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CCKS 2016 : China Conference on Knowledge Graph and Semantic Computing

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Link: http://www.ccks2016.cn/index.html
 
When Sep 19, 2016 - Sep 22, 2016
Where Beijing
Abstract Registration Due Jun 23, 2016
Submission Deadline Jul 1, 2016
Notification Due Aug 1, 2016
Final Version Due Aug 19, 2016
Categories    knowledge graph   semantic web   artificial intelligence   natural language processing
 

Call For Papers

The theme of this edition is “Semantic, Knowledge, and Big Linked Data". The format of the conference consists of pre-conference tutorials,evaluation and challenging workshops, keynotes, regular papers sessions, poster/demo sessions etc.. Particularly, we will organize a special industry forum for practitioners coming from industries to share their experiences and ideas.

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 Authors are invited to submit complete and unpublished papers, which are not under review in any other conference or journal.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 conference welcomes submissions in either English or Chinese. All accepted English submissions would be included in an English proceeding published by Springer. Accepted high quality Chinese submissions will be recommended to main Chinese journals. Selected high quality articles will be also recommended to Journal of Web Semantics, Elsevier Journal of Big Data Research, etc.

All submissions need to go to the submission website: https://easychair.org/conferences/?conf=ccks2016

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.)

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