Abstract Knowledge Graph Completion (KGC) is a fundamental problem for temporal
knowledge graphs (TKGs), and TKGs embedding methods are one of the essential methods …

With the number of scientific papers growing exponentially, recommending relevant papers
for researchers has become an important and attractive research area. Existing paper …

Recommendation systems have required centralized storage of user data, but due to privacy
concerns, recent studies adopted federated learning (FL) that discloses intermediate …

Paper recommendation with personalized methods helps researchers to track the latest
academic trends and master cutting-edge academic trends efficiently. Meanwhile, the …

The fast growth of data in the academic field has contributed to making recommendation
systems for scientific papers more popular. Content-based filtering (CBF), a pivotal …

Purpose The explosion of data due to the sophistication of information and communication
technology makes it simple for prospective tourists to learn about previous hotel guests' …

Extraction of methodology component names from scientific articles is a challenging task
due to the diversified contexts around the occurrences of these entities, and the different …

Research in scientific disciplines evolves, often rapidly, over time with the emergence of
novel methodologies and their associated terminologies. While methodologies themselves …

In order to solve the problem of low extraction accuracy caused by the lack of consideration
of the impact of the following text of the central word on the topic word in LSTM network, the …

To face the problem of information overload, digital libraries, like other businesses, have
used recommender systems and try to personalize recommendations to users by using the …