Learning Context-based Embeddings for Knowledge Graph Completion

Purpose:Due to the incompleteness nature of knowledge graphs(KGs),the task of predicting missing links between entities becomes important.Many previous approaches are static,this posed a notable problem that all meanings of a polysemous entity share one embedding vector.This study aims to propose a polysemous embedding approach,named KG embedding under relational contexts(ContE for short),for missing link prediction.Design/methodology/approach:ContE models and infers different relationship patterns by considering the context of the relationship,which is implicit in the local neighborhood of the relationship.The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors,which represent the contextual information of the relationship.Then,according to the position of the entity,the entity’s polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship.Findings:ContE is a fully expressive,that is,given any ground truth over the triples,there are embedding assignments to entities and relations that can precisely separate the true triples from false ones.ContE is capable of modeling four connectivity patterns such as symmetry,antisymmetry,inversion and composition.Research limitations:ContE needs to do a grid search to find best parameters to get best performance in practice,which is a time-consuming task.Sometimes,it requires longer entity vectors to get better performance than some other models.Practical implications:ContE is a bilinear model,which is a quite simple model that could be applied to large-scale KGs.By considering contexts of relations,ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning,it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks.Originality/value:ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to.It decomposes a relation vector into two vectors,namely,forward impact vector and backward impact vector in order to capture the relational contexts.ContE has the same low computational complexity as TransE.Therefore,it provides a new approach for contextualized knowledge graph embedding.

Impact of high-speed railway construction on spatial relationships in the Guanzhong Plain urban agglomeration

The Guanzhong Plain urban agglomeration is a response to the Belt and Road Initiative in Northwest China that aims to promote regional development.The direct impact of high-speed railway construction is to shorten the spatial-temporal distance among regions,improve the accessibility of regional transportation,and promote socioeconomic linkages.From the perspective of accessibility,this study analyzes the impact of high-speed railway construction on the spatial pattems and county-level economic relationships of the Guanzhong Plain urban agglom-eration.The results show that the construction of high-speed railway significantly improves regional accessibility,increases the potential for urban economic development,and gradually narrows the gaps in economic potential among cities.The construction of high-speed railway has increased the intensity of extenal economic relations among numerous counties in the Guanzhong Plain urban agglomeration,and most of the areas with increased connections are located in the direction of routes extension.The development of the internal economic network of the Guanzhong Plain urban agglomeration is unbalanced,and a complex network is gradually emerging with a few large cities at the core,but the construction of high-speed railway is changing the struicture of the economic network.In general,a certain degree of intrinsic coupling exists between regional accessibility change and the evolution of economic relations caused by high-speed railway,reflecting the requirements of the regional overall development strategy.

User activities and the heteroseneity of urban space:The case of Dahiyat Al Hussein park

Urban researchers have maintained a constant interest in the complexity and continuity of urban space usage.Some have applied actor-network theory(ANT)to investigate the heterogeneity of spaces and present them through the networks of their users'activities.However,these accounts are predominantly limited in examining the extent to which these spaces may be heterogeneous when exploring such networks.This paper draws on recent ANT scholarship,which employs an ethnographic research conducted in a main park in a housing project at Dahiyat Al Hussein in Amman,Jordan.The findings describe the complex and unpredictable negotiations that occur with in spaces by documenting the varieties and in terrelations among user activity networks within this common and shared urban space.This research reveals the extent to which spaces,parks in this case,may be heterogeneous by unpacking their usage.The conclusions and in sights assert then ecessity of paying attention to design detail and creating designs that are responsive to evolving user activities.