会议信息

ICKG 2026: IEEE International Conference on Knowledge Graph

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ICKG
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截稿日期:
2026-06-19
通知日期:
2026-08-31
会议日期:
2026-11-12
会议地点:
Shenyang, China
届数:
17
浏览: 32203   关注: 12   参加: 4

会伴指数 (CP-I)

52.4 / 100
全站第 1,110 名 / 共 5,655 个会议 · 前 20%
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
78
社区关注 (10%)
40
资料公开度 (15%)
35

用到的输入: 有据可查的届次：17 · 在会伴关注它的研究者：12 人 · 过去 24 个月打开过本页的研究者：7 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-09

征稿

ICKG 2026 (IEEE International Conference on Knowledge Graph) is an academic conference held in Shenyang, China on 2026-11-12. The paper submission deadline is 2026-06-19. Acceptance notifications are sent on 2026-08-31.

The annual IEEE International Conference on Knowledge Graph (ICKG) provides a premier international forum for presentation of original research results in knowledge discovery and graph learning, discussion of opportunities and challenges, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of knowledge discovery from data, with a strong focus on graph learning and knowledge graph, including algorithms, software, platforms. ICKG 2026 intends to draw researchers and application developers from a wide range of areas such as knowledge engineering, representation learning, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high performance computing, and World Wide Web etc. By promoting novel, high quality research findings, and innovative solutions to address challenges in handling all aspects of learning from data with dependency relationship. All accepted papers will be published in the conference proceedings by the IEEE Computer Society. Awards, including Best Paper, Best Paper Runner up, Best Student Paper, Best Student Paper Runner up, will be conferred at the conference, with a check and a certificate for each award. The conference also features a survey track to accept survey papers reviewing recent studies in all aspects of knowledge discovery and graph learning. Topics of Interest Topics of interest include, but are not limited to: Foundations, algorithms, models, and theory of knowledge discovery and graph learning Knowledge engineering with big data Machine learning, data mining, and statistical methods for data science and engineering Acquisition, representation and evolution of fragmented knowledge Fragmented knowledge modeling and online learning Knowledge graphs and knowledge maps Graph learning security, privacy, fairness, and trust Interpretation, rule, and relationship discovery in graph learning Geospatial and temporal knowledge discovery and graph learning Ontologies and reasoning Topology and fusion on fragmented knowledge Visualization, personalization, and recommendation of Knowledge Graph navigation and interaction Knowledge Graph systems and platforms, and their efficiency, scalability, and privacy Applications and services of knowledge discovery and graph learning in all domains including web, medicine, education, healthcare, and business Big knowledge systems and applications Crowdsourcing, deep learning and edge computing for graph mining Large language models and applications Open source platforms and systems supporting knowledge and graph learning Datasets and benchmarks for graphs Neurosymbolic & Hybrid AI systems Graph Retrieval Augmented Generation Survey Track: Survey paper reviewing recent study in key aspects of knowledge discovery and graph learning. Special Track Topics Each special track is handled by respective special track chairs, and the papers are also included in the conference proceedings. Special Track 01: KGC and Knowledge Graph Building Special Track 02: KR and KG Reasoning Special Track 03: KG and Large Language Model Special Track 04: GNN and Graph Learning Special Track 05: QA and Graph Database Special Track 06: KG and Multi-modal Learning Special Track 07: KG and Knowledge Fusion Special Track 08: Industry and Applications
由 Dou Sun 最后更新于

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