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USA-522310- Azienda Directories
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Azienda News:
- KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning
KnowFormer utilizes a transformer architecture to perform reasoning on knowledge graphs from the message-passing perspective, rather than reasoning by textual information like previous pretrained language model based methods
- Building Knowledge Graphs with LLM Graph Transformer
These two modes ensure that the LLM Graph Transformer is adaptable to different LLMs, allowing it to build graphs either directly using tools or by parsing output from a text-based prompt
- Relphormer: Relational Graph Transformer for Knowledge Graph . . .
To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer Specifically, we introduce Triple2Seq which can dynamically sample contextualized sub-graph sequences as the input to alleviate the heterogeneity issue
- Knowledge Graph Construction: State-of-the Art Techniques and . . .
Methods for knowledge graph construction are continually evolving Advancements in artificial intelligence, particularly with the advent of Generative Pre-trained Transformers (GPT), have paved the way for end-to-end automated methods for constructing knowledge graphs
- Unleashing Transformers for Knowledge Graphs - AI in Brief
Unlike past transformer-based models that relied on textual descriptions to make sense of graphs, KnowFormer takes a purely structural approach It utilizes the transformer’s attention mechanism to directly learn from the way entities are related in the graph
- Knowledge Graphs Vs Transformer Models | Restackio
Explore the differences between knowledge graphs and transformer models, focusing on their applications and strengths in data representation Knowledge Graph Embeddings (KGE) and Transformer models represent two distinct approaches to handling complex data structures, particularly in the context of knowledge graphs
- Graph Reasoning Transformers for Knowledge-Aware Question . . .
To address these challenges, we propose a novel knowledge-augmented question answering (QA) model, namely, Graph Reasoning Transformers (GRT) Different from conventional node-level methods, the GRT serves knowledge triplets as atomic knowledge and utilize a triplet-level graph encoder to capture triplet-level graph features
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