Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

Ghoulam, Aicha; Barigou, Fatiha; Belalem, Ghalem; Meziane, Farid

doi:10.4018/ijiit.2018070101

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…Shen et al [15] reflected the degree of association between words by utilizing the mutual information and took the entity concept with the highest mutual information value as the query extension word. To promote the performance of the QA model, Aicha et al [16] extended queries with medical entities and semantic relations based on the external resource of OWL. Nasir et al [17] presented a method of joint knowledge and related feedback, which analysed the diversity of query words and finds synonyms through different methods.…”

Section: Question Answering Based On Query Expansionmentioning

confidence: 99%

Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine

Zhai

Fang

Cui

2022

Mobile Information Systems

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With the development of the Internet of Things, intelligent medical devices and intelligent consultation platforms have been rapidly popularized, providing great convenience for medical treatment to patients and consultation to doctors. In the face of large-scale medical electronic information data, how to automatically and accurately learn professional knowledge and realize application is very important. The existing intelligent medical question answering models typically use query expansion to improve the accuracy of model matching answers but ignore the corresponding entity association between questions and answers, and the method of randomly generating negative samples cannot train the model to capture more semantic information. To solve these problems, a question answering method based on dual-dimensional entity association for intelligent medicine is proposed. This method learns semantics from the dual-dimension of question and answer respectively. In the question dimension, query extension words with strong relevance to query intention are obtained through entity association in the medical knowledge graph. In the answer dimension, answer sentences are segmented and sampled by employing a variety of similarity distances to generate negative samples in different ranges, provide different levels of correlation information between entities for model training, and then integrate the trained model to improve the accuracy and robustness of the question answering model. The experimental results show that the question answering model proposed in this paper has a good improvement in accuracy.

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Section: Question Answering Based On Query Expansionmentioning

confidence: 99%

Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine

Zhai

Fang

Cui

2022

Mobile Information Systems

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“…The utilization of Natural Language Processing (NLP) techniques and medical semantic resources for processing medical queries has been at the heart of MIR systems for years [2,5,6,[14][15][16][17][18][19][24][25][26][27][28][29][30][31][32][33][34][35]. For instance, Zhu and Carterette proposed a medical record search system for identifying cohorts required in clinical studies [33].…”

Section: Related Workmentioning

confidence: 99%

Head Concepts Selection for Verbose Medical Queries Expansion

et al. 2020

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Semantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. However, there are challenges that hinder their widespread use in real-world applications. Among these challenges is the insufficient knowledge individually encoded in existing medical ontologies, which is magnified when users express their information needs using long-winded natural language queries. In this context, many of the users' query terms are either unrecognized by the used ontologies, or cause retrieving false positives that degrade the quality of current medical information search approaches. In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically-enhanced inverted index documents, iii) contribute to a heuristical weighting technique in the query-document matching process. To demonstrate the effectiveness of the proposed approach, we conducted several experiments over the CLEF e-Health 2014 dataset. Findings indicate that the proposed method combining several extrinsic semantic resources proved to be more effective than related approaches in terms of precision measure.

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Research on Intelligent Retrieval Technology of User Information in Medical Information System Under the Background of Big Data

Liu¹

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

Cited by 8 publications

References 28 publications

Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine

Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine

Head Concepts Selection for Verbose Medical Queries Expansion

Research on Intelligent Retrieval Technology of User Information in Medical Information System Under the Background of Big Data

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