Valuable knowledge of catalysis is often hidden in a large amount of scientific literature. There is an urgent need to extract useful knowledge to facilitate scientific discovery. This work takes the first step toward the goal in the field of catalysis. Specifically, we construct the first information extraction benchmark data set that covers the field of catalysis and also develop a general extraction framework that can accurately extract catalysis-related entities from scientific literature with 90% extraction accuracy. We further demonstrate the feasibility of leveraging the extracted knowledge to help users better access relevant information in catalysis through an entity-aware search engine and a correlation analysis system.

中文翻译：

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在催化中释放从科学文献中提取知识的力量

催化方面的宝贵知识往往隐藏在大量的科学文献中。迫切需要提取有用的知识以促进科学发现。这项工作向催化领域的目标迈出了第一步。具体来说，我们构建了第一个涵盖催化领域的信息提取基准数据集，并开发了一个通用提取框架，可以从科学文献中准确提取催化相关实体，提取准确率为 90%。我们进一步证明了利用提取的知识通过实体感知搜索引擎和相关性分析系统帮助用户更好地访问催化相关信息的可行性。