Biomedical information extraction (BioIE) is an important task. The aim is to analyze
biomedical texts and extract structured information such as named entities and semantic …

Entity linking (EL) is the process of linking entity mentions appearing in web text with their
corresponding entities in a knowledge base. EL plays an important role in the fields of …

Entity representations are useful in natural language tasks involving entities. In this paper,
we propose new pretrained contextualized representations of words and entities based on …

Pre-trained language representation models (PLMs) cannot well capture factual knowledge
from text. In contrast, knowledge embedding (KE) methods can effectively represent the …

Neural language representation models such as BERT pre-trained on large-scale corpora
can well capture rich semantic patterns from plain text, and be fine-tuned to consistently …

Incorporating knowledge graph into recommender systems has attracted increasing
attention in recent years. By exploring the interlinks within a knowledge graph, the …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

Distributed word representations have become an essential foundation for biomedical
natural language processing (BioNLP), text mining and information retrieval. Word …

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed
alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model …

Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …