Indices recommendation is a long-standing topic in stock market investment. Predicting the future trends of indices and ranking them based on the prediction results is the main scheme for indices recommendation. How to improve the forecasting performance is the central issue of this study. Inspired by the widely used trend-following investing strategy in financial investment, the indices' future trends are related to not only the nearby transaction data but also the long-term historical data. This article proposes the MSGraph, which tries to improve the index ranking performance by modeling the correlations of short and long-term historical embeddings with the graph attention network. The original minute-level transaction data is first synthesized into a series of K-line sequences with varying time scales. Each K-line sequence is input into a long short-term memory network (LSTM) to get the sequence embedding. Then, the embeddings for all indices with the same scale are fed into a graph convolutional network to achieve index aggregation. All the aggregated embeddings for the same index are input into a graph attention network to fuse the scale interactions. Finally, a fully connected network produces the index return ratio for the next day, and the recommended indices are obtained through ranking. In total, 60 indices in the Chinese stock market are selected as experimental data. The mean reciprocal rank, precision, accuracy and investment return ratio are used as evaluation metrics. The comparison results show that our method achieves state-of-the-art results in all evaluation metrics, and the ablation study also demonstrates that the combination of multiple scale K-lines facilitates the indices recommendation.

Entity alignment, as a fundamental technique in knowledge graph integration, aims to identify the equivalent entity pairs that refer to the same real-world entity across different knowledge graphs. In domain-specific knowledge graphs, based on the atomic divisibility characteristics of entity categories, these categories can be classified into atomically divisible and atomically indivisible types. However, traditional methods for entity alignment in atomically indivisible knowledge graphs often fall short when dealing with tasks that involve more than one atomically divisible entity category. To address such a challenge, we propose a novel multi-embedding entity alignment approach based on matching information interaction over atomically divisible entities (MiAD). MiAD effectively exploits the matching information interaction between low-level entities and high-level entities to enrich the semantic representation of high-level entities and to enhance the reliability of the alignment results. The high-level entities are atomically divisible, and the matching result among the low-level entities can provide a significant hint for the entity alignment of high-level entities by analyzing their inherent interrelationships. To tackle the insufficient utilization of these matching information interactions, we propose a multiple hierarchical embedding strategy, which consists of a structural semantic aggregator, an attribute semantic aggregator, a corroborative aggregator, and a joint aggregator. This method leverages both the intra-level interactions and cross-level interactions to generate the hierarchical embeddings of high-level and low-level entities, respectively, from multiple perspectives (i.e., relations, attributes, and corroborative textural descriptions). As a result, MiAD significantly improves the accuracy of alignment outcomes and strengthens the overall structural and semantic understanding of higher-level entities. Comprehensive experimental results demonstrate that MiAD achieves substantial performance improvements compared with seven baseline methods on the Chinese recipe dataset Ta-da.