Enterprises with good long-term free cash flow data often have better prospects than enterprises with good net profit but unstable free cash flow for a long time, and free cash flow prediction is an important part of evaluating the enterprise value of an enterprise. By determining the fitness function, algorithm formula, population, and Backpropagation (BP) neural network design, a BP neural network model based on the improved genetic algorithm is proposed to predict the free cash flow of enterprises. Taking the free cash flow data of G Company from 1 January 2019 to 30 June 2019 as an example, after evaluating the most neurons and the best population, analyzing the relative errors and comparing the average relative errors of different prediction models, the results show that the model has better prediction accuracy. Cash flow forecasting can effectively improve decision making on productions and operations and the investment financing of enterprises, and has important practical significance for studying enterprise fund management.

Entity alignment is the most critical technology of knowledge fusion, which aims to identify and align entities that exist in different knowledge graphs (KGs) but represent the same real-world objects. Temporal knowledge graphs (TKGs) extend static triples into quadruples by introducing temporal information, which is more in line with the dynamic changes of the real world and is often used to enhance the performance of various applications. However, the existing entity alignment methods mainly focus on traditional KGs, ignoring the temporal information contained in TKGs may lead to misalignment between some similar entities. The latest method enhances the performance of entity alignment by learning temporal information embedding, but it does not make full use of the advantages of temporal information. In this paper, we propose a new Entity Alignment method for Cross-lingual TKG with BERT (EACTB). EACTB uses the BERT model of multi-language training to learn the semantic relevance of entity description. For temporal information, we propose a new and more efficient method to calculate the similarity of temporal information. EACTB uses graph convolution network (GCN) to embed structural information. In addition, iterative method is used to deal with the problem of insufficient training datasets. Experimental results on three cross-lingual TKGs datasets show that EACTB is significantly superior to existing methods.

In Semantic Web, modeling knowledge graph based on RDF becomes more and more popular. There is quite a lot of spatiotemporal information in Semantic Web, and recent works focus on not only general data but also spatiotemporal data. Existing efforts are mainly to add spatiotemporal labels to RDF, which expand RDF triple into quad or quintuple. However, extra labels often cause additional overhead for the system and lead to inefficient information organization management. In order to overcome this limitation, we propose an stRDFS model by labeling properties with spatiotemporal features and the corresponding determination methods of topological relations among different spatiotemporal entities. stRDFS considers spatiotemporal attribute as a part of the RDF model, which can record spatiotemporal information without changing the current RDF standard. Our approach improves the ability of recording and linking spatiotemporal data. More importantly, depending on formatting of spatiotemporal attributes in stRDFS, it will improve the semantic inferring ability, and the users are not required to be familiar with the underlying representations of spatiotemporal data.

A formal algebra is essential for applying standard database-style query optimization to XML queries. We develop such algebra for manipulating fuzzy spatiotemporal XML data. We also introduce several formal characterizations of fuzzy spatiotemporal algebraic equivalences, and study how XQuery expressions can be transformed into algebraic expressions. It shows that the algebra can lay a firm foundation for managing fuzzy spatiotemporal XML data.