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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Learning temporal granularity with quadruplet networks for temporal knowledge graph completion.

Rushan Geng1, Cuicui Luo2

  • 1School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.

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|May 16, 2025
PubMed
Summary
This summary is machine-generated.

Learning Temporal Granularity with Quadruplet Networks (LTGQ) enhances temporal knowledge graph completion by embedding elements into specialized spaces. This approach improves accuracy by capturing finer-grained temporal semantics and interactions.

Keywords:
Dynamic convolutional neural networksTemporal knowledge graphTemporal knowledge graph completionTimestamps mappingTriaffine

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Computer Science

Background:

  • Temporal Knowledge Graphs (TKGs) model dynamic real-world facts with evolving states.
  • The temporal dimension adds complexity to knowledge graph completion tasks.
  • Temporal granularity enhances fact representation precision.

Purpose of the Study:

  • To propose a novel method, Learning Temporal Granularity with Quadruplet Networks (LTGQ), for improving temporal knowledge graph completion.
  • To address the heterogeneity of TKGs by differentiating embeddings for entities, relations, and timestamps.
  • To enable a finer-grained capture of semantic information within temporal knowledge graphs.

Main Methods:

  • LTGQ embeds entities, relations, and timestamps into distinct specialized spaces.
  • Utilizes triaffine transformations to model high-order interactions within quadruples (entities, relations, timestamps).
  • Employs Dynamic Convolutional Neural Networks (DCNNs) to extract latent space representations across different temporal granularities.

Main Results:

  • LTGQ achieves more robust alignment between facts and their temporal contexts.
  • Demonstrates significant improvements in temporal knowledge graph completion accuracy.
  • Validated effectiveness across five public datasets.

Conclusions:

  • The proposed LTGQ model effectively enhances temporal knowledge graph completion.
  • The method's ability to capture fine-grained temporal semantics and interactions is crucial.
  • LTGQ offers a promising approach for handling dynamic and evolving knowledge representation.