Integrating knowledge graphs into NLP tasks

August 8, 2025 8:00 AM (GMT+7) → 10:00 AM

From AI research:

“Integrating Knowledge Graphs into NLP Tasks” is a cutting-edge research topic at the intersection of structured symbolic knowledge and data-driven language models.

It opens up powerful possibilities for making NLP models more fact-aware, interpretable, and contextually grounded.

🔍 I. What Are Knowledge Graphs (KGs)?

A Knowledge Graph is a structured representation of facts in the form of triples:

(subject, relation, object)
e.g., ("Einstein", "born_in", "Ulm")

Popular examples:

Wikidata, DBpedia, YAGO
Google Knowledge Graph
ConceptNet (commonsense)
UMLS (biomedical)

📌 II. Why Integrate KGs with NLP?

Language models like GPT or BERT are:

Great at generalization
But often lack factual grounding, commonsense, or domain-specific accuracy

Adding KGs helps:

Benefit	Description
Factual accuracy	Reduce hallucination and inject trusted facts
Commonsense reasoning	Augment models with world knowledge
Explainability	Trace answers back to symbolic logic or links
Personalization/context	Use user-specific KG to adapt responses
Multimodal linking	Connect text to knowledge (e.g., visual KG + captions)