Boosting Biomedical Information Retrieval Performance through Citation Graph: An Empirical Study

Abstract Background The number of biomedical research articles have increased exponentially with the advancement of biomedicine in recent years. These articles have thus brought a great difficulty in obtaining the needed information of researchers. Information retrieval technologies seek to tackle the problem. However, information needs cannot be completely satisfied by directly introducing the existing information retrieval techniques. Therefore, biomedical information retrieval not only focuses on the relevance of search results, but also aims to promote the completeness of the results, which is referred as the diversity-oriented retrieval. Results We address the diversity-oriented biomedical retrieval task using a supervised term ranking model. The model is learned through a supervised query expansion process for term refinement. Based on the model, the most relevant and diversified terms are selected to enrich the original query. The expanded query is then fed into a second retrieval to improve the relevance and diversity of search results. To this end, we propose three diversity-oriented optimization strategies in our model, including the diversified term labeling strategy, the biomedical resource-based term features and a diversity-oriented group sampling learning method. Experimental results on TREC Genomics collections demonstrate the effectiveness of the proposed model in improving the relevance and the diversity of search results. Conclusions The proposed three strategies jointly contribute to the improvement of biomedical retrieval performance. Our model yields more relevant and diversified results than the state-of-the-art baseline models. Moreover, our method provides a general framework for improving biomedical retrieval performance, and can be used as the basis for future work.

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Improving exploratory information retrieval for neophytes

ACM SIGAPP Applied Computing Review ◽

10.1145/3447332.3447336 ◽

2021 ◽

Vol 20 (4) ◽

pp. 50-64

Author(s):

Bissan Audeh ◽

Michel Beigbeder ◽

Christine Largeron ◽

Diana Ramírez-Cifuentes

Keyword(s):

Machine Learning ◽

Information Retrieval ◽

Digital Libraries ◽

Specific Information ◽

Information Need ◽

External Resources ◽

Learning Content ◽

Citation Graph ◽

Real Challenge ◽

Access To Documents

Digital libraries have become an essential tool for researchers in all scientific domains. With almost unlimited storage capacities, current digital libraries hold a tremendous number of documents. Though some efforts have been made to facilitate access to documents relevant to a specific information need, such a task remains a real challenge for a new researcher. Indeed neophytes do not necessarily use appropriate keywords to express their information need and they might not be qualified enough to evaluate correctly the relevance of documents retrieved by the system. In this study, we suppose that to better meet the needs of neophytes, the information retrieval system in a digital library should take into consideration features other than content-based relevance. To test this hypothesis, we use machine learning methods and build new features from several metadata related to documents. More precisely, we propose to consider as features for machine learning: content-based scores, scores based on the citation graph and scores based on metadata extracted from external resources. As acquiring such features is not a trivial task, we analyze their usefulness and their capacity to detect relevant documents. Our analysis concludes that the use of these additional features improves the performance of the system for a neophyte. In fact, by adding the new features we find more documents suitable for neophytes within the results returned by the system than when using content-based features alone.

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