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 …

Abstract Knowledge graph embeddings that generate vector space representations of
knowledge graph triples, have gained considerable popularity in past years. Several …

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 …

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 …

Abstract Knowledge graph embedding models (KGEMs) developed for link prediction learn
vector representations for entities in a knowledge graph, known as embeddings. A common …

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 …

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 …

Neural representation learning has become the de facto mode of large-scale information
compression over the past decade. These compression schemes generate embeddings …

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 …