Approximate Answering of Graph Queries
원문 초록
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and evolves, making existing facts deprecated or introducing new ones. However, we would still want to be able to answer queries as if the graph were complete. In this chapter, we will give an overview of several methods which have been proposed to answer queries in such a setting. We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations. Then, we give an overview of the different approaches and describe them in terms of expressiveness, supported graph types, and inference capabilities.
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Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models
DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
Knowledge Graph Embedding via Graph Attenuated Attention Networks
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
토론
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