In recent years, dynamic knowledge graph embedding (DKGE) has been widely studied to deal with large-scale dynamic knowledge graphs (DKG). The core idea is to encode dynamic information within DKGs into embedding vectors and decode them for various downstream tasks on the DKGs. Plenty of contributions have been made to this field. Full retraining DKG models additionally encode temporal information for higher performance, while neighboring retraining models view time data as dynamic changes in graph topology for better efficiency. However, existing approaches within these categories suffer from either effectiveness-insufficient or efficiency-insufficient issues. Recent contributions in graph area propose solutions to selectively retrain the models by choosing training data following certain criteria, but the majority of selective retraining models are designed for homogeneous graphs. The heterogeneous graph information and large graph sizes make it improper to transfer methods across scenarios. In this paper, we propose an efficient selective incremental training framework for DKGE, namely SIT. Given a restriction on training data size, we select a set of important triples instead of all triples in the DKG to improve training efficiency. In detail, we design a novel importance criteria considering DKGE model parameters, historical embedding and graph topology. Extensive experiments on open-source datasets demonstrate the effectiveness and efficiency of the SIT framework against different DKGE models.

In recent years, domain-specific knowledge bases (KBs) have attracted more attention in academics and industry because of their expertise and in-depth representation in a specific domain. However, when constructing a domain-specific KB, one needs to address not only the challenges in constructing a general KB, but also the difficulties raised by the nature of domain-specific raw data. Considering the usability of financial Knowledge Bases (KBs) in many downstream applications, such as financial risk analysis and fraud detection, we aim to build a Temporal Financial KB. However, the complex, time-varying financial knowledge and the volatile financial market evolution make construction quite challenging. Thus, we propose a Platform for Temporal Financial KB Construction, T-FinKB Platform1. This platform consists of three fundamental modules, i.e., evolved knowledge extraction module, temporal record linkage and conflicts resolution module, and dynamic knowledge update module designed for financial knowledge. Generated temporal financial KBs (T-FinKBs) from T-FinKB Platform can be updated automatically. T-FinKB has been successfully applied to a downstream application, stock trend prediction with backtesting evaluation. In addition, T-FinKB Platform provides several APIs for visualization, queries and analytics.