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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph.

Zhihua Yan1,2, Xijin Tang1,3

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 China.

Journal of Systems Science and Systems Engineering
|May 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a narrative graph approach to generate storylines from news documents, improving event evolution understanding. The method uses event-centric knowledge graphs and outperforms existing techniques for analyzing real-world events.

Keywords:
Storylinecommunity detectionevent evolutionevent-centric knowledge graph

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Online media generate vast unstructured news, complicating event evolution tracking.
  • Existing storyline generation methods often overlook event arguments and inter-event relationships.
  • Current event-centric knowledge graphs struggle to represent complex event evolution adequately.

Purpose of the Study:

  • To develop a novel method for generating storylines from news documents using an event-centric knowledge graph.
  • To represent complex event evolution in a structured, temporally ordered format called a narrative graph.
  • To enhance the understanding of societal events and their progression.

Main Methods:

  • News documents are collected and an event ontology is constructed.
  • An event-centric knowledge graph with temporal relations is built using graph neural networks and BERT fine-tuning.
  • A narrative graph generation framework with coherence and coverage constraints is proposed.

Main Results:

  • The proposed narrative graph approach effectively represents complex event evolution.
  • The method demonstrates superior performance compared to baseline approaches in storyline generation.
  • A case study validates the utility of narrative graphs for real-world event analysis.

Conclusions:

  • The narrative graph framework offers a significant advancement in processing and understanding news narratives.
  • This approach facilitates a more comprehensive perception of major societal events and their temporal dynamics.
  • The study highlights the potential of event-centric knowledge graphs for structured event representation and analysis.