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Updated: Oct 25, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System.

Kai He1,2,3, Lixia Yao4, JiaWei Zhang1,2,3

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Journal of Medical Internet Research
|August 4, 2021
PubMed
Summary
This summary is machine-generated.

Researchers built a system to extract genealogical information from online obituaries, creating knowledge graphs to enhance electronic health records for biomedical research.

Keywords:
EHRgenealogical knowledge graphgenealogyinformation extractionneural network

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Data Science

Background:

  • Genealogical information is crucial for biomedical research, including disease heritability and risk prediction.
  • Electronic health records (EHRs) and claims data are used to infer family relationships, but completeness can be limited.
  • Online obituaries offer a novel, rich data source for constructing comprehensive family trees.

Purpose of the Study:

  • To develop an end-to-end information extraction system for constructing Genealogical Knowledge Graphs (GKGs) from online obituaries.
  • To enrich EHR data with detailed genealogical information for advanced biomedical research.
  • To create a system that can assemble individual GKGs into larger, more comprehensive family structures.

Main Methods:

  • A corpus of 1700 online obituaries was curated, with data augmentation used to address data scarcity.
  • A multitask artificial neural network was developed to simultaneously detect names, extract relationships, and assign attributes (dates, residence, gender, age).
  • A predefined family relationship map with 4 entity types and 71 relationship types was utilized.
  • Related GKGs were assembled into larger graphs by identifying individuals present in multiple obituaries.

Main Results:

  • The system achieved high performance with precision (94.79%), recall (91.45%), and F1-score (93.09%) on 10-fold cross-validation.
  • A total of 12,407 GKGs were constructed, including one spanning 4 generations and 30 individuals.
  • The system demonstrated the potential for enriching EHR data with genealogical insights.

Conclusions:

  • Genealogical Knowledge Graphs derived from online obituaries can significantly enhance biomedical research capabilities.
  • The developed multitask deep neural system provides an effective method for constructing and assembling GKGs.
  • The study highlights the value of novel data sources for building comprehensive family relationship information, with shared code available for the scientific community.