Abstract
Non-suicidal Self-Injury (NSSI) refers to the intentional destruction of one’s own body tissue without suicidal intent and for purposes not socially sanctioned. Although many scholars have done a lot of research on NSSI, and there exist large literature on the research of NSSI. But there still lacks a comprehensive picture on the etiology of NSSI. Knowledge Graphs have become an important AI approach to integrating various types of complex knowledge and data resources. We have constructed Knowledge Graphs of NSSI. It integrates a wide range of knowledge resources related to NSSI, including metadata of medical literature, and their semantic annotations with well-known medical terminologies/ontologies such as SNOMED CT and UMLS. It provides a basic integration foundation of knowledge and data concerning NSSI for a comprehensive analysis. In this paper, we will show that Knowledge Graphs are useful for integrating multiple medical knowledge sources, and how Knowledge Graphs can be used for exploring the etiology of NSSI and gain a comprehensive analysis on the targeted problems.
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Huang, Z. et al. (2023). Exploring Etiology of Nonsuicidal Self-injury by Using Knowledge Graph Approach. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_4
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DOI: https://doi.org/10.1007/978-981-99-7108-4_4
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