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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Lehmann, Jens; Su, Fenglong; Xu, Chengjin. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.709/ Abstract: Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). A bunch of embedding-based approaches have been proposed for entity alignment, which embed entities and relations of different KGs into a vector space and measure the similarities between entity embeddings. Recently, the availability of temporal KGs (TKGs) that contain time information created the need for reasoning over time in such TKGs. Existing embedding-based entity alignment approaches disregard time information that commonly exists in many large-scale KGs, leaving much room for improvement. In this paper, we focus on the task of aligning entity pairs between TKGs and propose a novel Time-aware Entity Alignment approach based on Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of different KGs into a vector space and use GNNs to learn entity representations. To incorporate both relation and time ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/tqxe-dh70 https://underline.io/lecture/37361-time-aware-graph-neural-network-for-entity-alignment-between-temporal-knowledge-graphs BASE Hide details

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