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Smart War on COVID-19 and Global Pandemics
Published in Chhabi Rani Panigrahi, Bibudhendu Pati, Mamata Rath, Rajkumar Buyya, Computational Modeling and Data Analysis in COVID-19 Research, 2021
Anil D. Pathak, Debasis Saran, Sibani Mishra, Madapathi Hitesh, Sivaiah Bathula, Kisor K. Sahu
Deep neural networks are also deployed to search for antivirals against new targets. The search is not limited to the experimental drugs alone but also applied to the library of approved drugs. Gene expression profiles of the molecular perturbations which are closely related to SARS-CoV-2 such as coatomer protein complex (COPB2) mainly composed the search domain (Avchaciov et al. 2020). The biomedical knowledge graphs can be used to discover drugs for SARS-CoV-2 infection. Some of the well-known examples of knowledge graphs include Freebase (Bollacker et al. 2008), DBpedia (Bizer et al. 2009), Nell (Carlson et al. 2010), and YAGO (Hoffart et al. 2013). The biomedical knowledge graph is made up of a vast number of structured medical information such as diseases, genes, and drugs and with over 20 types of biomedical entities and their relationship extracted from the scientific literature by machine learning (Kamilaris et al. 2019; Sang et al. 2018). Benevolent AI’s knowledge graph is utilized to search for approved drugs that could be of help for COVID-19 (Richardson et al. 2020) as depicted in Figure 5.2. The author identified baricitinib as a potential treatment for COVID-19, which can be utilized to reduce the ability of the virus to infect lung cells. The use case of AI-based biomedical knowledge graph to search and identify the approved drugs that could help for COVID-19; the results show baricitinib as the potential treatment for 2019-nCoV (Richardson et al. 2020).
Artificial Intelligence for Biomedical Informatics
Published in Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Amrit Lal Sangal, Artificial Intelligence Technologies for Computational Biology, 2023
Shahid Azim, Samridhi Dev, Sushil Kumar, Aditi Sharan
A knowledge graph is a type of data modeling that uses a graph to describe related data, with nodes representing data entities and edges indicating relationships between them. Facts are stored in knowledge graphs as SPO (subject, predicate, and object) triples, with subjects and objects representing knowledge entities and predicates representing knowledge relations. Biomedical ontologies have become increasingly significant in recent years for characterizing existing biological information as knowledge graphs. Traditionally, graphs with interconnected biological components have been employed to depict complex biological systems.
Role of Knowledge Graphs in Analyzing Epidemics and Health Disasters
Published in Adarsh Garg, D. P. Goyal, Global Healthcare Disasters, 2023
The data are stored in the form of entities, relationship between the entities, and the attributes of entities as well as relations; therefore, sometimes it is also called network analysis. These graphs that are used here in graph analytics for storing, analyzing, and visualizing data are called knowledge graphs. A knowledge graph is a network of entities with some attributes and the relationships between them. For example, Figure 1.2 represents diseases and their symptoms and pathogens.
Integrated data-model-knowledge representation for natural resource entities
Published in International Journal of Digital Earth, 2022
Yulin Ding, Zhaowen Xu, Qing Zhu, Hankan Li, Yan Luo, Ying Bao, Lingjun Tang, Sen Zeng
To realize the efficient expression and management of the relationships among natural resource entities, this study modeled and expressed the relationships based on the prototype of the knowledge graph. A knowledge graph is a type of graph data structure that explicitly expresses the evolution process and structural relationships of knowledge resources and their carriers by nodes and relational edges. In order to efficiently express both the static and dynamic relationships among the same type of entities, as well as the relationships across different types of entities, this study proposes a spatiotemporal relationship graph based on a resource description framework (RDF) triple as a unit part of the knowledge graph, which can support spatiotemporal query and higher order inference. The detailed structure is shown in Figure 7.
An intelligent approach for mining knowledge graphs of online news
Published in International Journal of Computers and Applications, 2022
Kumar Abhishek, Vaibhav Pratihar, Shishir Kumar Shandilya, Sanju Tiwari, Vinay Kumar Ranjan, Sudhakar Tripathi
The previous phase resulted in a list of entities along with some relations defined over them. Now, the task remains to represent the data into a format where the fetched information can easily be queried and visualized. We take the aid of the Knowledge Graph as our knowledge base. A knowledge graph is a collection of interlinked entities where both the relations and entities boost the meaning of each other and provide room for the growth of knowledge. The construction of the knowledge graph is termed mining. During the mining of the knowledge graph, the following conversion takes place: Entities to NodesRelationships to Links
A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks
Published in Systems Science & Control Engineering, 2021
Yufei Zhang, Hongwei Wang, Xiang Zhai, Yanwei Zhao, Jing Guo
Knowledge representation models provide a structure for organizing knowledge elements. To support successful applications of design knowledge management, effective knowledge retrieval and reuse are also essential. As a new powerful technology, Knowledge graph (KG) integrates knowledge extraction, knowledge fusion (Bordes et al., 2013; Wang et al., 2014) knowledge reasoning (Chen et al., 2020), knowledge storage and reuse (Mathew, 2016) into a technical system. It is used to build a knowledge network through imitating the human thinking process and facilitating knowledge service using graphical storage. Since Google launched the knowledge graph project which was initially applied to improve results relevance (Yu et al., 2020), a considerable amount of work on KG has been conducted based on the data of WordNet, Wikidata and other open databases. For instance, based on the Wikidata dataset, (2019) researched entity disambiguation of short text. Wu wt al. (2020) presented a framework of knowledge graph construction, based on Baidu Baike, Hudong Baike and Chinese Wikipedia, to extract and link knowledge elements. Natural language process (NLP) makes it possible to automatically construct elements for knowledge graphs. KG has higher requirements on the quality of data, and thus has been successfully applied to areas such as biomedicine (Shen et al., 2019) and finance (Xue & Huang, 2017) with a large amount of structured data.