Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study

• Published in: Journal of medical Internet research 2023-04, Vol.25 (1), p.e45268-e45268
• Main Authors: Costello, Jeremy, Kaur, Manpreet, Reformat, Marek Z, Bolduc, Francois V
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Automation; Caregivers; Community support; COVID-19; Data mining; Economic impact; Experts; Extraction; False information; Graphs; Humans; Knowledge; Knowledge Bases; Language; Libraries; Machine learning; Medical personnel; Methods; Mobilization; Natural Language Processing; Neurodevelopmental Disorders; Original Paper; Pattern Recognition, Automated; Semantics; Support groups; Terminology; Thyroid diseases; Vocabulary; Websites
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/45268

Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such group is individuals with neurodevelopmental disorders (NDDs) and their families. NDDs affect up to 18% of the population and have major social and economic impacts. The current limitations in communicating information for individuals with NDDs include the absence of shared terminology and the lack of efficient labeling processes for web resources. Because of these limitations, health professionals, support groups, and families are unable to share, combine, and access resources. We aimed to develop a natural language-based pipeline to label resources by leveraging standard and free-text vocabularies obtained through text analysis, and then represent those resources as a weighted knowledge graph. Using a combination of experts and service/organization databases, we created a data set of web resources for NDDs. Text from these websites was scraped and collected into a corpus of textual data on NDDs. This corpus was used to construct a knowledge graph suitable for use by both experts and nonexperts. Named entity recognition, topic modeling, document classification, and location detection were used to extract knowledge from the corpus. We developed a resource annotation pipeline using diverse natural language processing algorithms to annotate web resources and stored them in a structured knowledge graph. The graph contained 78,181 annotations obtained from the combination of standard terminologies and a free-text vocabulary obtained using topic modeling. An application of the constructed knowledge graph is a resource search interface using the ordered weighted averaging operator to rank resources based on a user query. We developed an automated labeling pipeline for web resources on NDDs. This work showcases how artificial intelligence-based methods, such as natural language processing and knowledge graphs for information representation, can enhance knowledge extraction and mobilization, and could be used in other fields of medicine.