Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …

Organized relational knowledge in the form of “knowledge graphs” is important for many
applications. However, the ability to populate knowledge bases with facts automatically …

Large language models (LLMs) have great potential for synthetic data generation. This work
shows that useful data can be synthetically generated even for tasks that cannot be solved …

Extracting information from full documents is an important problem in many domains, but
most previous work focus on identifying relationships within a sentence or a paragraph. It is …

Structured and grounded representation of text is typically formalized by closed information
extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets …

Relation extraction studies the issue of predicting semantic relations between pairs of
entities in sentences. Attention mechanisms are often used in this task to alleviate the inner …

KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge
base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet …

Recently, we have witnessed entity profiling (EP) becoming increasingly one of the most
important topics in information extraction, personalized applications, and web data analysis …

The task of knowledge base population (KBP) aims to discover facts about entities from texts
and expand a knowledge base with these facts. Previous studies shape end-to-end KBP as …

Cold start knowledge base population (KBP) is the problem of populating a knowledge base
from unstructured documents. While neural networks have led to improvements in the …