KGHC: a knowledge graph for hepatocellular carcinoma
- PDF / 1,791,065 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 5 Downloads / 194 Views
RESEARCH
Open Access
KGHC: a knowledge graph for hepatocellular carcinoma Nan Li1, Zhihao Yang1*, Ling Luo1, Lei Wang2*, Yin Zhang2, Hongfei Lin1 and Jian Wang1 From 5th China Health Information Processing Conference Guangzhou, China. 22-24 November 2019
Abstract Background: Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing biomedical resources will provide support to the research on the hepatocellular carcinoma. To this purpose, we constructed a knowledge graph for Hepatocellular Carcinoma (KGHC). Methods: We propose an approach to build a knowledge graph for hepatocellular carcinoma. Specifically, we first extracted knowledge from structured data and unstructured data. Since the extracted entities may contain some noise, we applied a biomedical information extraction system, named BioIE, to filter the data in KGHC. Then we introduced a fusion method which is used to fuse the extracted data. Finally, we stored the data into the Neo4j which can help researchers analyze the network of hepatocellular carcinoma. Results: KGHC contains 13,296 triples and provides the knowledge of hepatocellular carcinoma for healthcare professionals, making them free of digging into a large amount of biomedical literatures. This could hopefully improve the efficiency of researches on the hepatocellular carcinoma. KGHC is accessible free for academic research purpose at http://202.118.75.18:18895/browser/. Conclusions: In this paper, we present a knowledge graph associated with hepatocellular carcinoma, which is constructed with vast amounts of structured and unstructured data. The evaluation results show that the data in KGHC is of high quality. Keywords: Hepatocellular carcinoma, Information extraction, Knowledge graph
Background Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. It accounts for 45% of the world’s deaths and is the most common cause of death in people with cirrhosis [1]. Although the prevention, diagnosis and treatment techniques have been progress, the morbidity and mortality are still on the rise * Correspondence: [email protected]; [email protected] 1 College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China 2 Beijing Institute of Health Administration and Medical Information, Beijing 100850, China
[2, 3]. Therefore, hepatocellular carcinoma has become a hot topic in life science researches and there is a growing trend of using the medical knowledge from the open field. At present, biomedical database is the main source of biomedical information. The majority of biomedical databases are manually extracted and curated by human experts from literatures. Since the amount of biomedical literatures is increasing rapidly, it is difficult for interaction database curators to detect and curate the information efficiently. Therefore, biomedical knowledge usually canno
Data Loading...
