A Sociotechnical Framework for Semantic Biomedical Content Authoring and Publishing

Autores

  • Steve Fonin Mbouadeu St. John's University
  • Asim Abbas St. John's University
  • Fazel Keshtkar St. John's University
  • Iram Wajahat Allied Institute of Medical Sciences
  • Syed Ahmad Chan Bukhari St. John's University

DOI:

https://doi.org/10.32473/flairs.v35i.130665

Palavras-chave:

Structured data, Biomedical semantics, Automated semantic annotation, Biomedical content authoring, Peer-to-Peer, Structured data publishing, FAIR biomedical data

Resumo

Due to the ubiquity of unstructured biomedical data, significant obstacles still remain in achieving accurate and fast access to online biomedical content. In lieu of the growing volume of biomedical content on the web, embedding semantic annotations has become key to enhancing search engine context-aware indexing, thereby improving search speeds and retrieval accuracy. We introduce Semantically: a socio-technical framework for semantic biomedical content authoring and publishing. Identifying the appropriate semantic vocabulary for biomedical content annotation is a time-consuming and technically challenging process. Semantically automates this search by recommending highly accurate annotations from a wide range of biomedical ontologies. Furthermore, the framework is integrated with a knowledge-sharing system which allows biomedical authors to collaborate on identifying precise annotations during the content authoring process. Similarly, preserving content-level semantics during and after publishing to foster semantic search remains a research challenge. Semantically addresses this barrier by extending Schema.org, a community-agreed and research engine endorsed guideline for publishing structured content on the web. gosemantically.com

Biografia do Autor

Steve Fonin Mbouadeu, St. John's University

I’m an undergraduate computer science student at St. John’s University. My research interests are currently in the semantic web space, advancing techniques to make information more FAIR on the web.

Asim Abbas, St. John's University

My name is Asim Abbas born in Pakistan in 1994. Currently I am working as a research scholar in St.Johns University, USA. I have completed my B.S. degree in computer science from Islamia University Peshawar, Pakistan in 2015. I have completed my master's at Ubiquitous Computing Laboratory, Department of Computer Science and Engineering at Kyung Hee University, South Korea. My research interest includes Deep Learning, Machine Learning, Nature Language Processing, Text Mining, Health Informatics.

Syed Ahmad Chan Bukhari, St. John's University

Dr. Bukhari is as an Assistant Professor and Director of Healthcare Informatics at St. John's University, New York. He received his Ph.D. in Computer Science from the University of New Brunswick, Canada and then went on to complete his postdoctoral fellowship at Yale School of Medicine where he worked with Stanford University- Center of Expanded Data Annotation and Retrieval (CEDAR) to develop FAIR (Findable, Accessible, Interoperable and Reusable) data submission pipelines to improve scientific experimental reproducibility. His current research efforts are concentrated on addressing several core problems in the area of healthcare informatics and data science. He particularly focuses on devising techniques to semantically confederate heterogeneous biomedical data and to further develop clinical predictive models for diseases prediction. These techniques further alleviate many data access-related challenges faced by healthcare providers. Dr. Bukhari is a senior IEEE member and a distinguished ACM speaker who serves as an editorial board member of multiple scientific journals. His research work has published in top-tier journals and picked by various scientific blogs and international media.

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Publicado

2022-05-04

Como Citar

Mbouadeu, S. F., Abbas, A., Keshtkar, F., Wajahat, I., & Bukhari, S. A. C. (2022). A Sociotechnical Framework for Semantic Biomedical Content Authoring and Publishing. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130665

Edição

Seção

Special Track: Semantic, Logics, Information Extraction and AI