Knowledge graph is essentially the knowledge base of semantic web, which is composed of entities (nodes) and relations (edges). As the representation of semantics, knowledge graphs can readily-easily formulate real-world entities, concepts, attributes, as well as their relations. All the specific features of knowledge graphs make it born with strong expressive ability and flexible modelling ability. At the same time, as a special kind of graph data, knowledge graphs are both human-readable and machine-friendly. With effective knowledge representation approaches, a variety of tasks can be resolved, including knowledge extraction, knowledge integration, knowledge management, and knowledge applications. Therefore, knowledge graphs have been applied in various domains such as information retrieval, natural language understanding, question answering systems, recommender systems, financial risk control, etc. However, new challenges have emerged in the context of knowledge graphs from many perspectives including scalability, explainability, robustness, etc.

The track will bring together researchers and practitioners to discuss the fundamentals, methodologies, techniques, and applications of knowledge graphs. In this track, our goal is to contribute to the next generation of knowledge graphs and exploring them using artificial intelligence, data science, machine learning, network science, and other appropriate technologies.

Topics of interest include but not limited to:

• Foundations and understanding of knowledge graphs

• Models and algorithms for knowledge graph construction and representation

• Large-scale graph algorithms and theories

• AI for/over knowledge graphs

• Misinformation or disinformation

• Privacy and security

• Fairness, transparency, explainability, and robustness

• Datasets and benchmarking

• Knowledge graphs in various domains

• Innovative applications of knowledge graphs

IMPORTANT DATES

• Submission deadline: October 24, 2021 [extended]

• Notification to authors: December 10, 2021

• Camera-ready copies of accepted papers: December 21, 2021

• Author registration due date: December 21, 2021

SUBMISSION INSTRUCTIONS

Authors are invited to submit original and unpublished papers of research and applications for this track. The author(s) name(s) and address(es) must not appear in the body of the paper, and self-reference should be in the third person. This is to facilitate double-blind review.

SAC 2022 accepts 1) Regular (Full) Papers, 2) Posters, and 3) Student Research Competition (SRC) Abstracts.

Full papers are limited to 8 pages with the option for up to 2 additional pages. Posters are limited to 3 pages with the option for up to 1 additional page. Student Research Abstracts are limited to 4 pages, with no additional pages.

Submissions must be formatted according to the ACM SAC template. For detailed instructions for authors, see the Author Kit on the ACM SAC 2022 website (https://www.sigapp.org/sac/sac2022/ ).

Please submit your papers via the SAC 2022 submission system. When submitting, select Track on Knowledge Graphs.

Extended versions of top-quality papers accepted and presented at the conference will be recommended for publication in Data Intelligence (MIT Press).

TRACK CHAIRS

PROGRAM COMMITTEE MEMBERS

Simone Angioni, University of Cagliari, Italy

Nana Yaw Asabere, Accra Technical Univerity, Ghana

Xiaomei Bai, Anshan Normal University, China

Russa Biswas, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany

Davide Buscaldi, University Sorbonne Paris Nord, France

Lianhua Chi, La Trobe University, Australia

Claudia d’Amato, University of Bari, Italy

Enrico Daga, The Open University, UK

Danilo Dessi, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany

Giuseppe Futia, Polytechnic University of Turin, Italy

Genet Asefa Gesese, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany

Pouya Ghiasnezhad Omran, Australian National University, Australia

Armin Haller, Australian National University, Australia

Inma Hernández, University of Seville, Spain

Mayank Kejriwal, University of Southern California, USA

Jiaying Liu, Dalian University of Technology, China

Andrea Mannocci, ISTI-CNR, Italy

Eduardo Mena, University of Zaragoza, Spain

Diego Reforgiato Recupero, University of Cagliari, Italy

Angelo Salatino, The Open University, UK

Luca Secchi, Linkalab, Italy

Gerardo Simari, National University of the South, Argentina

Suppawong Tuarob, Mahidol University, Thailand

Sahar Vahdati, Institute for Applied Informatics, Germany

Jingbo Wang, Australian National University, Australia

Wei Wang, Sun Yat-sen University, China

Zhe Wang, Griffith University, Australia

Fouad Zablith, American University of Beirut, Lebanon

Xiaowang Zhang, Tianjin University, China

Xiang Zhao, National University of Defense Technology, China

ACM SAC 2022 Track on Knowledge Graphs