The goal of this workshop is to facilitate practitioners in sharing their experiences, best practices, challenges, opportunities and lessons learned. Focus is on “Applied” Knowledge Graphs (KGs). This means all the presentation/demo will be on applying Knowledge Graphs work to use cases from Government, Industry, Non-Profit-Organisation, etc. Topics of interest include both generation of KGs through innovative approaches (e.g. by information extraction through LLMs), LLM enhanced by KGs, Ontologies, Taxonomies, Semantic Web, Linked Open-Data and exploitation of KGs for improving common tasks, e.g. Information Retrieval through Graph-Retrieval Augmented Generation (GraphRAG), etc.
Findings and application of KGs in various fields related to AI and Knowledge Engineering, such as AI-Agents, Semantic Reasoning, Knowledge Graph Database, Knowledge Visualization, Trust and Security of Knowledge Graphs, Natural Language Processing (NLP), Question Answering Systems, Graph Neural Networks (GNNs), Machine Learning are welcome.
The PRIWAKG Program Committee invites you to actively contribute to the workshop by submitting high quality Short Papers (limited to 6 pages including references) or Regular Papers (limited to 12 pages including references) of your use-cases for consideration and inclusion in the programme of the PRIWAKG-2024.
The PRIWAKG-2024 workshop will be held in HYBRID mode, meaning that you have the option to participate onsite or remotely via Zoom.
Deadline for Paper Submission: 30th August 2024
Acceptance/Rejection Notification: 20th September 2024
Camera Ready Submission & Author Registration: 20th October 2024
Presentation Slide Deadline: 30th October 2024
Workshop Date: TBD
Oral presentation
Poster presentation
Use-Case Demo
Papers describing your Use-Cases can be submitted under (but not limited to) the following fields:
Applied Research:
o Knowledge Graphs and Explainable AI
o Knowledge Graph Based Academic Network
o Knowledge Graph Based Rule Engine
o Knowledge Graph Embeddings
o Knowledge Acquisition
o Knowledge Graph Completion
o Knowledge Fusion
o Knowledge Reasoning
o Knowledge Refinement
o Knowledge Evolution
o Cognitive Knowledge Graph
o Visual Knowledge Graph
o Spatial Knowledge Graph
o Knowledge graphs for big scientific and experimental data
o Big knowledge graph representation and modeling
o Constructing knowledge graphs from structured and unstructured data
o Big knowledge graph embeddings
o Link prediction
o Knowledge graph completion
o Natural language processing and knowledge graph
o Semantic Web, ontology and knowledge graph
o Knowledge graph for recommender systems
o Scalable knowledge graph reasoning and inference
o Knowledge graph for big data processing
o Knowledge graph applications in business, biomedical, healthcare, etc.
o Knowledge graph visualization and human interaction
o Knowledge graph for explainable AI
o Knowledge graph alignment
o Graph neural networks and big knowledge graphs
o Scalable knowledge graph storage and query processing
o Record linkage using knowledge graphs
o Knowledge Graph for Sustainability
Social Networks:
o News-Entity Knowledge Graphs
o Information Aggregation of User-User and User-Item Graphs
o Multimedia knowledge Graph Construction
o Knowledge Graph Propagation
o Knowledge Graph Analytics
Health/Medical Care:
o Medical Knowledge Graph Embeddings
o Knowledge Guided Graph Attention Network
o Mining the Relationships Between Drugs
o Multilingual Clinical/Medical Knowledge Graph
o Large-Scale Multimodal and Multi-source Medical Knowledge Base
o Knowledge Graphs in Medical Imaging Analysis
o Biomedical Knowledge Graph
o Life Sciences Knowledge Graph
AI Systems Using Knowledge Graphs:
o Recommender Systems
o Question-Answering Systems
o Information Retrieval
o Risk Analytics
o Climate Sciences
o Environmental, Social and Corporate Governance
o Social Good (UN's 17 Sustainable Development Goals)
o Space Mission and Exploration
o Geoscience
o Geographic
o Macroeconomic Analysis
Technologies:
o Integration of Knowledge Graph with GenAI
o Vector Database
o Retrieval Augmented Generation (RAG)
o GraphRAG
o Graph Neural Network
Authors of Short and Regular papers are required to submit according to the CEUR-WS format. Authors are encouraged to use Overleaf or Microsoft Word to prepare their paper according to the above-mentioned format. Refer to the following URL for format details.
To submit your papers, please go the following page on Easychair.
All papers received by the indicated deadline and prepared in the above mentioned format will be reviewed and evaluated by the PRIWAKG Program Committee. Acceptance, together with technical details for camera ready paper and presentation (oral and/or poster presentation), will be notified to the submitting author by the Notification date shown above.
All accepted Short & Regular papers recommended by the Program Committee will be published in the PRIWAKG-2024 Proceedings via CEUR-WS.
Dr. Dickson Lukose
Mr. Prasad Yalamanchi
E-mail: priwakg@gmail.com
Copyright © 2024 Pacific Rim International Workshop on Applied Knowledge Graphs - All Rights Reserved.
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