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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 3/2020

Open Access 01-07-2020 | Hepatocellular Carcinoma | Research

KGHC: a knowledge graph for hepatocellular carcinoma

Authors: Nan Li, Zhihao Yang, Ling Luo, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang

Published in: BMC Medical Informatics and Decision Making | Special Issue 3/2020

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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.
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Metadata
Title
KGHC: a knowledge graph for hepatocellular carcinoma
Authors
Nan Li
Zhihao Yang
Ling Luo
Lei Wang
Yin Zhang
Hongfei Lin
Jian Wang
Publication date
01-07-2020
Publisher
BioMed Central
DOI
https://doi.org/10.1186/s12911-020-1112-5

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