The effect of using domain specific ontologies in query expansion in medical field

Jalali, Vahid; Borujerdi, Mohammad Reza Matash

doi:10.1109/innovations.2008.4781679

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…Mao et al [24] integrated a MeSH-enhanced concept layer into a language modeling framework to capture concept associations. Jalali et al [25] matched concept pairs between queries and documents using a semantic query expansion method. These studies motivate us to optimize query expansion in consideration of domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

A supervised term ranking model for diversity enhanced biomedical information retrieval

Lin

Yang

et al. 2019

BMC Bioinformatics

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BackgroundThe 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.ResultsWe 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.ConclusionsThe 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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Section: Related Workmentioning

confidence: 99%

A supervised term ranking model for diversity enhanced biomedical information retrieval

Lin

Yang

et al. 2019

BMC Bioinformatics

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“…In biomedicine, QE studies primarily focus on ontologies and pseudo-relevance feedback. For example, Jalali and Borujerdi (2008) and expand queries via MeSH ontology, and Srinivasan (1996), Aronson (1996), and Zhu et al (2006) expand queries via Unified Medical Language System (Lindberg et al, 1993). On the other hand, biomedical queries can be reformulated or systematically expanded based on initially retrieved documents focusing on abbreviations (Bacchin and Melucci, 2005), the controlled vocabulary of MeSH (Thesprasith and Jaruskulchai, 2014), or open vocabulary (Rivas et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Exploring Query Expansion for Entity Searches in PubMed

Huang¹,

2016

Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

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Identifying relevant studies from the entire scientific literature is an important task in biomedical research. Past efforts have incorporated semantically recognized biological entities and medical ontologies into biomedical literature search. However, semantic relations are largely overlooked by biomedical search engines. In this work, we aim to discover synonymous biomedical semantic relations between entities and explore their uses in query (semantics) understanding for improved retrieval performance. Specifically, we discover synonymous semantic relations from PubMed queries and apply them to query expansion and specification. In these two real-world scenarios, better PubMed retrieval effectiveness, in terms of recall and precision, can be achieved, demonstrating the utility of our proposed approach.

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“…Lu et al found that their replication of the PubMed's 4 Automatic Term Mapping (ATM) to MeSH terms was effective in finding more relevant documents, while, it did not improve the precision in the top retrieved documents. Jalali and Borujerdi [24] used the synonyms of the identified MeSH terms or direct descendants of them in the queries as expansion terms, and reported improvement over the various retrieval systems such as expansion with general purpose ontologies.…”

Section: Exploiting Meta-data For Patient Irmentioning

confidence: 99%

Improving patient record search: A meta-data based approach

Amini

Martínez

et al. 2016

Information Processing & Management

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The International Classification of Diseases (ICD) is a type of meta-data found in many Electronic Patient Records. Research to explore the utility of these codes in medical Information Retrieval (IR) applications is new, and many areas of investigation remain, including the question of how reliable the assignment of the codes has been. This paper proposes two uses of the ICD codes in two different contexts of search: Pseudo Relevance Judgments (PRJ) and Pseudo Relevance Feedback (PRF). We find that our approach to evaluate the TREC challenge runs using simulated relevance judgments has a positive correlation with the TREC official results, and our proposed technique for performing PRF based on the ICD codes significantly outperforms a traditional PRF approach. The results are found to be consistent over the two years of queries from the TREC Medical test collection.

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The effect of using domain specific ontologies in query expansion in medical field

Cited by 14 publications

References 13 publications

A supervised term ranking model for diversity enhanced biomedical information retrieval

A supervised term ranking model for diversity enhanced biomedical information retrieval

Exploring Query Expansion for Entity Searches in PubMed

Improving patient record search: A meta-data based approach

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