Proceedings of the 27th International Conference on Very Large Data Bases
Data extraction and label assignment for web databases
WWW '03 Proceedings of the 12th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
ACM SIGKDD Explorations Newsletter
ViPER: augmenting automatic information extraction with visual perceptions
Proceedings of the 14th ACM international conference on Information and knowledge management
Automatic wrapper induction from hidden-web sources with domain knowledge
Proceedings of the 10th ACM workshop on Web information and data management
Automatically Extracting Form Labels
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Transactions on large-scale data- and knowledge-centered systems III
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A large number of wrappers generate tables without column names for human consumption because the meaning of the columns are apparent from the context and easy for humans to understand, but in emerging applications, labels are needed for autonomous assignment and schema mapping where machine try to understand the tables. Autonomous label assignment is critical in volume data processing where ad hoc mediation, extraction and querying is involved. We propose an algorithm Lads for Labeling Anonymous Datasets, which can holistically label tabular web document. The algorithm has been tested on anonymous datasets from a number of sites, e.g music, movie, political, demographic, athletic obtained through different search engines such as Google, Yahoo and MSN. The comparative probabilities of attributes being candidate labels are presented which seem to be very promising, achieved as high as 93% probability of assigning good label to anonymous attribute. To the best of our knowledge, this is the first of its kind for label assignment based on multiple search engines' recommendation.