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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Biomedical question answering: A survey
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Biomedical question answering: A survey

机译：生物医学问答：一项调查

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Objectives: In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics. Materials and methods: We devised a conceptual framework within which to categorize current QA approaches. In particular, we used "semantic knowledge-based QA" as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches.Results: Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference.Conclusion: Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems.
机译：目标：在这项调查中，我们在基于语义知识的QA方法的更广泛框架内，回顾了生物医学QA（问题回答）的最新技术，并为人工与生物医学之间的关键交叉领域的未来研究发展指明了方向情报，信息检索和生物医学信息学。材料和方法：我们设计了一个概念框架，在其中对当前的质量保证方法进行了分类。尤其是，我们使用“基于语义知识的QA”作为归类的类别，在该类别下包含了基于语料库和基于知识库（KB）的QA技术和方法，这些方法和方法在QA流程中利用了基于语义知识的技术。我们将这些方法进一步分为三个子类别：（1）基于语义，（2）基于推理和（3）基于逻辑。在此框架的基础上，我们首先对属于这三个子类别的开放域或非生物医学域QA方法进行了调查。然后，我们首先对生物医学质量保证的特征和可用资源进行了深入的研究，然后依次对医学质量保证方法和生物学质量保证方法进行了回顾。结果：我们的综述提出了该领域未来研究发展的以下任务：（1）构建领域特定类型和问题分类法（生物学QA） ，（2）开发用于自然语言（NL）问题分析和分类的更先进的技术，（3）开发用于从潜在冲突的证据中生成答案的有效方法，（4）在整个质量检查流程中更广泛和综合地利用语义知识（5）结合逻辑和推理机制进行答案推断。结论：随着生物医学信息的增长，对质量保证系统的需求日益增长，它可以帮助用户更好地利用不断积累的信息。继续研究开发更先进的技术来处理NL文本，利用语义知识以及整合逻辑和推理机制，将导致更有用的QA系统。

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