Searching and extracting meaningful information out of highly heterogeneous datasets is a hot topic that received a lot of attention. However, the existing solutions are based on either rigid complex query languages (e.g., SQL, XQuery/XPath) which are hard to use without full schema knowledge, without an expert user, and which require up-front data integration. At the other extreme, existing solutions employ keyword search queries over relational databases [3], [1], [10], [9], [2], [11] as well as over semistructured data [6], [12], [17], [15] which are too imprecise to specify exactly the user's intent [16]. To address these limitations, we propose an alternative search paradigm in order to derive tables of precise and complete results from a very sparse set of heterogeneous records. Our approach allows users to disambiguate search results by navigation along conceptual dimensions that describe the records. Therefore, we cluster documents based on fields and values that contain t...