An important search task in the biomedical domain is to find medical records of patients who are qualified for a clinical trial.One commonly used approach is to apply NLP tools to map terms from queries and documents to concepts and then compute the relevance scores based on the conceptbased representation.However, the mapping results are not perfect, and none of previous work studied how to deal with them in the retrieval process.In this paper, we focus on addressing the limitations caused by the imperfect mapping results and study how to further improve the retrieval performance of the concept-based ranking methods.In particular, we apply axiomatic approaches and propose two weighting regularization methods that adjust the weighting based on the relations among the concepts.Experimental results show that the proposed methods are effective to improve the retrieval performance, and their performances are comparable to other top-performing systems in the TREC Medical Records Track.

Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Many information needs of enterprise search center around entities. Intuitively, information related to the entities mentioned in the query, such as related entities, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities to improve search quality. Experiment results show that the proposed entity-centric query expansion strategy is more effective to improve the search performance than the state-of-the-art pseudo feedback methods on longer, natural language-like queries with entities.

Search over enterprise data is essential to every aspect of an enterprise because it helps users fulfill their information needs. Similar to Web search, most queries in enterprise search are keyword queries. However, enterprise search is a unique research problem because, compared with the data in traditional IR applications (e.g., text data), enterprise data includes information stored in different formats. In particular, enterprise data include both unstructured and structured information, and all the data center around a particular enterprise. As a result, the relevant information from these two data sources could be complementary to each other. Intuitively, such integrated data could be exploited to improve the enterprise search quality. Despite its importance, this problem has received little attention so far. In this paper, we demonstrate the feasibility of leveraging the integrated information in enterprise data to improve search quality through a case study, i.e., finding relevant information of certain types from enterprise data. Enterprise search users often look for different types of relevant information other than documents, e.g., the contact information of per- sons working on a product. When formulating a keyword query, search users may specify both content requirements, i.e., what kind of information is relevant, and type requirements, i.e., what type of information is relevant. Thus, the goal is to find information relevant to both requirements specified in the query. Specifically, we formulate the problem as keyword search over structured or semistructured data, and then propose to leverage the complementary unstructured information in the enterprise data to solve the problem. Experiment results over real world enterprise data and simulated data show that the proposed methods can effectively exploit the unstructured information to find relevant information of certain types from structured and semistructured information in enterprise data.