Sep 12, 2024 · Knowledge graph (KG) entity alignment is the task of identifying corresponding entities across different KGs. Existing alignment techniques ...
This work addresses the problem of aligning incomplete KGs with representation learning and develops a missing links detector that discovers and recovers ...
Dec 17, 2021 · In this work, we address the problem of aligning incomplete KGs with representation learning. Our KG embedding framework exploits two feature ...
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Here we provide collections for incomplete graph representation learning literature. Year 2022. [IEEE TPAMI 2022] Learning on Attribute-Missing Graphs [paper| ...
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Abstract: Network alignment and network completion are two fundamental cornerstones behind many high-impact graph mining applications.
Node co-occurrence based graph neural networks for knowledge graph link prediction ... Link-Intensive Alignment for Incomplete Knowledge Graphs. V Van Tong, TT ...
First, the potential missing links between the entities are proposed by selecting the ones with high-correlated embeddings, then the correct relation between ...
Missing: Intensive | Show results with:Intensive
Multi-order graph convolutional networks for knowledge graph alignment. NT ... Link-Intensive Alignment for Incomplete Knowledge Graphs. V Van Tong, TT ...
Jun 6, 2024 · In this paper, we leverage a small set of labeled samples and a large-scale corpus to efficiently construct domain-specific knowledge graphs by an LLM.