eKNOW 2021 - The Thirteenth International Conference on Information, Process, and Knowledge Management

INVITATION
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Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results to:
- eKNOW 2021, The Thirteenth International Conference on Information, Process, and Knowledge Management
eKNOW 2021 is scheduled to be July 18 - 22, 2021 in Nice, France under the DigitalWorld 2021 umbrella.
The submission deadline is April 19, 2021.
Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: https://www.iariajournals.org
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=== eKNOW 2021 | Call for Papers ===
CALL FOR PAPERS, TUTORIALS, PANELS
eKNOW 2021, The Thirteenth International Conference on Information, Process, and Knowledge Management
General page: https://www.iaria.org/conferences2021/eKNOW21.html
Submission page: https://www.iaria.org/conferences2021/SubmiteKNOW2...
Event schedule: July 18 - 22, 2021
Contributions:
- regular papers [in the proceedings, digital library]
- short papers (work in progress) [in the proceedings, digital library]
- ideas: two pages [in the proceedings, digital library]
- extended abstracts: two pages [in the proceedings, digital library]
- posters: two pages [in the proceedings, digital library]
- posters: slide only [slide-deck posted at www.iaria.org]
- presentations: slide only [slide-deck posted at www.iaria.org]
- demos: two pages [posted at www.iaria.org]
Submission deadline: April 19, 2021
Extended versions of selected papers will be published in IARIA Journals: https://www.iariajournals.org
Print proceedings will be available via Curran Associates, Inc.: https://www.proceedings.com/9769.html
Articles will be archived in the free access ThinkMind Digital Library: https://www.thinkmind.org
The topics suggested by the conference can be discussed in term of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.
All tracks are open to both research and industry contributions, in terms of Regular papers, Posters, Work in progress, Technical/marketing/business presentations, Demos, Tutorials, and Panels.
Before submission, please check and comply with the editorial rules: https://www.iaria.org/editorialrules.html
eKNOW 2021 Topics (for topics and submission details: see CfP on the site)
Call for Papers: https://www.iaria.org/conferences2021/CfPeKNOW21.h...
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eKNOW 2021 Tracks (topics and submission details: see CfP on the site)
Knowledge fundamentals
Knowledge acquisition, processing, and management; Linguistic knowledge representation; Knowledge modeling and virtualization; Types of knowledge: structural, behavioral, relationships, etc.; Knowledge representation: visual-picture, connectionist model, semi-structured [a la workflow], structured/formal; Knowledge acquisition status: potential new knowledge, guessed semantics, confirmed semantics, auditing confirmed semantics, etc.; Knowledge update: probable insertion, validated insertion, auditing the insertion periodically based on new knowledge, etc.
Advanced topics in Deep/Machine learning
Distributed and parallel learning algorithms; Image and video coding; Deep learning and Internet of Things; Deep learning and Big data; Data preparation, feature selection, and feature extraction; Error resilient transmission of multimedia data; 3D video coding and analysis; Depth map applications; Machine learning programming models and abstractions; Programming languages for machine learning; Visualization of data, models, and predictions; Hardware-efficient machine learning methods; Model training, inference, and serving; Trust and security for machine learning applications; Testing, debugging, and monitoring of machine learning applications; Machine learning for systems.
ML: Knowledge and Information processing using Machine Learning
Machine learning models (supervised, unsupervised, reinforcement, constrained, etc.); Generative modeling (Gaussian, HMM, GAN, Bayesian networks, autoencoders, etc.); Explainable AI (feature importance, LIME, SHAP, FACT, etc.); Bayesian learning models; Prediction uncertainty (approximation learning, similarity); Training of models (hyperparameter optimization, regularization, optimizers); Active learning (partially labels datasets, faulty labels, semi-supervised); Applications of machine learning (recommender systems, NLP, computer vision, etc.); Data in machine learning (no data, small data, big data, graph data, time series, sparse data, etc.)
Trends on annotation and extraction
Natural Languages-based features and systems; Annotation handling (multilingual, semantic, shared, open, prosody, etc.); Annotation as a Service (AaaS); Handling argument-based knowledge; Event-based knowledge; Tagging and supertagging; Extraction patterns; Uncertain reasoning; Visual error analysis; Domain-specific paraphrase extraction; Tweets and sentence compression; Role labeling semantic; Heterogeneous annotations
Trends on news and social media
New events-based knowledge;In-context news creation; Superlative expressions; News highlights generation systems; News special summarization systems (e.g, for blind and/or visually impaired people); Sentiment classification (emotion, irony, sarcasm, rhetorical questions, opinion, etc.); Rumor dynamics and social media; Contextual pragmatic models;; Social prediction; Prediction semantic analysis; Predictability of distrust; Aspect-based sentiment analysis; Argument generation systems; Relevance of citation recommendation; Retrieval bias and retrieval performance; High-speed captioning images; Language models for images; Participative KM platforms
Trends on knowledge processing support and mechanisms
Open knowledge bases; Structured knowledge bases; Big knowledge applications; Linked knowledge objects; Knowledge datasets; Machine translation systems; Convolution neural networks; Hybrid representations and equivalent semantics; Processing bilingual information; Topic trends and temporal signatures; Cross-view features; Pattern-based knowledge; Ranking optimization in context; Concept-based classification and ranking; KM design for life long learning and long term uses
Knowledge identification and discovery
Mining for knowledge; Knowledge identification: semantic-ID, etc.; Knowledge discovery: how to express knowledge requests?, how to find knowledge?, etc.; Knowledge refinement: after many acquisitions, former knowledge can change semantically or structurally, etc.; Knowledge clustering
Knowledge management systems
Knowledge data systems; Industrial systems; Context-aware and self-management systems; Imprecision/Uncertainty/Incompleteness in databases; Cognitive science and knowledge agent-based systems; Databases and mobility in databases; Zero-knowledge systems; Expert systems; Tutoring systems; Digital libraries
Knowledge management (KM) and event processing (EP)
Methodologies and approaches to overcome technical hurdles and improve the interplay between KM and EP; Applications from various domains (e.g. financial, manufacturing, trading, telecommunication, service), which benefit from an integrated KM and EP
Knowledge semantics processing and ontology
Dynamic knowledge ontology; Collaborative knowledge ontology; Knowledge matching; Contextual reasoning; Tools for knowledge ontology; Context-based information extraction; Knowledge trading systems; Knowledge exchange portals; Cognitive sytems and knowledge processing; Human aspects in knowledge processing
Technological foresight and socio-economic evolution modelling
Anticipatory networks and decisions; Expert information management; Foresight support systems; Generating technological recommendations and rankings; Information society evolution; Online and real-time Delphi; Ontological knowledge bases of technologies and products; Roadmapping support systems; Strategic support systems; Technological information fusion; Technological policy decision support systems
Process analysis and modeling
Analysis and development of business architectures; Data mining and information retrieval for business processes; Business process modelling; Business process composition; Analysis and management lifecycle; Reasoning on business processes; Optimization of business processes; Adaptive business processes; Business process reengineering; Integration of processes; Process discovery; Business process quality; Resource allocation
Process management
Criteria for measurement of business process models; Monitoring business processes; Business process visualization; Management of business process integration; On-demand business transformation; Performance measurement; Conformance and risk management; Prediction; Business transformation; Packaged industry applications; Industry solutions
Information management
Informational mining/retrieval/classification; Geographic and spatial data Infrastructures; Information technologies; Information management systems; Information ethics and legal evaluations; Optimization and information technology; Organizational information systems
Decision support systems
Multi-criteria decision theory; Artificial intelligence; Adaptive design for decision support systems; Support technologies: knowledge-driven, data-driven, model-driven, and geographically-driven systems; Support methods: artificial neural networks, fuzzy logic, and genetic/evolutionary algorithms; Modeling, interfaces, and performance; Applications using decision support systems
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eKNOW series Committee: https://www.iaria.org/conferences2021/ComeKNOW21.h...
eKNOW 2021 Publicity Chairs
Alvaro Liebana, Universitat Politecnica de Valencia, Spain
Mar Parra, Universitat Politecnica de Valencia, Spain
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