Published Versions 1 Vol 2 (4) : 443–486 2020
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The Semantic Data Dictionary – An Approach for Describing and Annotating Data
: 2020 - 03 - 10
: 2020 - 03 - 21
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Abstract & Keywords
Abstract: It is common practice for data providers to include text descriptions for each column when publishing data sets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a column in a data set, existing data dictionaries typically are not machine-readable and do not follow a common specification standard. We introduce the Semantic Data Dictionary, a specification that formalizes the assignment of a semantic representation of data, enabling standardization and harmonization across diverse data sets. In this paper, we present our Semantic Data Dictionary work in the context of our work with biomedical data; however, the approach can and has been used in a wide range of domains. The rendition of data in this form helps promote improved discovery, interoperability, reuse, traceability, and reproducibility. We present the associated research and describe how the Semantic Data Dictionary can help address existing limitations in the related literature. We discuss our approach, present an example by annotating portions of the publicly available National Health and Nutrition Examination Survey data set, present modeling challenges, and describe the use of this approach in sponsored research, including our work on a large NIH-funded exposure and health data portal and in the RPI-IBM collaborative Health Empowerment by Analytics, Learning, and Semantics project. We evaluate this work in comparison with traditional data dictionaries, mapping languages, and data integration tools.
Keywords: Semantic Data Dictionary; Dictionary mapping; Codebook; Knowledge modeling; Data integration; Data dictionary; Mapping language; Metadata standard; Semantic Web; Semantic ETL; FAIR; Data
Acknowledgements
This work is supported by the National Institute of Environmental Health Sciences (NIEHS) Award 0255-0236-4609/1U2CES026555-01, IBM Research AI through the AI Horizons Network, and the CAPES Foundation Senior Internship Program Award 88881.120772 / 2016-01. We acknowledge the members of the Tetherless World Constellation (TWC) and the Institute for Data Exploration and Applications (IDEA) at Rensellaer Polytechnic Institute (RPI) for their contributions, including Rebecca Cowan, John Erickson, and Oshani Seneviratne.
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Article and author information
Cite As
S.M. Rashid, J.P. McCusker, P. Pinheiro, M.P. Bax, H. Santos, J.A. Stingone, A.K. Das & D.L. McGuinness. The semantic data dictionary – an approach for describing and annotating data. Data Intelligence 2(2020), 443–486. doi: 10.1162/dint_a_00058
Sabbir M. Rashid
S.M. Rashid (rashis2@rpi.edu), in drafting the paper, introduced the research, motivation, and claims of this article in Section 1, conducted the majority of the literature review presented in Section 2, summarized the methodology associated with the approach in Section 3, formulated the example of Section 4, detailed the case studies presented in Section 5, performed the evaluation of Section 7 and 8, helped with the discussion in Section 9, and summarized the conclusions of the article in Section 10.
rashis2@rpi.edu
Sabbir M. Rashid is a PhD student at Rensselaer Polytechnic Institute (RPI) working with Professor Deborah L. McGuinness on research related to data annotation and harmonization, ontology engineering, knowledge epresentation, and various forms of reasoning. Prior to attending RPI, Mr. Rashid completed a double major at Worcester Polytechnic Institute, where he received B.S. degrees in both Physics and Electrical & Computer ngineering. Much of his graduate studies at RPI have involved the research discussed in this article. His current work includes the application of deductive and abductive inference techniques over linked health data, such as in the context of chronic diseases like diabetes.
0000-0002-4162-8334
James P. McCusker
J.P. McCusker (mccusj2@rpi.edu) contributed to the content of Section 3 and aided in the formulation of the evaluationof Section 7.
James P. McCusker is the Director of Data Operations at the Tetherless World Constellation at Rensselaer Polytechnic Institute. He works with Deborah McGuinness on using knowledge graphs to further scientific research, especially in biomedical domains. He has worked on applying semantics to numerous projects, including drug repurposing using systems biology, cancer genome resequencing, childhood health and environmental exposure, analysis of sea ice conditions and materials science. He is the architect of the open source Whyis knowledge graph development and management framework, which has been used across many of these domains.
0000-0003-1085-6059
Paulo Pinheiro
P. Pinheiro (pinhep@rpi.edu) helped scope the example of Section 4.
Paulo Pinheiro is a data scientist and software engineer managing projects at the frontier between artificial intelligence and databases. His areas of expertise include the following: data policies and information assurance, such as security and privacy; data operation including curation, quality monitoring, semantic integration, provenance management and uncertainty assessment; data visualization; and data analytics including automated reasoning. Paulo holds a PhD in Computer Science from the University of Manchester, UK.
0000-0001-8469-4043
Marcello P. Bax
M.P. Bax (bax@ufmg.br) helped with the conducting of the literature review of Section 2 and aided in the formulation of theevaluation of Section 7.
Marcello P. Bax is a professor and researcher in the Postgraduate Program in Knowledge Management and Organization (PPG-GOC) at the School of Information Science at Federal University of Minas Gerais, Brazil. Prior to joining the School of Information Science, Dr. Bax was a postdoctoral fellow in the Computer Science Department at UFMG, a leading Computer Science research group in Latin America. Dr. Bax spent a year on sabbatical with Professor McGuinness’ group and the Tetherless World Constellation at Rensselaer Polytechnic Institute, during which he worked with the coauthors on the research described in this article. His research seeks to develop methods for the curating of scientific data, with a focus on semantic annotation, the goal of building curatorial repositories for data reuse and reproduction of scientific research results.
0000-0003-0503-3031
Henrique Santos
H.O. Santos (oliveh@rpi.edu) helped synthesize the related literature in Section 2 and presented some limitations of our approach in Section 10.
Henrique O. Santos is a Research Scientist in the Tetherless World Constellation at Rensselaer Polytechnic Institute, where he researches and applies Semantic Web technologies in multidisciplinary domains for supporting more flexible, more efficient, and improved solutions in comparison with traditional approaches. His research interests include data integration, knowledge representation, domain-specific reasoning and explainable artificial intelligence. He has over 10 years of experience working with Semantic Web technologies and holds a PhD in Applied Informatics from Universidade de Fortaleza, Brazil.
0000-0002-2110-6416
Jeanette A. Stingone
J.A. Stingone (js5406@cumc.columbia.edu) conducted the experiment and drafted the content presented in Section 6.
Jeanette A. Stingone is an Assistant Professor in the Department of Epidemiology at Columbia University’s Mailman School of Public Health. She couples data science techniques with epidemiologic methods to address research questions in children’s environmental health. She currently leads the Data Science Translation and Engagement Group of the Human Health and Exposure Analysis Resource Data Center. In this role, she supports the use of metadata standards and ontologies for data harmonization efforts across disparate studies of environmental health. Dr. Stingone’s interests also include the use of collective science initiatives to advance public health research.
0000-0003-3508-8260
Amar K. Das
A.K. Das (amardas@us.ibm.com) led the proposal of the research problems associated with the HEALS projects mentioned in Section 5.
Amar K. Das is the Program Director of Integrated Care Research at IBM Research and an Adjunct Associate Professor of Biomedical Data Science at Dartmouth College. His research activities include the development of biomedical ontologies and Semantic Web technologies for clinical decision support, information retrieval and machine learning. In his role in the RPIIBM HEALS initiative, Dr. Das is the IBM technical lead for advancing knowledge representation and reasoning in healthcare. Dr. Das holds an MD and PhD in Biomedical Informatics from Stanford University, and has completed a residency in Psychiatry and a postdoctoral fellowship in Clinical Epidemiology at Columbia University/New York State Psychiatric Institute.
0000-0003-3556-0844
Deborah L. McGuinness
D.L. McGuinness (dlm@cs.rpi.edu) has guided the overall direction of this research. All the authors havemade meaningful and valuable contributions in revising and proofreading the resulting manuscript.
Deborah L. McGuinness is the Tetherless World Senior Constellation Chair and Professor of Computer and Cognitive Science. She is also the founding director of the Web Science Research Center at Rensselaer Polytechnic Institute. Dr. McGuinness has been recognized with awards as a fellow of the American Association for the Advancement of Science (AAAS) for contributions to the Semantic Web, knowledge representation, and reasoning environments and as the recipient of the Robert Engelmore award from Association for the Advancement of Artificial Intelligence (AAAI) for leadership in Semantic Web research and in bridging Artificial Intelligence (AI) and eScience, significant contributions to deployed AI applications, and extensive service to the AI community. Deborah is a leading authority on the Semantic Web and has been working in knowledge representation and reasoning environments for over 30 years and leads the research group that designed and implemented the research presented in this paper.
0000-0001-7037-4567
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Published: Dec. 14, 2020 (Versions1
References
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