Drivers who feel social closeness with other drivers tend to drive more safely, according to previous research. Given this, we examine how communication in the driving context influences social closeness, drawing from theories of computer-mediated communication, a context with notable similarities to the road. A survey study found that social closeness with others is associated with greater communication comfort and identity expression through the car, but less treatment of the car as a social entity. An interview study provides context and caveats for these generalizations. Together, these studies present implications for near-future social-mobility services relating to safety on the road. 相似文献
In this work we present a multi-source uncertain entity resolution model and show its implementation in a use case of Yad Vashem, the central repository of Holocaust-era information. The Yad Vashem dataset is unique with respect to classic entity resolution, by virtue of being both massively multi-source and by requiring multi-level entity resolution. With today's abundance of information sources, this project motivates the use of multi-source resolution on a big-data scale. We instantiate the proposed model using the MFIBlocks entity resolution algorithm and a machine learning approach, based upon decision trees to transform soft clusters into ranked clustering of records, representing possible entities. An extensive empirical evaluation demonstrates the unique properties of this dataset that make it a good candidate for multi-source entity resolution. We conclude with proposing avenues for future research in this realm. 相似文献
Heterogeneous information networks, which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects, are ubiquitous in the real world. In this paper, we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network, and we propose a new method named DEM short for Deep Entity Matching. In contrast to the traditional entity matching methods, DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching. Importantly, we incorporate DEM with the network embedding methodology, enabling highly efficient computing in a vectorized manner. DEM’s generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly. To illustrate its functionality, we apply the DEM algorithm to two real-world entity matching applications: user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks. Extensive experiments on real-world datasets demonstrate DEM’s effectiveness and rationality.
