Call for Papers:

China Conference on Knowledge Graph and Semantic Computing (CCKS2017)

August 26-29, 2017, Cheng Du, China

Submission deadline: May 25,2017

CCKS2017 (China Conference on Knowledge Graph and Semantic Computing, is organized by the Technical Committee on Language and Knowledge Computing ( of The Chinese Information Processing Society of China (, and is the combination of two conferences: the Chinese Knowledge Graph Symposium (CKGS) and Chinese Semantic Web and Web Science Conference (CSWS).  CCKS2016 took place at Xijiao Hotel Beijing, and more than 400 researchers from academia and industry attended the meeting. The theme of CCKS2016 was “Semantic, Knowledge, and Big Linked Data”.

CCKS2017 will be hold on August 26th, 2017, Chendu China. It’s theme is “Language, Knowledge, and Intelligence” discussing key technologies and applications of big data, language understanding, knowledge acquisition, and intelligent service.

The conference welcomes different types of contributions describing new concepts, innovative research work, standards, implementations and experiments, applications, and industrial case studies. Authors are invited to submit complete and unpublished papers, which are not under review in any other conference or journal.

The submissions can be either English or Chinese. All accepted English submissions will be included in an English proceeding published by Springer. Accepted high quality Chinese submissions will be recommended to major Chinese journals such as Journal of Chinese Information Processing (, and Journal of Pattern Recognition and Artificial Intelligence ( , Elsevier Journal of Big Data Research, etc.

All submissions need to go to the submission website:

Important dates

  • Abstract due:  2017, May 18th
  • Paper due: 2017, May 25th
  • Notification: 2017, July 9th
  • Final version Due: 2017,  July 25th
  • Conference: 2017, Aug 26th-29th

Relevant topics of CCKS include, but are not limited to, the following ones:

** Knowledge Representation / Ontology Modeling

– Knowledge representation learning/ Knowledge graph embedding

– Distributional representation of knowledge

– Schema induction for knowledge graph

– Concept learning from text

– Ontology modeling, reuse, and evolution·

– Ontology mapping, merging, and alignment·

– Ontology evaluation

– New formalisms (such as probabilistic approaches)·

** Knowledge Graph Construction / Information Extraction

– Supervised, unsupervised, lightly-supervised and distantly-supervised learning from text

– Open information extraction

– Naturally-available data

– Human-computer collaboration in knowledge base construction;

– Automated population of wikis

– Machine reading

– Languages, toolkits and systems for automated knowledge base construction

– Dynamic data, online/on-the-fly adaptation of knowledge

** Semantic integration

– Entity recognition, disambiguation and linking

– Taxonomy integration

– Structure integration

– Heterogeneous knowledge base integration

– Cross lingual knowledge linking and integration

– Ontology based data integration

** Knowledge storage and indexing

– Distributed knowledge base systems;

– Probabilistic knowledge bases

– Scalable computation; distributed computation

– Querying under uncertainty

– Searching and ranking ontologies·

** Knowledge sharing, reuse and knowledge based system

– Knowledge visualization

– Semantic search

– Knowledge based Q&A

– Intelligent personal assistant system

– Semantic analysis of natural language/audio/video/image

– Demonstrations of existing automatically-built knowledge bases

– Queries on mixtures of structured and unstructured data; querying under uncertainty

– Semantic in big data/social web

** Knowledge inference

– Knowledge rule learning

– Knowledge completion

– Knowledge validation

– Reasoning over Semantic Web data

– Document entailment

– Logic-based, embedding-based and other methods for knowledge reasoning

– Representing and reasoning about trust, privacy, and security·

** Linked Data

– Publication of Linked Data·

– Consumption of Linked Data·

– Reasoning with Linked Data·

– Search, query, integration, and analysis on Linked Data·

– Integration and mash-up of Linked Data·

– Mining of Linked Data

– Domain specific applications (e-Government, disaster, life science etc.)

Invited Speaker:

  • Amit Sheth, Wright State University; Executive Director, Kno.e.sis
  • Jun Zhao, Institute of Automation, Chinese Academy of Sciences, Beijing


  • Ni Lao, Search, Google Inc.
  • Kang Liu, Automation Institute, China Academy of Science
  • Lei Zou, Institute of Computer Science and Technology, Peking University
  • Haofen Wang, Gowild Robotics Co. Ltd

Organizer: the Technical Committee on Language and Knowledge Computing ( of The Chinese Information Processing Society of China (

Co-organizer: Xihua University

Organization Committee

Conference General Chairs:

  • Juanzi LI, Tsinghua University
  • Ming ZHOU, Microsoft Institute Asia

Program Committee Chairs:

  • Guilin QI, SouthEast University
  • Ni LAO, Carnegie Mellon University

Tutorial Chairs:

  • Bing QING, Harbin Institute of Technology
  • Zhiyuan LIU, Tsinghua University

Industry Forum Chairs:

  • Jun YANG, Microsoft Institute Asia
  • Dianxia XIE, Haizhi Inteligence

Evaluation Chairs:

  • Zhichun WANG, Beijing Normal University
  • Yanghua XIAO, Fudan University

Poster/Demo Chairs:

  • Huajun CHEN, Zhejiang University
  • Kang LIU, Automation Institute,China Academy of Science

Local Chairs:

  • Yajun DU, Xihua University
  • Yongquan FAN, Xihua University

Sponsorship Chairs:

  • Tieyun QIAN, Wuhan University
  • Wei HU, Nanjing University

Publication Chairs:

  • Tong RUAN, East China University of Science and Technology
  • Jianfeng DU, Guangdong University of Foreign Studies

Publicity Chairs:

  • Xiaowang ZHANG, Tianjin University
  • Haofen WANG, East China University of Science and Technology

Top conference review Chairs:

  • Quan WANG, Institut of Information Engineering, CAS
  • Gong CHENG, Nanjing University

Area Chairs :

  • Gong CHENG, Nanjing University
  • Jianfeng DU, Guangdong University of Foreign Studies
  • Yansong FENG, Peking University
  • Yu HONG, Suzhou University
  • Zhiyuan LIU, Tsinghua University
  • Gerard de Melo, Tsinghua University
  • Yao MENG, Fujitsu
  • Jeff PAN, Aberdeen University
  • Guilin QI, Southeast University
  • Bin QIN, Harbin Institute of Technology
  • Xipeng QIU, Fudan University
  • Quan WANG, • China Academy of Science
  • Xin WANG, Tianjin University
  • Gang WU, Northeastern University