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eKNOW 2023: International Conference on Information, Process, and Knowledge Management

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投稿締切日:
2023-02-01 締切済み
通知日:
2023-02-28
開催日:
2023-04-24
開催地:
Venice, Italy
開催回数:
15
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eKNOW
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論文募集

eKNOW 2023 (International Conference on Information, Process, and Knowledge Management) is an academic conference held in Venice, Italy on 2023-04-24. The paper submission deadline is 2023-02-01 (extended). Acceptance notifications are sent on 2023-02-28.

The variety of the systems and applications and the heterogeneous nature of information and knowledge representation require special technologies to capture, manage, store, preserve, interpret and deliver the content and documents related to a particular target. Progress in cognitive science, knowledge acquisition, representation, and processing helped to deal with imprecise, uncertain or incomplete information. Management of geographical and temporal information becomes a challenge, in terms of volume, speed, semantic, decision, and delivery. Information technologies allow optimization in searching an interpreting data, yet special constraints imposed by the digital society require on-demand, ethics, and legal aspects, as well as user privacy and safety. Nowadays, there is notable progress in designing and deploying information and organizational management systems, experts systems, tutoring systems, decision support systems, and in general, industrial systems. Capturing, representing, and manipulating knowledge was and still is a fascinating and extremely useful challenge from both theoretical and practical perspective. Using validated knowledge for information and process management, and for decision support mechanisms raises a series of questions the conference is aimed at. We solicit both academic, research, and industrial contributions. We welcome technical papers presenting research and practical results, position papers addressing the pros and cons of specific proposals, such as those being discussed in the standard fora or in industry consortia, survey papers addressing the key problems and solutions on any of the above topics short papers on work in progress, and panel proposals. Industrial presentations are not subject to the format and content constraints of regular submissions. We expect short and long presentations that express industrial position and status. Tutorials on specific related topics and panels on challenging areas are encouraged. 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 topics and submission formats are open to both research and industry contributions. eKNOW 2023 conference tracks: 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
最終更新：Dou Sun（）

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International Journal of Data Mining & Knowledge Management ProcessAIRCC2231-007X
Information Technology and Management2.9Springer1385-951X
BJournal of Software: Evolution and Process1.8Wiley2047-7481
AIEEE Transactions on Multimedia9.7IEEE1520-9210
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BSoftware & Systems Modeling3.2Springer1619-1366
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CFuture Generation Computer Systems5.9Elsevier0167-739X

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