China Conference on Knowledge Graph and Semantic Computing (CCKS 2026)

August 21–23, 2026, Xi’an

http://sigkg.cn/ccks2026/

1.   Conference Overview

The China Conference on Knowledge Graph and Semantic Computing (CCKS) is organized by the Technical Committee on Language and Knowledge Computing of the Chinese Information Processing Society of China. It originated from the Chinese Knowledge Graph Symposium (CKGS) and the Chinese Semantic Web and Web Science Conference (CSWS). In 2016, the two conferences were merged. From 2016 to 2024, CCKS has been successfully held in Beijing, Chengdu, Tianjin, Hangzhou, Nanchang, Guangzhou (online), Qinhuangdao, Shenyang, Chongqing, and Fuzhou.

CCKS has become the core academic conference in China in the fields of knowledge graph and semantic technology, attracting researchers and professionals from related areas such as knowledge representation and reasoning, natural language understanding and knowledge acquisition, graph data management and graph computing, and intelligent question answering.

CCKS 2026 will be held in Xi’an from August 21 to 23, 2026. The theme of this year’s conference is “Knowledge, Memory, and Cognitive Reasoning”, aiming to explore the deep integration and synergistic evolution between knowledge memory mechanisms and cognitive reasoning. The conference will focus on key technologies in knowledge graphs and large models, such as knowledge representation, knowledge storage, knowledge mining, knowledge fusion, knowledge reasoning, explainability, memory enhancement, and cognitive computing. The goal is to guide breakthroughs in knowledge-driven theories and technologies for the next generation of cognitive intelligence and to promote the advancement of its industrial applications. The program will feature tutorials, keynote talks, frontiers and trends forum, industry forum, young scholar forum, evaluations and competitions, paper presentations, posters, and system demonstrations. Distinguished scholars and industry leaders will be invited to share the latest developments and foster academic-industry collaboration.

2. Evaluation Tasks

CCKS 2026 will organize evaluation competitions related to knowledge graphs and semantic computing to provide a platform for researchers to test technologies, algorithms and systems, and to promote research progress and industrial application in combining knowledge graphs with large models. The evaluation tasks in CCKS 2025 comprised eight competitions covering the topics such as knowledge editing, knowledge extraction, and complex question answering, attracting more than 2,100 teams and nearly 3,000 participants and offering a total prize pool of 150,000 RMB, creating significant impact in industry and academia. For CCKS 2026, task organizers may select the platform and evaluation scheme according to the characteristics of their tasks. We now publicly solicit evaluation task proposals for CCKS 2026 from researchers, research institutions, and enterprises in related fields.

Important Dates:

  • Submission deadline for task proposals: April 10
  • Notification of review results: April 12
  • Release of evaluation tasks: April 18
  • Registration period: April 18–July 3
  • Release of training and validation data: May 15
  • Release of test data: July 3
  • Submission of test results: July 10
  • Notification of evaluation rankings: July 17
  • Submission of evaluation papers: August 3
  • CCKS 2026 conference dates (evaluation reports and awards): August 21–23

Evaluation Chairs:

  • Jingping Liu, Sun Yat‑sen University (liujp68@mail.sysu.edu.cn)
  • Sheng Bi, Southeast University (bisheng@seu.edu.cn)

Please send evaluation task proposals to the evaluation chairs by email. The proposal should describe the task content and the preparation of evaluation data in detail. For the template and details of evaluation proposals, please refer to the task description files of CCKS 2025 (https://sigkg.cn/ccks2025/evaluation-2/).

Evaluation task topics include (but are not limited to):

– Knowledge Representation and Reasoning

  • Knowledge representation and ontology modeling
  • Knowledge representation learning
  • Ontology reuse and evolution
  • Ontology mapping, fusion and alignment
  • Ontology evaluation
  • Knowledge reasoning
  • Knowledge base completion
  • Knowledge representation and reasoning empowered by large models
  • Explainable knowledge reasoning/validation for cognitive reasoning
  • Memory‑enhanced knowledge representation and reasoning

– Knowledge Acquisition and Knowledge Graph Construction

  • Open knowledge extraction
  • Crowdsourced knowledge engineering and collaborative knowledge acquisition
  • Human–machine collaborative knowledge base construction
  • Knowledge mining empowered by large models
  • Knowledge acquisition from Wiki data
  • Tools, languages, and systems for automated knowledge base construction
  • Knowledge acquisition based on supervised/unsupervised learning
  • Semi‑supervised/weakly supervised learning and text extraction
  • Knowledge acquisition for memory and reasoning
  • Multimodal knowledge acquisition and alignment

– Linked Data, Knowledge Integration and Knowledge Graph Storage Management

  • Entity recognition, entity disambiguation, and entity linking
  • Terminology mapping and integration
  • Linking and integration of heterogeneous knowledge
  • Data integration based on ontologies
  • Knowledge fusion empowered by large models
  • Knowledge querying and search
  • Elastic knowledge storage and distributed computation
  • Graph databases
  • Knowledge query analysis empowered by large models
  • Knowledge storage and update for memory enhancement
  • Knowledge consistency/trustworthiness assessment and verification

– Natural Language Understanding, Semantic Computing and Knowledge Graph Mining

  • Text understanding
  • Machine reading comprehension
  • Semantic similarity/relevance computation
  • Synonym mining
  • Natural language understanding empowered by large models
  • Semantic interpretation for cognitive reasoning
  • Memory‑enhanced semantic understanding

– Knowledge Graph Applications

  • Knowledge graph visualization
  • Semantic search
  • Knowledge‑based question answering systems
  • Intelligent personal assistant systems
  • Knowledge‑based semantic analysis of natural language, speech, images, and video
  • Intelligent recommendation
  • Applications of large models and knowledge graphs
  • Explainable applications for industry knowledge utilization
  • Evaluation of knowledge‑driven decision/planning applications

– Knowledge Graph Empowered Large Models

  • Knowledge graph enhanced pre‑training of large models
  • Knowledge graph enhanced fine‑tuning of large models
  • Validation and interpretation of knowledge graph enhanced large model generation
  • Knowledge editing for large models based on knowledge graphs
  • Retrieval‑augmented generation based on knowledge graphs
  • Knowledge graph enhanced cognitive reasoning of large models
  • Knowledge memory mechanisms and memory‑enhanced generation

– Knowledge Graph Empowered Agent Applications

  • Agent applications enhanced by knowledge graphs
  • Knowledge graph enhanced agent question answering and dialogue
  • Knowledge graph enhanced agent retrieval applications
  • Knowledge graph enhanced multi‑agent collaboration
  • Evaluation of multi‑agent knowledge utilization capabilities
  • Agent memory related tasks
  • Multi‑agent memory sharing and alignment
  • Cognitive reasoning agents based on knowledge graphs