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Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

With the establishment and rapid development of the China (Shanghai) Pilot Free Trade Zone (FTZ), the scale of enterprises in the zone has grown rapidly. This paper takes the actual needs of the Shanghai FTZ as the background, extracts key information such as entities from the Big Data related to Internet enterprises, further constructs the enterprise knowledge graph, and applies it to the supervision and service of the FTZ. The enterprise knowledge graph is constructed using the Neo4j graph database. To verify the effectiveness of the named entity identification and relationship extraction methods proposed in this paper, experiments were conducted to validate them, and both achieved good results. [ABSTRACT FROM AUTHOR]

With the development of digital technology, Memory Projects, such as the American Memory and the Venice Time Machine, have been explored continually as typical digital humanity projects. In these projects, humanists are able to look into the thousand-year-history with a macro view by a 'data telescope', as well as numerous micro views by 'data microscopes'. This article investigates representative historical memory and city memory projects from over the past 20 years and analyses the purpose, investigation, cooperation, methods, implementation, and technical routes adopted. The following problems are apparent: resource decentralization and fragmentation, duplication of database construction, single-user service mode and low utilization rate of cultural heritage resources, etc. This study provides an overall technical route to solve these problems by examining ongoing practice. The Shanghai Memory Project, undertaken by Shanghai Library, uses knowledge reorganization and data reconciling technologies such as Big Data, Semantic Web, and Knowledge Graph to construct an overarching Humanities Research Environment, which is co-constructing, sharing, and interconnecting to avoid problems of resource decentralization and database duplication. The Project provides a user service model, which combines the library creating knowledge with user-generated content. Implementing data visualization technologies, such as AR, VR, etc. the project improves the collection display and promotes the utilization of the resources. The Urban Memory Project is a comprehensive program that relies on cooperation and interdisciplinary collaboration, co-construction, and the sharing of data. Meanwhile, an increasing number of Urban Memory Projects play a significant role in helping to develop the Digital Humanities' infrastructure. [ABSTRACT FROM AUTHOR]