Iskanje po katalogu digitalne knjižnice

Opis: Entity alignment refers to the process of discovering entities representing the same object in different knowledge graphs (KG). Recently, some studies have learned other information about entities, but they are aspect-level simple information associations, and thus only rough entity representations can be obtained, and the advantage of multi-faceted information is lost. In this paper, a novel node-level information strong fusion framework (SFEA) is proposed, based on four aspects: structure, attribute, relation and names. The attribute information and name information are learned first, then structure information is learned based on these two aspects of information through graph convolutional network (GCN), the alignment signals from attribute and name are already carried at the beginning of the learning structure. In the process of continuous propagation of multi-hop neighborhoods, the effect of strong fusion of structure, attribute and name information is achieved and the more meticulous entity representations are obtained. Additionally, through the continuous interaction between sub-alignment tasks, the effect of entity alignment is enhanced. An iterative framework is designed to improve performance while reducing the impact on pre-aligned seed pairs. Furthermore, extensive experiments demonstrate that the model improves the accuracy of entity alignment and significantly outperforms 13 previous state-of-the-art methods.
Ključne besede: knowledge graph, entity ealignment, graph convolutional network, knowledge fusion
Objavljeno v DKUM: 27.03.2025; Ogledov: 0; Prenosov: 5
Celotno besedilo (3,40 MB)
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Opis: Knowledge Graphs (KGs) describe objective facts in the form of RDF triples, each triple contains sufficient semantic information and triple structure information. Knowledge Graph Completion (KGC) is to acquire new knowledge by predicting hidden relationships between entities and adding the new knowledge to the KG. At present, the mainstream KGC approaches only applied the triple structure information or only utilized the semantic information of the text. This paper proposes an approach (TSTR) using BERT and deep neural networks to fully extract the semantic information of knowledge, and designs an aggregated re-ranking scheme that incorporates existing graph embedding approach to learn the structural information of triples. In experiments, the approach achieves state-of-the-art performance on three benchmark datasets, and outperforms recent KGC approaches on sparsely connected datasets.
Ključne besede: knowledge graph completion, BERT, deep convolutional architecture, re-ranking
Objavljeno v DKUM: 19.02.2024; Ogledov: 229; Prenosov: 21
Celotno besedilo (1,03 MB)
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