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Published on: September 30, 2021
KGHC: a knowledge graph for hepatocellular carcinoma
Nan Li1, Zhihao Yang2, Ling Luo1
1College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
Insights
Researchers developed a knowledge graph for Hepatocellular Carcinoma (KGHC) by extracting and filtering data from various sources. This KGHC resource aids healthcare professionals in hepatocellular carcinoma research.
Area of Science:
- Biomedical Informatics
- Oncology
- Data Science
Background:
- Hepatocellular carcinoma (HCC) is a prevalent malignancy with high mortality.
- Effective mining of medical knowledge from extensive text data is crucial for HCC research.
- Integrating diverse biomedical information supports advancements in understanding HCC.
Purpose of the Study:
- To construct a comprehensive knowledge graph for Hepatocellular Carcinoma (KGHC).
- To facilitate easier access to and analysis of HCC-related medical knowledge.
- To support researchers and healthcare professionals in their work on hepatocellular carcinoma.
Main Methods:
- Knowledge extraction from both structured and unstructured data sources.
- Application of the BioIE biomedical information extraction system for data filtering.
- Implementation of a data fusion method and storage in Neo4j for network analysis.
Main Results:
- The constructed KGHC contains 13,296 triples.
- Provides readily accessible knowledge on hepatocellular carcinoma, reducing literature review time.
- KGHC is available for academic research at http://202.118.75.18:18895/browser/.
Conclusions:
- A novel knowledge graph (KGHC) for hepatocellular carcinoma has been developed.
- The graph integrates vast amounts of structured and unstructured data.
- Evaluation confirms the high quality of the data within KGHC.
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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