Special issue on “Multi‐modal information learning and analytics of cross‐media big data”

We are living in the era of data deluge. Meanwhile, the world of big data includes a rich and complex set of cross-media content, including text, images, video, audio, and graphics. To date, substantial research efforts have been dedicated to big data processing and cross-media mining, yielding good theoretical underpinnings and great practical success. However, studies that jointly consider cross-media big data analytics are relatively sparse (Zhang, Liu, Deng, Xu, and Choo (2018); Zhang, Wei, Wang, and Liao (2018)). This research gap needs more attention since it will benefit many real-world applications. Despite its significance, it is important to analyse cross-media big data due to the heterogeneity (Zhang, Zhao, Li, Chen, and Yuan (2015)), large-scale volume, increasing size, lack of structure, correlations, and noise. Multi-modal information learning (Demertzis, Iliadis, Avramidis, and El-Kassaby (2017)), which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved substantial progress in both academia and industry. Additionally, deep learning has been adopted in all kinds of computer vision applications, and many breakthroughs have been achieved in sub-areas, such as DeepFace in the LFW competition for face verification and GoogleNet for the ImageNet Competition for object categorization. We can expect that more and more computer vision applications will benefit from multi-modal information learning. This special issue focuses on learning methods to achieve high-performance multi-modal information analysis and understanding under uncontrolled environments on a large scale, which is also a very challenging problem. This problem has attracted considerable attention from both academia and industry. We hope this topic will aggregate top-level research on new advances in multi-modal information from cross-media data. The purpose of this SI is to provide a forum for researchers and practitioners to exchange ideas and progress in related areas. After several rounds of review, six papers have been accepted for publication. Zhu, Ma, and Liu (2018) combined status-striving motivation with opinion dynamics to study the decision mechanism and evolution law of employee knowledge sharing behaviour. A multiagent simulation method is used to design the decision-making model of employee knowledge sharing behaviour. The influence of the network structure, number of people, and the proportion of employees with different statuses on knowledge-sharing performance is considered. The simulation software is used to convert the theoretical model into a parametric experiment, and the sensitivity analysis is also employed. According to natural language processing and machine translation technology, to solve the problem, He (2018) aims to establish a parallel corpus for information extraction based on the OntoNotes corpus by combining automatic extraction and manual adjustment. To verify the validity of the parallel corpus constructed in this paper, a comparative experiment was carried out on the corpus. The corpus entity alignment rate, anaphora absence, and syntactic structure were analysed in detail based on statistics. To reduce the vagueness and subjectivity of customer demand in the process of product–service system design, a fuzzy semantic calculation method is proposed by Zhang (2018) to obtain the importance of service demand. In addition, according to the demand of the clustering of service modules, a new clustering method is proposed to analyse discrete data based on the improved K-means algorithm that is based on the Kruskal algorithm. Based on the actual situation of the knowledge flow during industrial transfer, a dynamic model of knowledge flow in a complex network is built by Zhou and Wu (2018). The mechanism of the knowledge flow in the network is analysed, and an analogue simulation is conducted using the Netlogo platform to identify the effects of the networkApostrophe;s average degree, the number of enterprises involved in the transfer, the network size, and the effective transmission rate of the knowledge on the efficiency of the knowledge flow in the network. The work presented by Yang (2018) proposes an ontology mapping approach, in which the ontology element name and structure are combined. It uses the approaches based on linguistics and distance to generate a variable weight semantic graph. On this graph, the similarity of element names and structure are calculated through iterative computation. In the process of iteration, similarity result values are constantly adjusted. The approach avoids the problem of single methods that cannot use the entire amount of ontology information; therefore, it provides a more ideal mapping result. As an important resource of the company, the customer plays a vital role in the process of knowledge sharing. Therefore, research on the mechanism of customersApostrophe; participation in knowledge sharing is especially important. In the work by Wang and Liu (2019), from the perspective of knowledge sharing between customers and enterprises, a customer participation model of a knowledge-sharing mechanism is built based on three aspects: subjective influencing factors of knowledge sharing, objective influencing factors of knowledge sharing, and environmental influencing factors of knowledge sharing. As the special issue editors, we would like to take this opportunity to thank the various authors for their papers and the reviewers for their work. We are also grateful to Jon Hall, Editor-in-Chief of the Wiley journal Expert Systems.