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首页> 外文会议>Annual meeting of the Association for Computational Linguistics >Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences
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Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences

机译：Life-iNet：基于结构化网络的生命科学知识探索和分析系统

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摘要

Search engines running on scientific literature have been widely used by life scientists to find publications related to their research. However, existing search engines in the life-science domain, such as PubMed, have limitations when applied to exploring and analyzing factual knowledge (e.g., disease-gene associations) in massive text corpora. These limitations are mainly due to the problems that factual information exists as an unstructured form in text, and also keyword and MeSH term-based queries cannot effectively imply semantic relations between entities. This demo paper presents the Life-iNet system to address the limitations in existing search engines on facilitating life sciences research. Life-iNet automatically constructs structured networks of factual knowledge from large amounts of background documents, to support efficient exploration of structured factual knowledge in the unstructured literature. It also provides functionalities for finding distinctive entities for given entity types, and generating hypothetical facts to assist literature-based knowledge discovery (e.g., drug target prediction).
机译：生命科学家广泛使用运行于科学文献上的搜索引擎来查找与其研究相关的出版物。但是，生命科学领域中的现有搜索引擎（例如PubMed）在应用于探索和分析大规模文本语料库中的事实知识（例如疾病与基因的关联）时存在局限性。这些限制主要是由于以下事实：事实信息以文本的非结构形式存在，而且基于关键字和基于MeSH术语的查询无法有效地暗示实体之间的语义关系。本演示文件介绍了Life-iNet系统，以解决现有搜索引擎在促进生命科学研究方面的局限性。 Life-iNet自动从大量背景文件中构建结构化的事实知识网络，以支持在非结构化文献中对结构化的事实知识进行有效的探索。它还提供了功能，可为给定的实体类型查找独特的实体，并生成假设的事实，以帮助基于文献的知识发现（例如，药物靶标预测）。

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