CfP: eKNOW 2026 || May 24 - 28, 2026 - Venice, Italy

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 2026, The Eighteenth International Conference on Information, Process, and Knowledge Management

eKNOW 2026 is scheduled to be May 24 - 28, 2026 in Venice, Italy under the DigitalWorld 2026 umbrella.

The submission deadline is February 7, 2026.

Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: https://www.iariajournals.org

All events will be held in a hybrid mode: on site, online, prerecorded videos, voiced presentation slides, pdf slides.

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============== eKNOW 2026 | Call for Papers ===============

CALL FOR PAPERS, TUTORIALS, PANELS


eKNOW 2026, The Eighteenth International Conference on Information, Process, and Knowledge Management

General page: https://www.iaria.org/conferences2026/eKNOW26.html

Submission page: https://www.iaria.org/conferences2026/SubmiteKNOW26.html


Event schedule: May 24 - 28, 2026


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: February 7, 2026


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 Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.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.
Before submission, please check and comply with the editorial rules: https://www.iaria.org/editorialrules.html


eKNOW 2026 Topics (for topics and submission details: see CfP on the site)

Call for Papers: https://www.iaria.org/conferences2026/CfPeKNOW26.html

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eKNOW 2026 Tracks (topics and submission details: see CfP on the site)

Knowledge acquisition with AI-based tools

AI-tools for knowledge extraction structured knowledge from unstructured data; Semantic analysis and AI for for deep semantic analysis; AI-based enhancing knowledge retrieval and understanding; AI-tools for machine learning for pattern recognition; AI in ontology and data modeling; AI tools for the creation and management of ontologies; Natural language processing for knowledge discovery; AI-enhanced learning repositories; AI systems to manage and optimize access to digital knowledge repositories; AI-tools in data governance; Integration of AI with traditional knowledge systems; AI-tools enhancing data interpretation and utility; Ethical implications of AI in knowledge acquisition for biases, privacy concerns, and ethical issues

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 2026 Committee: https://www.iaria.org/conferences2026/ComeKNOW26.html


IARIA Ambassadors

Steve Chan, Decision Engineering Analysis Laboratory, USA
Dirceu Cavendish, Kyushu Institute of Technology, Japan
Monika Maria Moehring, Study Centre for Blind and Disabled Students, Technische Hochschule Mittelhessen, Gie en Germany
Carlos Becker Westphall, Federal University of Santa Catarina, Brazil
Lasse Berntzen, University of South-Eastern Norway, Norway
Les Sztandera, Thomas Jefferson University, Philadelphia, USA
Andreas Rausch, TU Clausthal, Clausthal-Zellerfeld, Germany
Timothy Phan, NASA, Jet Propulsion Laboratory, USA
Manuela Vieira, CTS/ISEL/IPL, Portugal
Luigi Lavazza, Universit dell'Insubria - Varese, Italy