A retrospective of knowledge graphs

Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for ma- chines, and even for humans. Knowledge graphs have be- come prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and fi- nally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via log- ical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph sys- tems and discuss the future research directions.

Extraction of palaeochannel information from remote sensing imagery in the east of Chaohu Lake, China

Palaeochannels are deposits of unconsolidated sediments or semi-consolidated sedimentary rocks depos- ited in ancient, currently inactive river and stream channel systems. It is distinct from the overbank deposits of currently active river channels, including ephemeral water courses which do not regularly flow. We have introduced a spectral characteristics-based palaeochannel information extraction model from SPOT-5 imagery with special time phase, which has been built by virtue of an analysis of remote sensing mechanism and spectral characteristics of the palaeochannel, combined with its distinction from the spatial distribution and spectral features of currently active river channels, also with the establishment of remote sensing judging features of the palaeochannel in remote sensing image. This model follows the process of supervised classification → farmland masking and primary component analysis→ underground palaeochan- nel information extraction → information combination→ palaeochannel system image. The Zhegao River Valley in the east of Chaohu Lake was selected as a study area, and SPOT-5 imagery was used as a source of data. The result was satisfactory when this method has been successfully applied to extract the palaeochannel informa- tion, which can provide good reference for regional remote sensing archeology and neotectonic research. However, the applicability of this method needs to be tested further in other areas as the spatial characteristics and spectral response of palaeochannel might be different.