Titlebook: Knowledge Science, Engineering and Management; 15th International C Gerard Memmi,Baijian Yang,Meikang Qiu Conference proceedings 2022 The E

壓迫

微不足道

Cross-perspective Graph Contrastive Learning and recommendation. Most existing methods adopt Graph Neural Network (GNN) or its variants to propagate the attributes over the structure network. However, the attribute information will be overshadowed by the structure perspective. To address the limitation and build a link between nodes features

付出

倒轉(zhuǎn)

Pre-train Unified Knowledge Graph Embedding with?Ontologyneralization capability of the model. Recent research shows that introducing additional ontology information can naturally convert the entity typing task to a specific case of link prediction between the instance and ontology layers. However, the unbalanced scale of the two layers brings difficulty

艦旗

離開真充足

Irremediable

暫時(shí)別動(dòng)

STING

Question Answering over?Knowledge Graphs with?Query Path Generationefects such as high cost of query graph generation and large search scope of knowledge graphs. Especially for the complex questions, which refer to those with multi-hop relations and constraints, there are problems such as incomplete search and inaccurate selection of answers. In order to solve the

雄辯

Improving Parking Occupancy Prediction in?Poor Data Conditions Through Customization and?Learning tog and additional congestion. However, accurate and fast forecasting in data-poor car parks remains a challenge. To tackle the bottleneck, this paper proposes a knowledge transfer framework that can customize a lightweight but effective pre-trained network to those data-deficient parking lots for POP