  Contextualized entity representations learned by state-of-the-art
transformer-based language models (TLMs) like BERT, GPT, T5, etc., leverage the
attention mechanism to learn the data context from training data corpus.
However, these models do not use the knowledge context. Knowledge context can
be understood as semantics about entities and their relationship with
neighboring entities in knowledge graphs. We propose a novel and effective
technique to infuse knowledge context from multiple knowledge graphs for
conceptual and ambiguous entities into TLMs during fine-tuning. It projects
knowledge graph embeddings in the homogeneous vector-space, introduces new
token-types for entities, aligns entity position ids, and a selective attention
mechanism. We take BERT as a baseline model and implement the
"Knowledge-Infused BERT" by infusing knowledge context from ConceptNet and
WordNet, which significantly outperforms BERT and other recent knowledge-aware
BERT variants like ERNIE, SenseBERT, and BERT_CS over eight different subtasks
of GLUE benchmark. The KI-BERT-base model even significantly outperforms
BERT-large for domain-specific tasks like SciTail and academic subsets of QQP,
QNLI, and MNLI.
