Curriculum-meta learning for order-robust continual relation extraction
Continual relation extraction is an important task that focuses on extracting new facts
incrementally from unstructured text. Given the sequential arrival order of the relations, this …
incrementally from unstructured text. Given the sequential arrival order of the relations, this …
Do embeddings actually capture knowledge graph semantics?
Abstract Knowledge graph embeddings that generate vector space representations of
knowledge graph triples, have gained considerable popularity in past years. Several …
knowledge graph triples, have gained considerable popularity in past years. Several …
Generalized relation learning with semantic correlation awareness for link prediction
Developing link prediction models to automatically complete knowledge graphs has recently
been the focus of significant research interest. The current methods for the link prediction …
been the focus of significant research interest. The current methods for the link prediction …
Experiments on paraphrase identification using quora question pairs dataset
A Chandra, R Stefanus - arXiv preprint arXiv:2006.02648, 2020 - arxiv.org
We modeled the Quora question pairs dataset to identify a similar question. The dataset that
we use is provided by Quora. The task is a binary classification. We tried several methods …
we use is provided by Quora. The task is a binary classification. We tried several methods …
Do Similar Entities have Similar Embeddings?
Abstract Knowledge graph embedding models (KGEMs) developed for link prediction learn
vector representations for entities in a knowledge graph, known as embeddings. A common …
vector representations for entities in a knowledge graph, known as embeddings. A common …
Research on relation extraction method based on similar relations and bayesian neural network
Y Fang, Y Zang, J Ge - Journal of Physics: Conference Series, 2021 - iopscience.iop.org
The solidification of network parameter values for most relation extraction models after
training makes the model overconfident in the process of prediction and classification tasks …
training makes the model overconfident in the process of prediction and classification tasks …
[PDF][PDF] Resolving Representation Heterogeneity in Real-World Knowledge Graphs
JC Kalo - 2021 - leopard.tu-braunschweig.de
Abstract Knowledge graphs are repositories providing factual knowledge about entities.
They are a great source of knowledge to support modern AI applications for Web search …
They are a great source of knowledge to support modern AI applications for Web search …
Developing Novel Triple Embeddings for Scalable Alignment of Knowledge Graphs and Natural Language
A Kalinowski - 2024 - search.proquest.com
Neural representation learning has become the de facto mode of large-scale information
compression over the past decade. These compression schemes generate embeddings …
compression over the past decade. These compression schemes generate embeddings …
Representation and curation of knowledge graphs with embeddings
N Jain - 2022 - publishup.uni-potsdam.de
Knowledge graphs are structured repositories of knowledge that store facts about the
general world or a particular domain in terms of entities and their relationships. Owing to the …
general world or a particular domain in terms of entities and their relationships. Owing to the …