Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

Abstract Domain tuning pre-trained language models (PLMs) with task-specific prompts
have achieved great success in different domains. By using cloze-style language prompts to …

Background. There is an increasing trend to represent domain knowledge in structured
graphs, which provide efficient knowledge representations for many downstream tasks …

The complexities arising from disparate data sources, conflicting contracts, residency
requirements, and the demand for multiple AI models in trade finance supply chains have …

We present a novel method for relation extraction (RE) from a single sentence, mapping the
sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially in …

In the last decade, a large number of knowledge graph (KG) completion approaches were
proposed. Albeit effective, these efforts are disjoint, and their collective strengths and …

Document-level relation extraction (DocRE) poses the challenge of identifying relationships
between entities within a document. Existing approaches rely on logical reasoning or …

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph.
Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent …

With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient
form of knowledge representation that captures the semantics of web objects. In recent …