Government Big Data Management and Intelligent Services
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Article List
2023, 12(1):17-25.
DOI: 10.12146/j.issn.2095-3135.20220711001
Abstract:
Government data sharing is an important foundation for promoting the construction of digital government and realizing the modernization of government’s governance system and capacity. In the past, the research on government data sharing focused on the establishment of data sharing management mechanism and the construction of technical support capacity of the platform, while the key to achieving efficient allocation of data value lies in the quality of supplied data and the guarantee of the sharing services. This paper summarizes the current operation mode of government data sharing service, and points out that the mode of “physical convergence and unified service” needs to pay attention to the possible security risks of high concentration of data, and there are some problems in the data sharing operation under the decentralized service mode, such as the demand of each department cannot be met quickly, the efficiency of data sharing is low, and the quality of data sharing service is poor. On this basis, a “joint operation” mode is proposed, which is jointly constructed by the data providers and the platform operators. This mode requires the data provider to be responsible for ensuring the quality of the data source, the platform operator to be responsible for the labeling and service capability of the data service, and both parties to jointly optimize the authorization mechanism and ensure the quality of shared services.
Government data sharing is an important foundation for promoting the construction of digital government and realizing the modernization of government’s governance system and capacity. In the past, the research on government data sharing focused on the establishment of data sharing management mechanism and the construction of technical support capacity of the platform, while the key to achieving efficient allocation of data value lies in the quality of supplied data and the guarantee of the sharing services. This paper summarizes the current operation mode of government data sharing service, and points out that the mode of “physical convergence and unified service” needs to pay attention to the possible security risks of high concentration of data, and there are some problems in the data sharing operation under the decentralized service mode, such as the demand of each department cannot be met quickly, the efficiency of data sharing is low, and the quality of data sharing service is poor. On this basis, a “joint operation” mode is proposed, which is jointly constructed by the data providers and the platform operators. This mode requires the data provider to be responsible for ensuring the quality of the data source, the platform operator to be responsible for the labeling and service capability of the data service, and both parties to jointly optimize the authorization mechanism and ensure the quality of shared services.
2023, 12(1):4-16.
DOI: 10.12146/j.issn.2095-3135.20220826001
Abstract:
Under the “Internet ＋” environment, the integration of government big data with public services and social sensor data has transformed the urban management model from single to comprehensive, the urban service system from isolation to sharing, and the urban decision-making model from mechanical to intelligent, which is the development trend of government service. In order to solve the problems of insufficient integration of government affairs data, public service data and social sensing data, insufficient sharing, low utilization efficiency and lack of technical platforms, the overall framework of the “Internet ＋” government affairs big data intelligent service platform is proposed. This paper designs the platform construction theory, system architecture, technical architecture, core technology and service application, etc., and realizes the transparent access of the “Internet ＋” government big data intelligent service platform, city information unit, transparent intelligent agent, transparent management and microservices, etc. Exploratory applications have been carried out on platforms such as Digital Guangdong and Dandong Social Security in Liaoning, showing that the platform can effectively support the efficient sharing, management and utilization of government affairs data, public service data and social sensor data, and improve department service effectiveness and capability. The overall design scheme of the platform proposed can provide valuable reference for provincial and municipal governments to carry out the construction of “Internet ＋” government big data intelligent service.
Under the “Internet ＋” environment, the integration of government big data with public services and social sensor data has transformed the urban management model from single to comprehensive, the urban service system from isolation to sharing, and the urban decision-making model from mechanical to intelligent, which is the development trend of government service. In order to solve the problems of insufficient integration of government affairs data, public service data and social sensing data, insufficient sharing, low utilization efficiency and lack of technical platforms, the overall framework of the “Internet ＋” government affairs big data intelligent service platform is proposed. This paper designs the platform construction theory, system architecture, technical architecture, core technology and service application, etc., and realizes the transparent access of the “Internet ＋” government big data intelligent service platform, city information unit, transparent intelligent agent, transparent management and microservices, etc. Exploratory applications have been carried out on platforms such as Digital Guangdong and Dandong Social Security in Liaoning, showing that the platform can effectively support the efficient sharing, management and utilization of government affairs data, public service data and social sensor data, and improve department service effectiveness and capability. The overall design scheme of the platform proposed can provide valuable reference for provincial and municipal governments to carry out the construction of “Internet ＋” government big data intelligent service.
2023, 12(1):79-90.
DOI: 10.12146/j.issn.2095-3135.20220909001
Abstract:
The current method of dividing commuting groups takes less into account the time continuity characteristics of commuting trips. Based on the one-week subway card swipe data in Shanghai, this paper constructs a work-life recognition model for commuters, defines a commuting trip time similarity calculation method, and then extracts the features to classify commuter groups hierarchically, and uses the hot spot analysis model to perform spatial analysis and visual expression for spatial analysis and visualization, and explores the spatiotemporal regularity of commuters and the spatial distribution characteristics of work-housing organization characteristics in Shanghai. The results show that: (1) The employment single center model is obvious, and the employment hotspots of different clusters are distributed in the city center, and the settlements are characterized by the spatial organization of “hot in the west and cold in the east”. (2) The mainstream commute hours are 7:00—8:30 and 17:00—19:00, with nearly half of the commuters commuting during the morning rush hours and 90% leaving the work places before 19:30. (3) The travel time characteristics of the different commuting types are generally consistent with the distribution of their work and housing hotspots. The proposed research method reveals that the travel time law of commuters has a strong correlation with the spatial distribution of work-housing hotspots, which provides reference information for urban operation management and urban planning.
The current method of dividing commuting groups takes less into account the time continuity characteristics of commuting trips. Based on the one-week subway card swipe data in Shanghai, this paper constructs a work-life recognition model for commuters, defines a commuting trip time similarity calculation method, and then extracts the features to classify commuter groups hierarchically, and uses the hot spot analysis model to perform spatial analysis and visual expression for spatial analysis and visualization, and explores the spatiotemporal regularity of commuters and the spatial distribution characteristics of work-housing organization characteristics in Shanghai. The results show that: (1) The employment single center model is obvious, and the employment hotspots of different clusters are distributed in the city center, and the settlements are characterized by the spatial organization of “hot in the west and cold in the east”. (2) The mainstream commute hours are 7:00—8:30 and 17:00—19:00, with nearly half of the commuters commuting during the morning rush hours and 90% leaving the work places before 19:30. (3) The travel time characteristics of the different commuting types are generally consistent with the distribution of their work and housing hotspots. The proposed research method reveals that the travel time law of commuters has a strong correlation with the spatial distribution of work-housing hotspots, which provides reference information for urban operation management and urban planning.
2023, 12(1):42-55.
DOI: 10.12146/j.issn.2095-3135.20220715004
Abstract:
In order to recommend matters related to government services and improve user efficiency and government service level, a recommendation algorithm is proposed, that is, a collaborative filtering recommendation method for government services combined with user characteristics. Unlike traditional collaborative filtering which does not consider user attributes, this method combines user portrait technology with it. First, the method establishes a user portrait of government services, and then uses the singular value metric analysis method to integrate the user portrait and the user-based collaborative filtering algorithm, so that the feature attributes can participate in the similarity calculation, improve the similarity between users, and solve the problem of data sparsity. To make the results more practical, the method calculates the predicted government service score, and recommends the TOP-N with the highest score to the user. In the experimental part, the actual data of the government affairs service of a city’s enterprise legal person is used for verification. The results show that the algorithm can meet the personalized requirements of the government affairs service recommendations and improve the prediction accuracy.
In order to recommend matters related to government services and improve user efficiency and government service level, a recommendation algorithm is proposed, that is, a collaborative filtering recommendation method for government services combined with user characteristics. Unlike traditional collaborative filtering which does not consider user attributes, this method combines user portrait technology with it. First, the method establishes a user portrait of government services, and then uses the singular value metric analysis method to integrate the user portrait and the user-based collaborative filtering algorithm, so that the feature attributes can participate in the similarity calculation, improve the similarity between users, and solve the problem of data sparsity. To make the results more practical, the method calculates the predicted government service score, and recommends the TOP-N with the highest score to the user. In the experimental part, the actual data of the government affairs service of a city’s enterprise legal person is used for verification. The results show that the algorithm can meet the personalized requirements of the government affairs service recommendations and improve the prediction accuracy.
2023, 12(1):26-41.
DOI: 10.12146/j.issn.2095-3135.20220730001
Abstract:
Internet＋government affairs big data has the characteristics of cross-domain, multi-protocol and difficult convergence. In the process of big data collection and aggregation, there is a need for a variety of protocol conversions, which require a gateway to realize a unified protocol adaptation conversion and provide the data support for multi-source heterogeneous data aggregation and data fusion. Traditional protocol conversion methods are usually designed for specific protocol conversion requirements, and their scalability is poor, so they are not suitable for the situation where there are multiple protocol conversion requirements. By studying and analyzing the structure of protocol messages and the characteristics of protocol conversion, this paper proposes a method to construct the knowledge graph of the protocol conversion. By constructing the schema layer and instance layer of the graph, a knowledge graph of protocol conversion containing the structure of protocol messages and the mapping relationship between message fields is established. On this basis, a protocol conversion method based on the knowledge graph is proposed to realize the packet conversion between different protocols. The effectiveness of the proposed method is verified by an application example of protocol conversion and a comparison experiment with the existing protocol conversion methods.
Internet＋government affairs big data has the characteristics of cross-domain, multi-protocol and difficult convergence. In the process of big data collection and aggregation, there is a need for a variety of protocol conversions, which require a gateway to realize a unified protocol adaptation conversion and provide the data support for multi-source heterogeneous data aggregation and data fusion. Traditional protocol conversion methods are usually designed for specific protocol conversion requirements, and their scalability is poor, so they are not suitable for the situation where there are multiple protocol conversion requirements. By studying and analyzing the structure of protocol messages and the characteristics of protocol conversion, this paper proposes a method to construct the knowledge graph of the protocol conversion. By constructing the schema layer and instance layer of the graph, a knowledge graph of protocol conversion containing the structure of protocol messages and the mapping relationship between message fields is established. On this basis, a protocol conversion method based on the knowledge graph is proposed to realize the packet conversion between different protocols. The effectiveness of the proposed method is verified by an application example of protocol conversion and a comparison experiment with the existing protocol conversion methods.
2023, 12(1):91-104.
DOI: 10.12146/j.issn.2095-3135.20220712001
Abstract:
The traditional person re-identification methods are difficult to independently cope with the complex and diverse recognition tasks in the security scenario of smart city in practice. In order to meet the needs of multi-level person re-identification, the deep integration of person re-identification and multi-level urban information units is proposed. Existing models and attentions for person re-identification tasks only focus on learning the robust features while neglecting the difference between features of pairs. Diff attention module is proposed to guide the network to learn a more discriminative attention map based on the difference of feature vectors. Taking the diff attention module, diff attention framework which matches many backbone models is developed. Two training strategies: joint training and separate training are proposed. Compared with other person re-identification methods, these framework and strategies have achieved excellent performance on Market-1501, CUHK03, and MSMT17 datasets.
The traditional person re-identification methods are difficult to independently cope with the complex and diverse recognition tasks in the security scenario of smart city in practice. In order to meet the needs of multi-level person re-identification, the deep integration of person re-identification and multi-level urban information units is proposed. Existing models and attentions for person re-identification tasks only focus on learning the robust features while neglecting the difference between features of pairs. Diff attention module is proposed to guide the network to learn a more discriminative attention map based on the difference of feature vectors. Taking the diff attention module, diff attention framework which matches many backbone models is developed. Two training strategies: joint training and separate training are proposed. Compared with other person re-identification methods, these framework and strategies have achieved excellent performance on Market-1501, CUHK03, and MSMT17 datasets.
2023, 12(1):68-78.
DOI: 10.12146/j.issn.2095-3135.20220901001
Abstract:
The large-scale population mobility between cities reflects the correlation between cities to a certain extent. This paper selects the national population migration data during the Spring Festival from 2015 to 2018 in the Tencent migration database and uses Gephi software to conduct visualization analysis to obtain the strength of population connections between the cities as well as the evolutionary characteristics of the overall pattern of inter-connectivity strength. The DBSCAN algorithm is used to cluster the time series of cities across the country, and the commonalities and differences in urban mobility at different time nodes during the Spring Festival are discussed. And the distance attenuation effects of intra-provincial and inter-provincial population flow are analyzed from the improved gravity model. The results show that: (1) The national cities have formed a stable “diamond” structure as a whole, Beijing, Shanghai, Xi’an, and other cities have formed a single-axis spatial organization model, Guangdong and Shenzhen have formed a multi-axis spatial organization model, and the first-tier cities have strong centrality. In addition to cities with a “diamond” structure, cities in the northwest region and cities in the northeast region have strong advantages in terms of transit and acceptance capabilities. (2) The changing trend of net migration population during the Spring Festival was relatively stable, and a symmetrical inflow and outflow were formed before and after the Spring Festival. (3) The flow during the Spring Festival travel period conforms to the law of distance attenuation. With the increase in distance, the attractiveness of the city gradually decreases.
The large-scale population mobility between cities reflects the correlation between cities to a certain extent. This paper selects the national population migration data during the Spring Festival from 2015 to 2018 in the Tencent migration database and uses Gephi software to conduct visualization analysis to obtain the strength of population connections between the cities as well as the evolutionary characteristics of the overall pattern of inter-connectivity strength. The DBSCAN algorithm is used to cluster the time series of cities across the country, and the commonalities and differences in urban mobility at different time nodes during the Spring Festival are discussed. And the distance attenuation effects of intra-provincial and inter-provincial population flow are analyzed from the improved gravity model. The results show that: (1) The national cities have formed a stable “diamond” structure as a whole, Beijing, Shanghai, Xi’an, and other cities have formed a single-axis spatial organization model, Guangdong and Shenzhen have formed a multi-axis spatial organization model, and the first-tier cities have strong centrality. In addition to cities with a “diamond” structure, cities in the northwest region and cities in the northeast region have strong advantages in terms of transit and acceptance capabilities. (2) The changing trend of net migration population during the Spring Festival was relatively stable, and a symmetrical inflow and outflow were formed before and after the Spring Festival. (3) The flow during the Spring Festival travel period conforms to the law of distance attenuation. With the increase in distance, the attractiveness of the city gradually decreases.
2023, 12(1):56-67.
DOI: 10.12146/j.issn.2095-3135.20220715002
Abstract:
Methods such as BERT and the combination of neural network model have been gradually applied to the acquisition of disaster information. However, such methods have many problems, such as large number of parameters, inconsistent data sets and fine-tuning data sets, and local instability. In this paper, an information recognition model based on MacBERT and adversarial training is proposed. The model obtains the initial vector representation through MacBERT pre-training model, and then adds some perturbations to generate adversarial samples. Then input to the bi-directional long short-term memory and conditional random field in turn, which not only reduces the pre-training times and fine-tuning stage differences, but also improves the robustness of the model. The experimental results show that the information recognition model based on MacBERT and adversarial training are improved the accuracy rate and F1 value on the microblog dataset and the 1998 People’s Daily dataset, and the execution is excellent than other models, which indicates that the model has certain feasibility for urban waterlogging information recognition.
Methods such as BERT and the combination of neural network model have been gradually applied to the acquisition of disaster information. However, such methods have many problems, such as large number of parameters, inconsistent data sets and fine-tuning data sets, and local instability. In this paper, an information recognition model based on MacBERT and adversarial training is proposed. The model obtains the initial vector representation through MacBERT pre-training model, and then adds some perturbations to generate adversarial samples. Then input to the bi-directional long short-term memory and conditional random field in turn, which not only reduces the pre-training times and fine-tuning stage differences, but also improves the robustness of the model. The experimental results show that the information recognition model based on MacBERT and adversarial training are improved the accuracy rate and F1 value on the microblog dataset and the 1998 People’s Daily dataset, and the execution is excellent than other models, which indicates that the model has certain feasibility for urban waterlogging information recognition.
2023, 12(3):1-18.
DOI: 10.12146/j.issn.2095-3135.20221205001
Abstract:
In recent years, the vigorous development of “Internet ＋ Government Services” has promoted the deep integration of the Internet and the government public service system, and the construction of digital government has achieved initial results. How to achieve standardized, efficient, safe management, sharing and application of Internet ＋ big data in government affairs, and promote the solution of the “Three Integrations and Five Crossings Issues”, is the key to building a higher quality digital government and promoting the modernization of national governance system and capacity. This paper first summarizes the development of government data management at home and abroad. Secondly, it expounds the collection technology, storage technology, fusion technology, computing technology, application technology and security technology in the full life cycle of Internet ＋ government big data management, and summarizes the evolution of mainstream data management technology. Finally, this paper discusses some urgent problems in Internet ＋ government data management, and the development trend of Internet ＋ government data management technologies.
In recent years, the vigorous development of “Internet ＋ Government Services” has promoted the deep integration of the Internet and the government public service system, and the construction of digital government has achieved initial results. How to achieve standardized, efficient, safe management, sharing and application of Internet ＋ big data in government affairs, and promote the solution of the “Three Integrations and Five Crossings Issues”, is the key to building a higher quality digital government and promoting the modernization of national governance system and capacity. This paper first summarizes the development of government data management at home and abroad. Secondly, it expounds the collection technology, storage technology, fusion technology, computing technology, application technology and security technology in the full life cycle of Internet ＋ government big data management, and summarizes the evolution of mainstream data management technology. Finally, this paper discusses some urgent problems in Internet ＋ government data management, and the development trend of Internet ＋ government data management technologies.
2023, 12(3):19-33.
DOI: 10.12146/j.issn.2095-3135.20221004001
Abstract:
Cross-domain collaboration of government services is a new governance model, which has been spawned by the combination of digital transformation of government and cross-domain governance. This model is aimed at achieving the value goal of governance of government services. However, due to the different specific business and functions of each government department, each department has an independent data management system, and each information system has diverse storage, complex data formats and different business processes. As a result, sharing and utilizing the heterogeneous data between departments in a safe and reliable way has become a challenging research problem. Traditional government data sharing usually adopts a centralized sharing mode, which is prone to a series of issues such as data privacy leakage, departmental authority problems, and single point of failure. To address this issue, this paper proposes a government data sharing scheme that combines attribute-based encryption and blockchain. Firstly, an access control policy is formulated by the data owner to restrict the attributes of data requesters. Subsequently, fine-grained access control as well as key update in secure data sharing is achieved by using subset overlay technology, which is combined with linear secret sharing to achieve complete hiding of the access policy. The inter planetary file system distributed network is used to store the ciphertext after symmetric encryption to relieve the storage pressure of the blockchain system. Finally, the hash of the retrieved data ciphertext is re-encrypted using the Keccak algorithm to achieve data integrity verification. Security analysis and experimental analysis show that the proposed scheme can meet the requirements of secure sharing of government data in terms of security and efficiency, and thus realize the secure and traceable sharing of government data.
Cross-domain collaboration of government services is a new governance model, which has been spawned by the combination of digital transformation of government and cross-domain governance. This model is aimed at achieving the value goal of governance of government services. However, due to the different specific business and functions of each government department, each department has an independent data management system, and each information system has diverse storage, complex data formats and different business processes. As a result, sharing and utilizing the heterogeneous data between departments in a safe and reliable way has become a challenging research problem. Traditional government data sharing usually adopts a centralized sharing mode, which is prone to a series of issues such as data privacy leakage, departmental authority problems, and single point of failure. To address this issue, this paper proposes a government data sharing scheme that combines attribute-based encryption and blockchain. Firstly, an access control policy is formulated by the data owner to restrict the attributes of data requesters. Subsequently, fine-grained access control as well as key update in secure data sharing is achieved by using subset overlay technology, which is combined with linear secret sharing to achieve complete hiding of the access policy. The inter planetary file system distributed network is used to store the ciphertext after symmetric encryption to relieve the storage pressure of the blockchain system. Finally, the hash of the retrieved data ciphertext is re-encrypted using the Keccak algorithm to achieve data integrity verification. Security analysis and experimental analysis show that the proposed scheme can meet the requirements of secure sharing of government data in terms of security and efficiency, and thus realize the secure and traceable sharing of government data.
2023, 12(3):34-47.
DOI: 10.12146/j.issn.2095-3135.20220921001
Abstract:
The construction of smart cities can effectively improve urban governance and operation capacity and break the urban development dilemma. To explore how to provide intelligent services for urban management through spatio-temporal big data in the physical-digital space intersection, this paper proposes a transparent fusion framework for multi-source spatio-temporal big data based on the analysis of the semantic relationships of multi-source, multi-dimensional, and heterogeneous spatio-temporal big data.To achieve this goal, a concept of “city information unit” is further proposed as the basis for building the data organization of physical-digital spatial integration. In particular, the multi-source, multi-dimensional,and heterogeneous spatio-temporal big data are first actively aggregated, semantically resolved, and then the geographic knowledge is constructed spatiotemporally; based on the unique data code, the data information is mapped to the city information unit; Next, in this paper, data matching model and association model are found, and a transparent data fusion framework is constructed. Combined with multi-source heterogeneous data element matching technology, a transparent spatio-temporal data fusion rule base is constructed. Finally, with the support of various fusion methods, the transparent fusion of urban entity and spatio-temporal multi-source spatio-temporal data is realized. With the help of urban information unit and data coding, we realize the dynamic integration system of urban entity and spatio-temporal big data, so as to provide users with intelligent information services.
The construction of smart cities can effectively improve urban governance and operation capacity and break the urban development dilemma. To explore how to provide intelligent services for urban management through spatio-temporal big data in the physical-digital space intersection, this paper proposes a transparent fusion framework for multi-source spatio-temporal big data based on the analysis of the semantic relationships of multi-source, multi-dimensional, and heterogeneous spatio-temporal big data.To achieve this goal, a concept of “city information unit” is further proposed as the basis for building the data organization of physical-digital spatial integration. In particular, the multi-source, multi-dimensional,and heterogeneous spatio-temporal big data are first actively aggregated, semantically resolved, and then the geographic knowledge is constructed spatiotemporally; based on the unique data code, the data information is mapped to the city information unit; Next, in this paper, data matching model and association model are found, and a transparent data fusion framework is constructed. Combined with multi-source heterogeneous data element matching technology, a transparent spatio-temporal data fusion rule base is constructed. Finally, with the support of various fusion methods, the transparent fusion of urban entity and spatio-temporal multi-source spatio-temporal data is realized. With the help of urban information unit and data coding, we realize the dynamic integration system of urban entity and spatio-temporal big data, so as to provide users with intelligent information services.
2023, 12(3):48-60.
DOI: 10.12146/j.issn.2095-3135.20221026001
Abstract:
Government data resources are characterized by a wide range of sources, diverse types, large data volumes and unclear data distribution, which can lead to lacks of unified management, high efficiency in data access and use, and ability to continuously promote the data release value. In order to solve the above problems and realize the correlative fusion of data from different sources and different types, this paper adopts the fusion of multi-source heterogeneous data to provide technical means for the exchange and sharing of government data. This paper proposes the concept of information channel and channel coupling, and provides a multisource heterogeneous information channel coupling method, which includes three module for constructing the Initial channel coupling knowledge graph, realizing the channel data coupling based on the channel coupling knowledge graph, and realizing the knowledge updating based on the channel coupling knowledge graph, respectively. In the proposed method, the technologies of atlas construction, knowledge extraction, knowledge fusion, knowledge processing and knowledge updating are integrated together, and a top-down sorting retrieval model is proposed to accelerate the speed and accuracy of knowledge fusion and data retrieval.
Government data resources are characterized by a wide range of sources, diverse types, large data volumes and unclear data distribution, which can lead to lacks of unified management, high efficiency in data access and use, and ability to continuously promote the data release value. In order to solve the above problems and realize the correlative fusion of data from different sources and different types, this paper adopts the fusion of multi-source heterogeneous data to provide technical means for the exchange and sharing of government data. This paper proposes the concept of information channel and channel coupling, and provides a multisource heterogeneous information channel coupling method, which includes three module for constructing the Initial channel coupling knowledge graph, realizing the channel data coupling based on the channel coupling knowledge graph, and realizing the knowledge updating based on the channel coupling knowledge graph, respectively. In the proposed method, the technologies of atlas construction, knowledge extraction, knowledge fusion, knowledge processing and knowledge updating are integrated together, and a top-down sorting retrieval model is proposed to accelerate the speed and accuracy of knowledge fusion and data retrieval.
2023, 12(3):61-71.
DOI: 10.12146/j.issn.2095-3135.20221025001
Abstract:
With the continuous improvement of digital construction of government affairs, the need for realizing “knowledge-based, personalized and intelligent” government services based on knowledge graph is gradually being awakened. At present, the application of knowledge graph in the government domain is often oriented to a single scenario, making it difficult to establish connections between different scenarios with respect to government knowledge. The search, management and approval efficiency of government services based on traditional databases is still not high. To expand the scope of government services and improve the efficiency of search, management, examination and approval, this paper proposes a set of top-down mapping methods to construct a multi-layer government knowledge graph. Specifically, this method first construct the conceptual model of government knowledge from the perspective of government service, and then obtain the government knowledge, data preprocessing and knowledge fusion according to the conceptual model; finally, forming a multi-layer government knowledge graph with top-down relationships of concept, business service, social service and information sharing. With the visual display of Neo4j and the deployed services, paper validate the proposed method by taking the search of real estate approval, the examination and approval of occupied forest land and the social service for public complaints as examples, which proves that the method is efficient and feasible. It not only provides knowledge graph support and association for different government scenarios, but also helps to realize the fusion and sharing of multi-source government data, thus providing a reference for the subsequent construction of government knowledge graph.
With the continuous improvement of digital construction of government affairs, the need for realizing “knowledge-based, personalized and intelligent” government services based on knowledge graph is gradually being awakened. At present, the application of knowledge graph in the government domain is often oriented to a single scenario, making it difficult to establish connections between different scenarios with respect to government knowledge. The search, management and approval efficiency of government services based on traditional databases is still not high. To expand the scope of government services and improve the efficiency of search, management, examination and approval, this paper proposes a set of top-down mapping methods to construct a multi-layer government knowledge graph. Specifically, this method first construct the conceptual model of government knowledge from the perspective of government service, and then obtain the government knowledge, data preprocessing and knowledge fusion according to the conceptual model; finally, forming a multi-layer government knowledge graph with top-down relationships of concept, business service, social service and information sharing. With the visual display of Neo4j and the deployed services, paper validate the proposed method by taking the search of real estate approval, the examination and approval of occupied forest land and the social service for public complaints as examples, which proves that the method is efficient and feasible. It not only provides knowledge graph support and association for different government scenarios, but also helps to realize the fusion and sharing of multi-source government data, thus providing a reference for the subsequent construction of government knowledge graph.
