<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0021_s_006_w2aab3b7b5b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>This paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD) and related diseases using the machine reading approach.</p></sec><sec id="j_jdis-2017-0021_s_007_w2aab3b7b5b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level.</p></sec><sec id="j_jdis-2017-0021_s_008_w2aab3b7b5b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS), which can help answer the question of how AIDS/HIV and AD are very different yet related diseases.</p></sec><sec id="j_jdis-2017-0021_s_009_w2aab3b7b5b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction.</p></sec><sec id="j_jdis-2017-0021_s_010_w2aab3b7b5b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV.</p></sec><sec id="j_jdis-2017-0021_s_011_w2aab3b7b5b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.</p></sec></abstract>

PurposeThis paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD) and related diseases using the machine reading approach.Design/methodology/approachThe study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level.FindingsSeveral topics within the AD research field are identified, such as the Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS), which can help answer the question of how AIDS/HIV and AD are very different yet related diseases.Research limitationsSome manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction.Practical implicationsThis study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV.Originality/valueBoth the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.

Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

School of Informatics, Computing, and Engineering

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

PurposeThis paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD) and related diseases using the machine...

Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

Article Category: Research Paper

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 81 - 94

Eingereicht: 16. Okt. 2017

Akzeptiert: 12. Nov. 2017

DOI: https://doi.org/10.1515/jdis-2017-0021

Schlüsselwörter
Machine reading, Alzheimer’s disease, Knowledge discovery, Data mining

© 2017 Walter de Gruyter GmbH, Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

Article Category: Research Paper

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 81 - 94

Eingereicht: 16. Okt. 2017

Akzeptiert: 12. Nov. 2017

DOI: https://doi.org/10.1515/jdis-2017-0021

SchlüsselwörterMachine reading, Alzheimer’s disease, Knowledge discovery, Data mining

© 2017 Walter de Gruyter GmbH, Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Machine reading, Alzheimer’s disease, Knowledge discovery, Data mining