Generating and Answering Ontological Queries over Semi-structured Medical Data

越来越多的个人信息（例如，人、事件、生物对象）可以以结构化或半结构化的形式电子地获得。基于这些数据手动选择满足某些约束的个体是复杂的、容易出错的、耗费时间和人员的工作。因此，需要开发能够根据可用数据自动或半自动回答问题的工具。虽然简单的问题可以直接使用自然语言中的关键字来表达和回答，但可以引用类型和关系信息的复杂问题增加了检索结果的精度，从而减少了对结果的后续手动验证的工作。这种情况的一个例子是使用电子病历来查找满足某些属性的非平凡组合的患者的设置，例如临床试验的资格标准。另一个也将被视为本项目用例的示例是学生向考试办公室询问有关学习和考试规定的问题的设置。在这两种情况下，原始问题都是用自然语言表达的。在GoAsq项目中，我们将研究、比较并最终联合收割机两种不同的方法，用于回答文本、半结构化和结构化数据上用自然语言表达的问题。一种方法是利用法国合作伙伴在基于文本的问题回答方面的专业知识，使用自然语言处理和信息检索技术直接回答自然语言问题。另一个试图将自然语言问题翻译成正式的，类似数据库的查询，然后回答这些正式的查询w.r.t.使用数据库技术的领域相关本体。自动翻译是必需的，因为它将是相当困难的人问的问题（医生，学生），以制定他们作为正式的查询。本体允许克服产生源数据的人（例如，撰写临床笔记的GP）和提出问题的人（例如，研究人员制定试验标准）。因此，GoAsq可以利用本体社区在通过本体访问数据方面取得的最新进展，称为基于本体的查询应答（OBQA）。更确切地说，在项目的任务1中，我们研究了上述两个用例（资格标准;研究规则）。在任务2中，我们将介绍和分析这些用例所需的现有形式化查询语言的扩展。任务3将开发从文本查询中提取正式查询的技术，任务4将评估以这种方式获得的方法，将其与基于文本的问答方法进行比较，并开发一种结合两者优点的混合方法。

More and more information on individuals (e.g., persons, events, biological objects) are available electronically in a structured or semi-structured form. Selecting individuals satisfying certain constraints based on such data manually is a complex, error-prone, and time and personnel-consuming effort. For this reason, tools that can automatically or semi-automatically answer questions based on the available data need to be developed. While simple questions can directly be expressed and answered using keywords in natural language, complex questions that can refer to type and relational information increase the precision of the retrieved results, and thus reduce the effort for posterior manual verification of the results. One example for this situation is the setting where electronic patient records are used to find patients satisfying non-trivial combinations of certain properties, such as eligibility criteria for clinical trials. Another example that will also be considered as a use case in this project is the setting where a student asks the examination office questions about study and examination regulations. In both cases, the original question is formulated in natural language.In the GoAsq project, we will investigate, compare, and finally combine two different approaches for answering questions formulated in natural language over textual, semi-structured, and structured data. One approach uses the expertise in text-based question answering of the French partners to directly answer natural language questions using natural language processing and information retrieval techniques. The other tries to translate the natural language questions into formal, database-like queries and then answer these formal queries w.r.t. a domain-dependent ontology using database techniques. The automatic translation is required since it would be quite hard for the people asking the questions (medical doctors, students) to formulate them as formal queries. The ontology allows to overcome the possible semantic mismatch between the person producing the source data (e.g., the GPs writing the clinical notes) and the person formulating the question (e.g., the researcher formulating the trial criteria). GoAsq can thus leverage recent advances obtained in the ontology community on accessing data through ontologies, called ontology-based query answering (OBQA). More precisely, in Task 1 of the project we investigate the two use cases mentioned above (eligibility criteria; study regulations). In Task 2 we will introduce and analyze extensions to existing formal query languages that are required by these use cases. Task 3 will develop techniques for extracting formal queries from textual queries, and Task 4 will evaluate the approach obtained this way, compare it with approaches for text-based question answering, and develop a hybrid approach that combines the advantages of both.