随着电子文件元数据标准的不断颁布,元数据标准的实施日益重要。本文根据电子文件元数据形成和积累的规律,基于ISO23081的最佳实践,结合杭州市电子文件中心建设的实例,围绕电子文件管理系统(Electronic Records Management System,ERMS...随着电子文件元数据标准的不断颁布,元数据标准的实施日益重要。本文根据电子文件元数据形成和积累的规律,基于ISO23081的最佳实践,结合杭州市电子文件中心建设的实例,围绕电子文件管理系统(Electronic Records Management System,ERMS)实施过程中的元数据方案设计展开探讨,着重讨论了文件管理元数据实体、实体级次及其元数据的确定等问题。展开更多
By network security threat intelligence analysis based on a security knowledge graph(SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is d...By network security threat intelligence analysis based on a security knowledge graph(SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template(FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.展开更多
文摘随着电子文件元数据标准的不断颁布,元数据标准的实施日益重要。本文根据电子文件元数据形成和积累的规律,基于ISO23081的最佳实践,结合杭州市电子文件中心建设的实例,围绕电子文件管理系统(Electronic Records Management System,ERMS)实施过程中的元数据方案设计展开探讨,着重讨论了文件管理元数据实体、实体级次及其元数据的确定等问题。
基金the National Natural Science Foundation of China (No. 61802081)the Guizhou Provincial Natural Science Foundation, China (No. 20161052)+2 种基金the Guizhou Provincial Public Big Data Key Laboratory Open Project, China (No. 2017BDKFJJ024)the Guizhou University Doctoral Fund, China (No. 201526)the Major Scientific and Technological Special Project of Guizhou Province, China (No. 20183001).
文摘By network security threat intelligence analysis based on a security knowledge graph(SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template(FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.
