TL;DR
The Adaptive Contrastive Knowledge Graph Embedding (ACKGE) framework, which uses the empirical mean and standard deviation of entity similarities to define probabilistic semantic bounds, achieves competitive performance against strong baselines in link prediction tasks, particularly in handling complex relational structures.
With the increasing complexity of online communication, sensitive content frequently appears in variant, implicit, and context-dependent forms, posing significant challenges to conventional flat detection approaches. Domain-specific fine-tuning often induces representation anisotropy and semantic feature entanglement,...
Semantic Global Graph with Multi-layer Alignment for Recommendation (SeGMA4Rec), which combines item-level semantic enrichment with sequence-level hierarchical regularization, is proposed and achieves strong and generally superior performance over competitive baselines.
A knowledge graph completion model based on semantic constrained hybrid scoring (SCHS) is proposed, which integrates three synergistic mechanisms that essentially resolves the rigid trade-off between geometric alignment and flexible semantic matching.
An adaptive geometric hypergraph encoder that projects entities into a product manifold and design a topology-aware gating mechanism to dynamically select optimal geometric spaces for each entity, and a role-aware Transformer equipped with role-specific projections and micro-structural bias injection to refine embeddin...
This framework introduces a dual-encoder architecture that separately processes structural and attribute information, incorporates node positional encoding to approximate Neighborhood Identity Distribution (NID), and employs dual reconstruction tasks for both edges and node attributes.
A novel FKGC model, context-aware relational learning and multidimensional matching (CRL-MM), which considers the entity pair and its neighborhood as a unified contextual whole, aggregating neighborhood information through adaptive task relations and paired entity awareness to improve entity encoding.
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