Abstract: Network big data refers to the massive data generated via interaction and fusion of the ternary human-machine-thing universe in the cyberspace and available on the Internet. It has a few typical features, such as multi-sourced, heterogeneous, interactive, bursty, and noisy. It contains mainly unstructured data, and has strong real-timeness. Network big data implicitly contains tremendous highly-interconnected knowledge. Building up open Web oriented large-scale knowledge bases is an effective means for obtaining rich knowledge from network big data. This paper compares both the domestic and international mainstream open Web knowledge bases. We specifically analyze the core techniques and methods for constructing open Web knowledge bases, fusing multi-sourced knowledge, and updating the knowledge bases. Furthermore, we summarize the research status and main issues of open Web knowledge base based information search, data mining, and system applications from different aspects, including user intension understanding, query extension, semantic Q&A, clue mining, relationship referencing, and prediction of relationships and attributes. Finally, we look into the development trends and main challenges of open Web knowledge bases.