Abstract:

Various kinds of largescale datasets spring up in the big data era, which makes it a hot topic for organizing the knowledge embedded in datasets to provide more abundant and powerful knowledge services. In this paper, we explore the features of the knowledge in largescale datasets, such as content diversity, networkalike structure, unstructured data sources and ongoing evolution, and investigate the important issues of knowledge organization from a computational perspective. We assume that knowledge organization methodology should meet the requirements of computational knowledge services which focus on the utilization, discovery and increment of knowledge. The keypoint here is that the organization and management of knowledge information is substantially affected by the ongoing knowledge evolution. Consequently, we point out that the knowledge organization should be built upon proper semantic data management. Furthermore, the key issues of knowledge organization for largescale datasets have been discussed. The relevant theoretical topics, such as semantic data modeling, knowledge representation and knowledge computation are explored to address the urgent problems and the possible emerging trends of related fields are also mentioned.

Key words: Knowledge organization, Big data, Semantic data modeling, Knowledge representation, Knowledge computation, Knowledge evolution