Entity alignment helps discover and link entities from different knowledge graphs (KGs) that refer to the same real-world entity, making it a critical technique for KG fusion. Most entity alignment methods are based on knowledge representation learning, which uses a mapping function to project entities from different KGs into a unified vector space and align them based on calculated similarities. However, this process requires sufficient pre-aligned entity pairs. To address this problem, this study proposes an entity alignment method based on joint learning of entity and attribute representations. Structural embeddings are learned using the triples modeling method based on TransE and PTransE and extracted from the embedding vector space utilizing semantic information from direct and multi-step relation paths. Simultaneously, attribute character embeddings are learned using the N-gram-based compositional function to encode a character sequence for the attribute values, followed by TransE to model attribute triples in the embedding vector space to obtain attribute character embedding vectors. By learning the structural and attribute character embeddings simultaneously, the structural embeddings of entities from different KGs can be transferred into a unified vector space. Lastly, the similarities in the structural embedding of different entities were calculated to perform entity alignment. The experimental results showed that the proposed method performed well on the DBP15K and DWK100K datasets, and it outperformed currently available entity alignment methods by 16.8, 27.5, and 24.0% in precision, recall, and F1 measure, respectively.
Obtaining larger category-label-containing training signal datasets in non-cooperative scenarios is difficult. Moreover, employing smaller labeled signal datasets for specific emitter identification is technically challenging. Therefore, we propose a novel method for few-shot SEI. We first design a bispectral analysis and Radon transformation-based signal preprocessing scheme to obtain feature vectors that effectively characterize the radio frequency fingerprints. The feature vectors are then fed to a network model for feature learning. Moreover, a meta-learning algorithm is applied to the network model to adapt to few-shot feature learning. The conventional meta-learning algorithm is improved to develop a novel algorithm involving latent embedding optimization for meta-learning. The proposed method extracts lowdimensional key features from high-dimensional input data and evaluates the distance and degree of feature dispersion. The resulting information is employed in sample point prediction. The algorithm effectively achieves few-shot SEI and offers stable and efficient recognition after training with a minimum of forty samples. This method identifies emitter individuals under multiple modulation types and exhibits scalability in identifying the emitter numbers. Moreover, it offers adaptability in identifying the emitter individuals under multiple propagation channel types.
This study focused on the construction of a spatiotemporal knowledge graph for ship activities. First, a ship activity ontology model was proposed to describe the entities and relations of ship activities. Then, maritime event text data were utilized as the ship activity dataset, where entities and relations were extracted to form triplets. Thus, the data layer was populated, completing the construction of the ship activity spatiotemporal knowledge graph. The process of extracting triplets involved initially inputting the text sentences into the Bidirectional Encoder Representations from Transformers (BERT) model for pretraining to obtain vector representations of characters. These representations were then fed into a lattice long short-term memory network (Lattice-LSTM) for further processing. The resulting hidden vectors h1,h2,⋯,hn were input into the conditional random field (CRF) to perform named entity recognition. The recognized entities were then labeled in the original sentences and input into another BERT-Lattice-LSTM network. The resulting hidden vectors h′1,h′2,⋯,h′n were fed into a relation classifier, which output the relation between the two labeled entities, completing the extraction of entity–relation triplets. In experiments, the proposed method achieved triplet extraction performance exceeding 90% for three different evaluation metrics: Precision, Recall, and F1-measure.
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