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ICKG 2026: IEEE International Conference on Knowledge Graph

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ICKG
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投稿締切日:
2026-06-19
通知日:
2026-08-31
開催日:
2026-11-12
開催地:
Shenyang, China
開催回数:
17
閲覧: 32626   フォロー: 12   参加: 4

会伴インデックス (CP-I)

52.4 / 100
全 5,693 件中 第 1,167 位 · 上位 21%
学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
78
コミュニティの注目度 (10%)
40
公開情報の充実度 (15%)
35

使用した入力: 確認できる開催回数：17 · 会伴でフォローしている研究者：12 人 · 過去 24 か月にこのページを開いた研究者：7 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-10-03

論文募集

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
最終更新：Dou Sun（）

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