KGen: a knowledge graph generator from biomedical scientific literature.
KGen: a knowledge graph generator from biomedical scientific literature.
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Kgen：来自生物医学科学文献的知识图谱生成器。
DOI：
10.1186/s12911-020-01341-5
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发表时间：
2020-12-14
影响因子：
3.5
通讯作者：
de Ribaupierre H
中科院分区：
文献类型：
--
作者：
Rossanez A;Dos Reis JC;Torres RDS;de Ribaupierre H
知识通常是从科学调查产生的数据中产生的。在几个领域中不断增长的科学研究产生了大量的数据，从这些数据中获得新的知识需要计算帮助。例如，阿尔茨海默氏症，一种威胁生命的退行性疾病，目前还无法治愈。随着科学界努力更好地理解它并找到治疗方法，产生了大量数据，也产生了新的知识。这种知识的恰当表达给研究人员、科学界和社会带来了巨大的好处。在这篇文章中，我们研究和评估了一种从科学文献中的生物医学文本生成知识图(KGs)的半自动方法。我们的解决方案探索自然语言处理技术，目的是提取和表示以KGS编码的科学文献知识。我们的方法将KGS中表示的实体和关系链接到Web上现有的生物医学本体中的概念。我们通过从非结构化文本中生成KG来证明我们方法的有效性，这些KG来自于一组从阿尔茨海默病科学论文中提取的摘要。我们让医生通过对摘要的分析，比较我们从手动提取的三元组中提取的三元组。评估还涉及医生使用我们的软件工具对生成的KG进行定性分析。实验结果表明了所生成的KGS的质量。该方法提取了大量的三元组，表明了基于规则的方法在文本关系识别中的有效性。此外，还成功地获取了本体链接，验证了本文提出的本体链接方法的有效性。我们证明了我们的建议在构建表示从生物医学科学文本中获得的知识的本体链接的知识方面是有效的。这种表征可以为各个领域的研究增加价值，使研究人员能够比较不同研究中概念的出现情况。生成的KG可能为基于数据分析的潜在新理论的提出铺平道路，以促进其研究领域的最新水平。
Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.
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影响因子：
3
作者：
Gangemi, Aldo;Presutti, Valentina;Mongiovi, Misael
通讯作者：
Mongiovi, Misael
影响因子：
3.7
作者：
Barnickel T;Weston J;Collobert R;Mewes HW;Stümpflen V
通讯作者：
Stümpflen V
影响因子：
120.7
作者：
EVANS, DA;FUNKENSTEIN, H;TAYLOR, JO
通讯作者：
TAYLOR, JO
DOI：
10.1093/database/bav049
发表时间：
2015
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Kamdar MR;Dumontier M
通讯作者：
Dumontier M
DOI：
10.1097/nen.0b013e318232a379
发表时间：
2011-11-01
影响因子：
3.2
作者：
Braak, Heiko;Thal, Dietmar R.;Del Tredici, Kelly
通讯作者：
Del Tredici, Kelly