Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy.

Soil degradation in low soil humus content karst areas is a serious problem. Humus is composed of a series of polymer organic compounds, with no fixed form, therefore it is difficult to study, especially humin. In this study, 13C CP/MAS NMR was used to study the humic acids (HA), fulvic acids (FA), and humin (HM) components in the soil profiles of carbonate rocks and argillaceous rocks in the Northern Guizhou region. Through the vertical distribution of humus in soil, the transformation mechanism among functional groups of humus was studied. The content of HA and FA in the soil of Zunyi New Area was low, and the humification degree was low. FA was the main HA with simple molecules, which were directly related to the surface vegetation in the area. There may have been some genetic relationship between Aliphatic C and Aromatic C, Aliphatic C and Carboxyl C in the same group of humus. In the phylogenetic relationship of HA, FA, and HM, more transformations existed between HA and FA, and between HA and HM, while the transformations between FA and HM were very rare. This study provides an important scientific basis for the theory of the formation and transformation of soil humus in karst area.

Soil physical erosion and chemical weathering quantification at the slope scale are important to reveal the material cycle of the ecosystem in the karst region, because of the high heterogeneity due to the complex habitats. The Zr-based mass balance method has been widely used to quantify long-term physical erosion and chemical weathering at the slope scale, but the method is still in the exploratory research stage for quantifying short-term physical erosion and chemical weathering. In this paper, sloping fields (nine sampling sites and two sloping fields) in Zunyi, within the Guizhou karst region, were studied. We attempted to quantify the short-term physical erosion and chemical weathering rates by ¹³⁷Cs combined with the Zr-based mass balance method, and an ideal distribution curve of the Zr concentration in the soil surface layer of a karst region is proposed. The results showed the following: (1) The average soil erosion rate on the slope of the study area is 580 t/(km²·a), which is equivalent to 14% of the average value of the Wujiang River basin in which it is located. This shows that the spatial distribution of soil erosion in this area varies significantly. (2) The Zr concentration in the erosion profile (EUC (1)) corresponds to 48% of that in the deposition profile (DUC (3)). This indicates that physical erosion in the study area survives chemical weathering, which is also consistent with the relationship model hypothesis. In addition, the vertical distribution characteristics of Zr concentration in all profiles are basically consistent with the ideal hypothesis curve. (3) The chemical weathering rate of the topsoil has been preliminarily estimated to be around 30 t/(km²·a), and the ratio between the physical soil erosion and chemical weathering is 20:1. The results show that the physical erosion is dominant over the chemical weathering of topsoil, and the chemical weathering rate was proportional to the physical erosion. This study provides a new method for quantifying short-term soil erosion and weathering erosion at the slope scale in karst regions, which is important for regional ecological restoration and sustainable development.

Geospatial knowledge in massive academic papers can provide knowledge services such as location-based research hotspot analysis, spatio-temporal data aggregation, research results recommendation, etc. However, geospatial knowledge often exists implicitly in literature resources in unstructured form, which is difficult to be directly accessed and mined and utilized for rapid production of massive thematic maps. In this paper, we take the geospatial knowledge of the area studied as an example and introduce its extraction method in detail. An integrated feature template matching and random forest classification algorithm is proposed for accurately identifying research areas from the abstract texts of academic papers and producing thematic maps. Firstly, the precise recognition of geographical names is achieved step by step based on BiLSTM-CRF algorithm and improved heuristic disambiguation method; then, the area studied is extracted by the designed integrated feature recognition template of area studied using random forest classification algorithm, and a fast thematic map is designed for the knowledge of area studied, topic and literature. The experimental results show that the area studied recognition accuracy can reach 97%, the F-value is 96%, and the recall rate reaches 96%, achieving high accuracy and high efficiency of area studied extraction in text. Based on the geospatial knowledge, the thematic map can achieve the effect of fast map formation and accurate expression.