3rd NLP4KGC

International Workshop on Natural Language Processing 

for Knowledge Graph Creation



Amsterdam

September 17 2024

Supported by

NLP4KGC: 3rd International Workshop on Natural Language Processing for Knowledge Graph Creation in conjunction with SEMANTiCS 2024 Conference.



Call for Paper


This is the third edition of the workshop “Natural Language Processing for Knowledge Graph Creation – NLP4KGC” (NLP4KGC-2024).

The workshop focuses on methods and approaches of knowledge and data extraction from text, as well as theoretical and practical aspects of using semantics and Natural Language Processing for Knowledge Graph creation, and the use of such knowledge graphs for Graph Neural Network (GNN) tasks.

Knowledge Graphs (KG) are emerging and becoming increasingly popular. Building a domain knowledge graph from a large amount of text requires a tremendous amount of work, including entity recognition, entity disambiguation and relationship extraction. Because of this, there has been more work done on automated ways to generate knowledge graphs from text. Recent efforts in NLP development have shown that semantic deep neural networks can learn the complex syntactic and semantics of the natural language and thus, give more potential for automation even in the most complex domains i.e., legal documents. New approaches to Knowledge Graph development use a combination of extraction methods and state-of-the-art Natural Language Processing (NLP) techniques. Recently, considerable literature in this space has centered around the use of Graph Neural Networks (GNNs) to learn powerful embeddings which leverage topological structures in the KGs. Despite the successes this existing research has achieved, deep learning on graphs for NLP still faces many challenges. Presentation of challenges faced in specific domains such as Science, Sustainability are also are welcomed. For instance in the sciences, the production of resources (e.g., publications) is growing at a rate that outstrips an individual’s capacity to discovery and utilize them. As a result, alongside manual methods to structure and make discoverable that knowledge, automation is vital. We welcome applications that analyze and synthesize large volumes of scientific information (especially scientific literature, notes documents, and dataset metadata) and can use that information to create ontologies and semantic representations that better organize them. We also welcome contributions that address automation components of maintenance of ontologies and knowledge graphs. 


This workshop invites contributions on methods and approaches of knowledge and data extraction from text, as well as theoretical and practical aspects of using semantic deep NLP for KG creation and the use of such KG for Graph Neural Network (GNN) tasks.


Important Dates


Topics

Here are the major topics but not limited:

1.       Natural Language Understanding

• Entity extraction and resolution from text for knowledge graph construction

• Text entity relationship extraction and linking for knowledge graph construction

• Entity Disambiguation

• Large Language Models

• Multilingual/ language-specific information retrieval for knowledge graph construction

2.       Knowledge Graph Construction

• Ontology learning from text

• Neural network architectures for knowledge graph construction from text

• Dynamic knowledge graph building from text/text streams

• Explainable AI for knowledge graph construction from text

• GNNs for NLP (graph construction, graph representation learning)

3.     Responsible AI

•    Knowledge Graphs for Bias Mitigation

•    Interpretability and Explainability Knowledge Graph driven assessment Responsible AI frameworks

4.     Knowledge Graphs and Web

•  Automatic annotation of web pages with Schema.org

• Distant supervision with web-scale data such as Web Data Commons

 • Semantic Web and Linked Data

Submission Instructions

 We invite full research papers, negative results, position papers, dataset and system demo papers. The page limit for the full research papers, negative results and dataset papers is 16 pages excluding references and for the short papers and demos it is 7 pages excluding references.  Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this workshop. Submissions will be evaluated by the program committee based on the quality of the work and its fit to the workshop themes. All submissions are double-blind and a high-resolution PDF of the paper should be uploaded to the EasyChair submission site  before the paper submission deadline.  The accepted papers will be presented at the NLP4KG workshop integrated with the conference, and they will be published as CEUR proceedings. All must be submitted and formatted in the style of the CEUR proceedings format. For details on CEUR style, see CEUR's Author Instruction. Also see Overleaf Template..

If you have any questions, please contact us via email: sima.iranmanesh@bcu.ac.uk