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Summary
This summary is machine-generated.

This study introduces a hybrid Knowledge Graph (KG) builder that unifies relation extraction and KG completion. The framework effectively generates dense KGs, excelling with rare relations in both text and visual domains.

Keywords:
inductive logic programminginformation extractionknowledge graphsrelation learningrelation prediction

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Area of Science:

  • Artificial Intelligence
  • Data Science

Background:

  • Generating structured Knowledge Graphs (KGs) is crucial for decision-making and information augmentation.
  • Current methods often treat relation extraction and KG completion as separate phases.

Purpose of the Study:

  • To propose a unified framework for building Knowledge Graphs (KGs) from scratch.
  • To combine neural relation extraction with differentiable inductive logic programming (ILP) for KG completion.

Main Methods:

  • A hybrid KG builder integrating a neural relation extractor and a differentiable ILP model.
  • Iterative KG completion using the ILP component to enhance the graph structure.
  • Evaluation across both textual and visual domains.

Main Results:

  • Achieved comparable performance on standard relation extraction datasets (Wikidata, Visual Genome).
  • Demonstrated superior performance over neural baselines in reasoning out dense KGs.
  • Showcased particular effectiveness in handling rare relations.

Conclusions:

  • The proposed hybrid KG builder offers a unified and effective approach to KG generation.
  • The framework shows promise for improving KG density and handling complex relational data.
  • This method advances the state-of-the-art in automated Knowledge Graph construction.