In this paper, we propose a survival modeling approach to promoting ranking diversity for biomedical information retrieval. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using Wikipedia to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, we further evaluate our approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that our approach can achieve promising performance improvements.

With the rapid growing of Web 2.0, people spend more time on social networks such as Facebook and Twitter. In order to know the people they are interacting with, finding the web appearances of them will help the social network users to a great extent. We propose a novel and effective latent factor model to find web appearances of target social network users. Our method solves the name ambiguity problem by simultaneously exploring the link structure of social networks and the web. Experiments on real-world data show the superiority of our method over several baselines.

In this paper, we propose an approach based on a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on JV-size slide window. Experiments on TREC 2007 Genomics collection and two distinctive IR baseline runs demonstrate the effectiveness of our method in promoting ranking diversity for biomedical information retrieval. Evaluation results show that our approach can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track.

BACKGROUND: In the biomedical domain, the desired information of a question (query) asked by biologists usually is a list of a certain type of entities covering different aspects that are related to the question, such as genes, proteins, diseases, mutations, etc. Hence it is important for a biomedical information retrieval system to be able to provide comprehensive and diverse answers to fulfill biologists' information needs. However, traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. This assumption may result in high redundancy and low diversity in the retrieval ranked lists. RESULTS: In this paper, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. It consists of two component models, namely the aspect-term relevance model and the aspect-term novelty model. They model the relevance of a document and the novelty of a document respectively. We show that our approach can achieve 16.4% improvement over the highest aspect level MAP reported in the TREC 2007 Genomics track, and 9.8% improvement over the highest passage level MAP reported in the TREC 2007 Genomics track. CONCLUSIONS: The proposed combination model which models aspects, terms, topic relevance and document novelty as potential functions is demonstrated to be effective in promoting ranking diversity as well as in improving relevance of ranked lists for genomics search. We also show that the use of aspect plays an important role in the model. Moreover, the proposed model can integrate various different relevance and novelty measures easily.

Traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. However, this assumption may result in high redundancy and low diversity in a ranked list. In order to provide comprehensive and diverse answers to fulfill biologists' information need, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. Experiments conducted on the TREC 2006 and 2007 Genomics collections show that the proposed approach is effective in promoting both diversity and relevance of retrieval ranked lists.

In this paper, we propose a probabilistic survival model derived from the survival analysis theory for measuring aspect novelty. The retrieved documents' query-relevance and novelty are combined at the aspect level for re-ranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval.

In this paper, we present a context-sensitive approach for re-ranking retrieved documents for further improving the effectiveness of high-performance biomedical literature retrieval systems. For each topic, a two-dimensional context is learnt from the top N and the last N' documents in initial retrieval ranked list, which contains lexical context and conceptual context. The probabilities that retrieved documents are generated within the contextual space are then computed for document re-ranking. Empirical evaluation on the TREC Genomics full-text collection and two strong biomedical literature retrieval runs demonstrates that the context-sensitive re-ranking approach yields better retrieval performance.

Combination of multiple evidences has been shown to be effective in genomics literature retrieval. Citation information is an intuitive evidence for facilitating literature retrieval. Previous research on citation analysis has demonstrated that useful linkage information can be extracted from the citation graph. However, the question of how the combination of citation evidence and content evidence should be done to maximize retrieval accuracy still remains largely unanswered. In this paper, we propose BioCLink, a new probabilistic approach that integrates citation evidence into content-based weighting function for improving genomics literature retrieval performance. Based on findings of our previous study, a strategy for modeling citation evidence is proposed. BioCLink provides the combination of content and citation evidences with a theoretical support. Moreover, exhaustiveparameter tuning can be avoided using BioCLink. Extensive experiments on TREC 2006 and 2007 Genomics collections demonstrate the advantages and effectiveness of our proposed methods.