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Entity alignment aims to discover and link the same entity objects that point to the real word in different knowledge graphs. Entity alignment based on GCN usually acts on undirected graphs of the single relation type, which is easy to result in the problem of inconsistent embedding corresponding results to entity leaning. Therefore, this paper proposed an entity alignment model based on two-way GCN and CVm. It realized the complete representation of the entity by splitting the asymmetric adjacency weight matrix to construct a two-way GCN, so that the model could be learned the forward and backward hidden features of the entity. At the same time, for selecting the most representative entity local semantic information, it used the CVm to weight attributes, which effectively improved the accuracy of entity alignment. By verifying the model on two large real heterogeneous datasets, the Hit@ 1 value of this method was 4% higher than the existing embedding-based entity alignment method on average and maintains a high average reciprocal rank. It proves that this method improves the entity alignment effect to a certain extent. [ABSTRACT FROM AUTHOR]

In order to solve the issue of malicious phonily occupying seats for some ticket agents or passengers in the process of air ticket sales, this paper proposed a blockchain system scheme based on the improved PoA consensus mechanism. The scheme used the alliance blockchain as a communication platform among airlines, adopted credit points and dynamic grading to build a new PoA consensus mechanism, protected passengers ' private information with hash algorithm, and achieved digital storage of ID information, registration of passengers, storage and sharing of passenger booking information for airline corporations relying on smart contracts. The prototype system trial operation shows that the system can timely detect and effectively stop the phonily occupying seats behavior. After analysis and evaluation, the program has the characteristics of privacy protection, decentralization and high security, and provides a new reference to solve the long-standing problem of phonily occupying seats in the civil aviation field. [ABSTRACT FROM AUTHOR]

Knowledge representation learning aims to represent the entities and relationships in the knowledge graph as low-dimen sional dense real-valued vectors, which can effectively alleviate the data sparsity of the knowledge graph and significantly improve the calc ulation efficiency. However, most existing knowledge representation learning methods only treat entities as an integral part of triples, and do not consider the characteristics of entities themselves, such as entity simila rity . In order to strengthen the sema ntic expression of embedded vectors, this pap er proposed a representation learning method SimE based on entity similarity. The method first used the structural domain of the entity to measure the similarity of the entity, and then comb ined the simila rity of the entity and the Laplace feature map as a constraint of the representation learning method based on the fact of triples to form a j oint represe ntation. Experime ntal results show that the method is close to the best method c urrently in tasks such as link prediction and triple classification. [ABSTRACT FROM AUTHOR]