imprecise, partial correspondences) from automatic data extraction techniques can be incorporated into our approach for better accuracy on high reflectivity data sets. Finally, we validate our approach by showing that we get high precision in our answers on real data sets from a variety of domains.