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We study the following problem: given the name of an ad-hoc concept as well as a few seed entities belonging to the concept, output all entities belonging to it. Since producing the exact set of entities is hard, we focus on returning a ranked list of entities. Previous approaches either use seed entities as the only input, or inherently require negative examples. They suffer from input ambiguity and semantic drift, or are not viable options for ad-hoc tail concepts. In this paper, we propose to leverage the millions of tables on the web for this problem. The core technical challenge is to identify the "exclusive" tables for a concept to prevent semantic drift; existing holistic ranking techniques like personalized PageRank are inadequate for this purpose. We develop novel probabilistic ranking methods that can model a new type of table-entity relationship. Experiments with real-life concepts show that our proposed solution is significantly more effective than applying state-of-the-art set expansion or holistic ranking techniques.

When collecting and combining data from various sources into a data warehouse, ensuring high data quality and consistency becomes a significant, often expensive, challenge. Common data quality problems include inconsistent data conventions amongst sources such as different abbreviations or synonyms; data entry errors such as spelling mistakes; missing, incomplete, outdated or otherwise incorrect attribute values. These data defects generally manifest themselves as foreign-key mismatches and approximately duplicate records, both of which make further data mining and decision support analyses either impossible or suspect. We demonstrate two new data cleansing operators, Fuzzy Lookup and Fuzzy Grouping, which address these problems in a scalable and domain-independent manner. These operators are implemented within Microsoft SQL Server 2005 Integration Services. Our demo will explain their functionality and highlight multiple realworld scenarios in which they can be used to achieve high data quality.

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Today's record matching infrastructure does not allow a flexible way to account for synonyms such as "Robert" and "Bob" which refer to the same name, and more general forms of string transformations such as abbreviations. We expand the problem of record matching to take such user-defined string transformations as input. These transformations coupled with an underlying similarity function are used to define the similarity between two strings. We demonstrate the effectiveness of this approach via a fuzzy match operation that is used to lookup an input record against a table of records, where we have an additional table of transformations as input. We demonstrate an improvement in record matching quality and efficient retrieval based on our index structure that is cognizant of transformations.