Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding

Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs) have emerged as crucial tools in diverse Knowledge Graphs (KGs), seeks to identify equivalent entity pairs fields, including information retrieval [48], question answering across these graphs. Most existing approaches regard EA as a graph [1, 4], recommendation systems [6, 38], and natural language processing representation learning task, concentrating on enhancing graph [12]. Despite their growing relevance, KGs are hindered encoders. However, the decoding process in EA - essential for effective by coverage limitations, which diminish their utility in various operation and alignment accuracy - has received limited applications. A core challenge in leveraging heterogeneous KGs lies attention and remains tailored to specific datasets and model architectures, in Entity Alignment (EA) - the process of identifying analogous necessitating both entity and additional explicit relation entities across different KGs. EA typically unfolds in two phases: embeddings. This specificity limits its applicability, particularly encoding and decoding (Figure 1). Current EA methods heavily rely in GNN-based models. To address this gap, we introduce a novel, on seed alignments for supervised learning of entity representations, generalized, and efficient decoding approach for EA, relying solely thereby encoding KGs into a unified embedding space and on entity embeddings.