A weakly-supervised extraction method identifies concepts within conceptual hierarchies, at the appropriate level of specificity (e.g., Bank vs. Institution), to which attributes (e.g., routing number) extracted from unstructured text best apply. The extraction exploits labeled classes of instances acquired from a combination of Web documents and query logs, and inserted into existing conceptual hierarchies. The correct concept is identified within the top three positions on average over gold-standard attributes, which corresponds to higher accuracy than in alternative experiments. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing Abstracting methods; I.2.7 [Artificial Intelligence]: Natural Language Processing General Terms Algorithms, Experimentation Keywords Knowledge acquisition, class attributes, named entities, conceptual hierarchies, Web sea...