日本データベース学会の皆様
早稲田大学の山名です。
JSTでは、以下の国際シンポジウムを開催いたしますので、お時間がございましたら
ご参加いただければ幸いです。
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2024/9/12(木) 9:25-16:29@日本科学未来館（東京都江東区青海2-3-6）
「Society5.0を支える革新的コンピューティング技術」 国際シンポジウム
https://www.jst.go.jp/kisoken/crest/com-revol/index.html
配信もありますが、ポスター・デモを除きます。
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本シンポジウムは、坂井先生＠東大が領域長を務める
CREST「 「Society5.0を支える革新的コンピューティング技術」 のもと
採択されたCRESTプロジェクトの発表の他、
Intelligent Cyber-Physical Systems: Harnessing AI and ML Innovations for Social Good
Radu Marculescu (Professor, Texas University at Austin)
9:50-10:50
の基調講演があります。
講演者
テキサス大学オースティン校 Radu Marculescu 教授
講演者略歴：
ラドゥ・マルクレスク(Radu Marculescu)教授(IEEEフェロー・ACMフェロー)
テキサス大学オースティン校 電気・コンピュータ工学部 教授 兼 ローラ・ジェニングス・ターナー チェア。
2000年から2019年まで、カーネギーメロン大学 電気・コンピュータ工学部 教授。
現在は、コンピュータビジョン、バイオイメージング、ソーシャルセンシング、およびインターネット・オブ・シングス
（IoT）アプリケーションのためのシステム設計と最適化のためのML/AIアルゴリズムとツールの開発等の
研究を行う。2019年には、ネットワーク・オン・チップの設計、分析、および最適化の科学への重要な
貢献に対してIEEE Computer Society Edward J. McCluskey Technical Achievement Awardを
受賞。最近では、The International Conference on Hardware/Software Co-Design and
System Synthesis (CODES) で、2020年ESWEEK Test-of-Time Awardを受賞。
IEEEフローおよびACMフェロー。
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早稲田大学 山名早人
日本データベース学会のみなさま
筑波大学の天笠です．お世話になっております．
知識グラフに関する国際会議 ICKG 2024 の論文募集をご案内しま
す．関連する成果をお持ちでご興味があれば，投稿をご検討くださ
い．よろしくお願いいたします．
天笠俊之
The 15th IEEE International Conference on Knowledge Graph (ICKG), December 11-12, Abu Dhabi, UAE
http://ickg2024.openkg.cn
All deadlines are at 11:59PM Pacific Daylight Time.
Paper submission (abstract and full paper): July 31, 2024 (!!!Extended to September 2nd, 2024!!!)
Notification of acceptance/rejection: September 30, 2024
Camera-ready deadline and copyright forms: October 15, 2024
Early Registration Deadline: November 11, 2024
Conference: December 11-12, 2024
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 2024 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. At least five high quality papers will be invited for a special issue of the Knowledge and Information Systems Journal, in an expanded and revised form. In addition, at least eight quality papers will be invited for a special issue of Data Intelligence Journal in an expanded and revised form with at least 30% difference.
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.
Survey Track
Survey paper reviewing recent study in keep aspects of knowledge discover and graph learning.In addition to the above topics, authors can also select and target the following
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
Submission Guidelines
Paper submissions should be no longer than 8 pages, in the IEEE 2-column format, including the bibliography and any possible appendices. Submissions longer than 8 pages will be rejected without review. All submissions will be reviewed by the Program Committee based on technical quality, originality, significance, and clarity. For survey track paper, please preface the descriptive paper title with “Survey:”, followed by the actual paper title. For example, a paper entitled “A Literature Review of Streaming Knowledge Graph”, should be changed as “Survey: A Literature Review of Streaming Knowledge Graph”. This is for the reviewers and chairs to clearly bid and handle the papers. Once the paper is accepted, the word, such as “Survey:”, can be removed from the camera-ready copy.
For special track paper, please preface the descriptive paper title with “SS##:”, where “##” is the two digits special track ID. For example, a paper entitled “Incremental Knowledge Graph Learning”, intended to target Special Track 01 (Machine learning and knowledge graph) should be changed as “SS01: Incremental Knowledge Graph Learning”.
All manuscripts are submitted as full papers and are reviewed based on their scientific merit. The reviewing process is single blind, meaning that each submission should list all authors and affiliations. There is no separate abstract submission step. There are no separate industrial, application, or poster tracks. Manuscripts must be submitted electronically in the online submission system. No email submission is accepted.To help ensure correct formatting, please use the style files for U.S. Letter as template for your submission. These include LaTeX and Word.Key DatesImportant Dates of the Conference.
With very best regards! Huajun
On behalf of the OC committe of ICKG2024
