The goal of the enterprise track is to conduct experiments with enterprise data that reflect the experiences of users in real organizations. This year, we continued with the CERC collection introduced in TREC 2007 (Bailey et al., 2007). Topics were developed in conjunction with CSIRO Enquiries, who field email and telephone questions about CSIRO research from the

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Opinions on the web present a wealth of information that can be leveraged in our day to day decision making tasks ranging from which product to purchase to which doctor to consult for a particular ailment. Due to the large volume of opinions available from different sources across web, digesting all the available opinions is a time consuming process which can severely impair user produc-tivity. As a result, these valuable opinions become more of a hindrance than a help in decision making scenarios especially those involving a large number of entities. Most existing work on solving this general problem has been focused on sum-marizing opinions to help users better digest all the opinions. Unfortunately, in many decision making scenarios, the number of entities in consideration could be quite large. Thus, making decisions by reading summaries alone would still be inefficient as you would need to read summaries of different entities thoroughly. Further, as most of the opinion summarization systems focus on generating

dain @ cl.cs.titech.ac.jp In today’s knowledge-based economy, having the proper expertise is crucial to resolving many tasks. Expertise Finding (EF) is the area of research concerned with matching available experts to given tasks. A standard approach is to input a task description/proposal/paper into an EF system, and receive recommended experts as output. Mostly, EF systems operate either via a content-based approach, which uses the text of the input, as well as the text of the available experts ’ profiles to determine a match, and structure-based approaches, which use the inherent relationship between experts, affiliations, papers, etc. (such as is available in citation networks). The majority of methods use one approach (content-based, "C") or the other (structure-based, "S"); though sometimes both approaches are used in tandem (C and S). The underlying data representation is fundamentally different, which makes the methods mutually incompatible. However, in previous work Watanabe et al. [34] achieved good results by converting content-based data to a structure-representation and using a structure-based approach. We posit that the reverse may also hold merit, namely, a content-based approach leveraging structure-based data converted to a content-based representation. We compare our idea to a content only-based approach, demonstrating that our method