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Found 8 papers, 4 papers with code
Open Knowledge Base Canonicalization with Multi-task Learning
MulCanon unifies the learning objectives of these sub-tasks, and adopts a two-stage multi-task learning paradigm for training.
Open Knowledge Base Canonicalization with Multi-task Unlearning
MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training.
Multi-modal Entity Alignment in Hyperbolic Space
To mitigate this problem, a viable approach is to integrate complementary knowledge from other MMKGs.
Towards Entity Alignment in the Open World: An Unsupervised Approach
These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment.
Reinforcement Learning based Collective Entity Alignment with Adaptive Features
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs).
Degree-Aware Alignment for Entities in Tail
Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge.
CLEEK: A Chinese Long-text Corpus for Entity Linking
Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus.
Collective Entity Alignment via Adaptive Features
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration.
