PEJL: A path-enhanced joint learning approach for knowledge graph completion

Knowledge graphs (KGs) often suffer from incompleteness. Knowledge graph completion (KGC) is proposed to complete missing components in a KG. Most KGC methods focus on direct relations and fail to leverage rich semantic information in multi-hop paths. In contrast, path-based embedding methods can capture path information and utilize extra semantics to improve KGC. However, most path-based methods cannot take advantage of full multi-hop information and neglect to capture multiple semantic associations between single and multi-hop triples. To bridge the gap, we propose a novel path-enhanced joint learning approach called PEJL for KGC. Rather than learning multi-hop representations, PEJL can recover multi-hop embeddings by encoding full multi-hop components. Meanwhile, PEJL extends the definition of translation energy functions and generates new semantic representations for each multi-hop component, which is rarely considered in path-based methods. Specifically, we first use the path constraint resource allocation (PCRA) algorithm to extract multi-hop triples. Then we use an embedding recovering module consisting of a bidirectional gated recurrent unit (GRU) layer and a fully connected layer to obtain multi-hop embeddings. Next, we employ a KG modeling module to leverage various semantic information and model the whole knowledge graph based on translation methods. Finally, we define a joint learning approach to train our proposed PEJL. We evaluate our model on two KGC datasets: FB15K-237 and NELL-995. Experiments show the effectiveness and superiority of PEJL

Population decline, infrastructure and sustainability

Japan has experienced population decline since 2010 and the situation is expected to become more severe after 2030 with forecasts indicating an expected 30% decline from 2005 to 2055. Many other developed countries such as Germany and Korea are also experiencing depopulation. These demographic changes are expected to affect society at many levels such as labour markets decline, increased tax burden to sustain pension systems, and economic stagnation. Little is known however about the impacts of population decline on man-made physical infrastructure, such as possible deterioration of current infrastructure or increased financial burden of sustaining it. Infrastructure can be classified into 3 categories: point-type (e.g. buildings), point-network type (e.g. water supply) and network type (e.g. road). The impact of depopulation may vary according to the type of infrastructure. Previous research in this area has been limited in scope (e.g. case studies conducted in a single city focusing on a single type of infrastructure) and method (e.g. most research in the topic has been qualitative). This thesis presents a new comprehensive study on the impacts of population decline on infrastructure in Japan, taking into account all types of infrastructure and using a quantitative approach. Data collection methods include interviews and two large scale questionnaire surveys, the first conducted with municipalities and the second, a stated preference survey, conducted with members of the public. The goal of sustainable development is relevant even in a depopulated society, and hence a sustainable development framework is applied to the analysis where social, economic, environmental and engineering impacts are investigated. The main findings indicate that some infrastructure impacts observed and reported in depopulated areas do not seem to be related to any population decline; moreover, the preferences of citizens for infrastructure development is very similar between depopulated areas and non-depopulated areas. The results also suggest that the premises of Barro’s overlapping generations model, very relevant to a discussion of intergenerational decision making and related sustainability, appear to be rejected in this context