In recent years, an increasing number of large-scale knowledge graphs have been constructed and published on the Web by both academic and industrial communities, such as DBpedia, YAGO, Freebase, Wikidata, Google Knowledge Graph, Microsoft Satori, Facebook Entity Graph, and others. Essentially, a knowledge graph is a large network consisting of entities, their properties, and the semantic relationships between entities. This graph-based knowledge data has posed significant challenges to traditional data management theories and technologies. Over the past two decades, the database community has made substantial efforts in graph databases to enhance the efficiency and scalability of storing, querying, mining, and analyzing large-scale graph data. However, there remains a gap between the requirements of knowledge graph applications across various domains and the current state of graph database technologies.
Against this backdrop, the 9th International Workshop on Knowledge Graph Management and Applications (KGMA 2026) aims to serve as a platform for researchers, practitioners, developers, and users from communities such as knowledge graph research and application, graph databases, and social networks. The workshop will focus on addressing the key challenges in knowledge graph management, analysis, and application, presenting state-of-the-art solutions, facilitating the exchange of ideas and research results, and discussing future research directions. This aligns closely with the theme of the 10th APWeb-WAIM Joint International Conference on Web and Big Data, as knowledge graphs are a core component of web data and big data management, making the workshop highly relevant to the main conference’s focus.
The topics of interest include, but are not limited to the following:
Knowledge Graph Organization and Construction
- Knowledge graph information extraction
- Knowledge graph data integration
- Knowledge graph construction
- Knowledge graphs and knowledge representation
- Knowledge graphs and knowledge bases
- Knowledge graphs and knowledge engineering
- Knowledge graphs and ontologies
- Knowledge graphs in social networks
- Probabilistic and uncertain knowledge graphs
Knowledge Graph and Graph Data Storage
- Knowledge graph storage and indexing
- RDF graph storage and indexing
- Graph database storage scheme
- Distributed knowledge graph storage and indexing
- Relational-based knowledge graph storage and indexing
Knowledge Graph Query Processing
- Graph pattern matching
- Reachability query processing
- Shortest path query processing
- Regular path query processing
- Navigational query processing
- Graph query languages
- Distributed/parallel graph query processing
- Knowledge graph query processing and benchmarking
Knowledge Graph Learning and Data Mining
- Knowledge graph embedding and representational learning
- Graph classification
- Graph clustering
- Graph frequent pattern mining
- Link prediction in knowledge graphs
- Outlier detection in knowledge graphs
- Deep learning on knowledge graphs
Knowledge Graph Analysis and Applications
- Knowledge graph data visualization
- Social network analysis using knowledge graphs
- Knowledge-graph based inference and reasoning
- Knowledge-graph based information retrieval
- Knowledge-graph based recommendation systems
- User interfaces of knowledge-graph based systems
- Question answering using knowledge graphs
- Knowledge-graph based intelligent systems
- Knowledge-graph based information systems
- Knowledge graphs and natural language processing
- Biological and biomedical knowledge graphs
- Knowledge-graph based bioinformatics
- Security, privacy, and trust on knowledge graphs
Knowledge Graph and Design&Art
- Knowledge-augmented artistic creation and design generation
- Multimodal knowledge graphs in visual art and design analysis
- Design knowledge management and reuse
- Digital art curation and exhibition based on knowledge graphs
- Knowledge graphs for design style recognition and evolution analysis
- Cross-domain knowledge integration for art and design innovation
All paper submissions for KGMA 2026 will be via Microsoft CMT (under the 9th International Workshop on Knowledge Graph Management and Applications (KGMA 2026)).
All submissions must be written in English and conform to the Springer LNCS proceedings format with the following page limits: 12 pages including references. Authors must provide the complete and final list of authors at the submission stage. No addition, removal, or change in the order of authors is allowed after submission.
Please use one of the following templates for the LNCS (Lecture Notes in Computer Science) format: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Meng Wang
Associate Professor, Tongji University, China
Email: [email protected]
Xin Wang
Professor, Tianjin University, China
Email: [email protected]