Multi-source aggregated classification for stock price movement prediction
Predicting stock price movements is a challenging task. Previous studies mostly used
numerical features and news sentiments of target stocks to predict stock price movements …
numerical features and news sentiments of target stocks to predict stock price movements …
Fusing topology contexts and logical rules in language models for knowledge graph completion
Abstract Knowledge graph completion (KGC) aims to infer missing facts based on the
observed ones, which is significant for many downstream applications. Given the success of …
observed ones, which is significant for many downstream applications. Given the success of …
Quantitative stock portfolio optimization by multi-task learning risk and return
Selecting profitable stocks for investments is a challenging task. Recent research has made
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
Knowledge graphs in education and employability: A survey on applications and techniques
Studies on the relationship between education and employability are of paramount
importance for policy makers, training institutions, companies and students. The availability …
importance for policy makers, training institutions, companies and students. The availability …
Rule-enhanced iterative complementation for knowledge graph reasoning
Abstract Knowledge graph (KG) reasoning aims to infer missing valid triples from observed
triples, thereby improving the semantics of the whole KG. The general KG reasoning …
triples, thereby improving the semantics of the whole KG. The general KG reasoning …
The data set knowledge graph: Creating a linked open data source for data sets
Several scholarly knowledge graphs have been proposed to model and analyze the
academic landscape. However, although the number of data sets has increased remarkably …
academic landscape. However, although the number of data sets has increased remarkably …
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation
Acquiring reviewers for academic submissions is a challenging recommendation scenario.
Recent graph learning-driven models have made remarkable progress in the field of …
Recent graph learning-driven models have made remarkable progress in the field of …
Multiview feature augmented neural network for knowledge graph embedding
D Jiang, R Wang, L Xue, J Yang - Knowledge-Based Systems, 2022 - Elsevier
Link prediction in knowledge graph embedding is a meaningful research topic. Knowledge
graph embedding (KGE) focuses on the problem of predicting missing links based on triples …
graph embedding (KGE) focuses on the problem of predicting missing links based on triples …
Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students
The nature of education has been transformed by technological advances and online
learning platforms, providing educational institutions with more options than ever to thrive in …
learning platforms, providing educational institutions with more options than ever to thrive in …
Aic: an industrial knowledge graph with Abstraction-Instance-Capability reasoning abilities for personalized customization
In the era of the internet, people are increasingly interested in highly personalized
customization, which has been leading the global industry to transform from “Enterprise …
customization, which has been leading the global industry to transform from “Enterprise …