Call for papers


2nd NLP4KGC: Natural Language Processing for Knowledge Graph Construction

 

SCOPE AND OBJECTIVE OF WORKSHOP

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, more work has been done on automated ways to generate knowledge graphs from text. Recent efforts in NLP (Natural Language Processing) 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 KG development use a combination of extraction methods and state-of-the-art NLP techniques. Additionally, the advancement of Graph Neural Networks (GNNs) are able to learn powerful embeddings which leverage topological structures in the KGs and provide explanations of the outcome. 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 is also 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.

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.       Knowledge Graphs and Web

·       Automatic annotation of web pages with Schema.org

·       Distanst supervision with web-scale data such as Web Data Commons

• Semantic Web and Linked Data

 

SUBMISSION


• Papers submission deadline: July 3, 2023


 

ORGANIZING CHAIRS

Edlira Vakaj, Birmingham City University, UK

Sanju Tiwari, Universidad Autónoma de Tamaulipas, México

Rizou Stamartina, Singular Logic, Greece

Nandana Mihindukulasooriya, IBM Research, Ireland

Fernando Ortiz-Rodríguez, Universidad Autónoma de Tamaulipas, Mexico

Ryan Mcgranaghan, NASA Jet Propulsion Laboratory, USA

 

 

Contact Person

Edlira Vakaj