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KDAH-CIKM 2020 : Third Workshop on Knowledge-Driven Analytics and systems impacting Human Quality of Life

Virtual
Event Date: Oct 19, 2020 - Oct 23, 2020
Submission Deadline: May 15, 2020
Notification of Acceptance: Jul 17, 2020
Camera Ready Version Due : Aug 17, 2020

About

The Conference on Information and Knowledge Management (CIKM), founded in 1992, is a key calendar event each year for the international academic, business and government communities for presentation and discussion of research on information retrieval, data science and knowledge management.

The theme for CIKM in 2020 is Data and knowledge for the next generation: sustainability, transparency and fairness.

Call For Paper

We encourage submissions of high-quality research papers on all topics in the general areas of artificial intelligence, databases, information retrieval, and knowledge management. Topics of interest include, but are not limited to, the following areas:

  • Data and information acquisition and preprocessing (e.g., data crawling, data quality, data privacy, mitigating biases, and data wrangling)
  • Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, (knowledge) graphs, data warehousing, privacy and security, modelling, information credibility)
  • Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customised hardware)
  • Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, understanding, and interpretability)
  • Neural Information and knowledge processing (e.g. graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)
  • Information access and retrieval (e.g., facets and entities, web search, question answering, and dialogue systems, retrieval models, query processing, personalization, recommender and filtering systems)
  • Special data processing (e.g., multilingual text, sequential, stream, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data)
  • Understanding multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations)
  • Data presentation (e.g., visualization, summarization, readability, understandability, transparency, VR, speech input and output)
  • Users and interfaces for information and data systems (e.g., user behaviour analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, spoken interfaces)
  • Crowdsourcing (e.g. task assignment, worker reliability, optimisation, trustworthiness, transparency, best practices)
  • Comparative evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices)

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