ContE: contextualized knowledge graph embedding for circular relations

TL;DR: KGE techniques based on representation spaces are used to represent knowledge graphs into low-dimensional semantic spaces for various applications. The article categorizes KGE models based on three mathematical perspectives of representation spaces and discusses different KGE methods over these categories. It also explores the advantages of mathematical space in different embedding needs and provides promising research directions.

TL;DR: Wang et al. as discussed by the authors presented the first XWS-TC benchmark to compare the two approaches on fair grounds, where the datasets, supervisions, and hyperparameter choices are standardized across methods.

TL;DR: A benchmark comparing seed matching and prompting approaches for extremely weakly supervised text classification. Findings suggest that both approaches are competitive, with seed matching being more tolerant to human guidance changes and prompting being more selective to pre-trained language models.

Abstract: Current recommendation systems for tourism struggle to capture the dynamic changes in user preferences. Therefore, this study proposes a knowledge graph embedding technique that combines dynamic mapping matrices to construct a tourism recommendation model. Meanwhile, bidirectional long short-term memory networks and node-level attention mechanisms are introduced to enhance the modeling ability for dynamic changes in user behavior. The experimental results on the YAGO11k dataset showed that the accuracy of the training set reached 99.4%, and the model had excellent training performance and generalization ability. In the evaluation indicators of the knowledge graph, the average ranking and average reciprocal ranking were 452 and 0.430, significantly better than the baseline, with a hit rate of up to 29.1%. This model provides an effective solution for personalized travel recommendations.

TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.

TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

TL;DR: TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.

TL;DR: MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.