Abstract
Based on the integration of multi-source data, an approach of domain-specific knowledge graph construction is proposed to guide the construction of a “people-centered” poverty alleviation knowledge graph, and to achieve cross-functional and cross-regional sharing and integration of national basic data resources and public services. Focusing on “precise governance and benefit people service”, poverty alleviation ontology is constructed to solve semantic heterogeneity in multiple data sources integration, and provide an upper data schema for poverty alleviation knowledge graph construction. Karma modeling is used to implement semantic mapping between ontology concepts and data, and integrate multi-source heterogeneous data into RDF data. The RDF2Neo4j interpreter is developed to parse RDF data and store RDF data schema based on the graph database Neo4j. Based on visualization technology and natural language processing technology, Poverty Alleviation Knowledge Graph Application System is designed to achieve knowledge graph query and knowledge question answering function, which improved the application value of government data.
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Acknowledgment
This work was supported by National Key Research &Development Program of China (No. 2016YFB1001103).
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Yun, H., He, Y., Lin, L., Pan, Z., Zhang, X. (2019). Construction Research and Application of Poverty Alleviation Knowledge Graph. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_42
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DOI: https://doi.org/10.1007/978-3-030-30952-7_42
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