Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from such data, and currently is widespread in numerous fields, including science, engineering, healthcare, business, and medicine. Recently, the rapid growth of social networks and online services entailed that Knowledge Discovery approaches focused on the World Wide Web (WWW), whose popular use as global information system led to a huge amount of digital data. KDWeb 2021 is focused on the field of Knowledge Discovery from digital data, with particular attention for Data Mining, Machine Learning, and Information Retrieval methods, systems, and applications. KDWeb 2020 is aimed at providing a venue to researchers, scientists, students, and practitioners involved in the fields of Knowledge Discovery on Data Mining, Information Retrieval, and Semantic Web, for presenting and discussing novel and emerging ideas. KDWeb 2021 will contribute to discuss and compare suitable novel solutions based on intelligent techniques and applied in real-world applications. The workshop is hosted by the 21th International Conference on Web Engineering (ICWE 2021). For more information see the Call for Papers
In the current era of digital and social data, the world became more connected, networked, and traceable, with the consequent exponentially growth of data creation, sharing, and storing. In particular, data changed from static, complete, and centralized to dynamic, incomplete, and distributed; furthermore, data rapidly increased its scope and size, with the continuous increase of volumes, varieties, and velocities. All these aspects led to new challenges undertaken by the field of Big Data Analysis. Consequently, there is the need for novel computational techniques and tools able to assist humans in extracting useful information (knowledge) from the huge volumes of data. Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from such data, and is currently widespread in numerous fields, including science, engineering, healthcare, business, and medicine. A major aspect of Knowledge Discovery is to extract valuable knowledge and information from data. Typical tasks are aimed at gathering only relevant information from digital data (e.g., text documents, multimedia files, or webpages), by searching for information within documents and for metadata about documents, as well as searching relational databases and the Web.
Recently, the rapid growth of social networks and online services entailed that Knowledge Discovery approaches focused on the World Wide Web (WWW), whose popular use as global information system led to a huge amount of digital data. Typically, a webpage has unstructured or semi-structured textual content, leading to present to users both relevant and irrelevant information. Hence, there is the need of novel techniques and systems able to easily extract information and knowledge from the huge web data.
KDWeb 2021 is aimed at providing a venue to researchers, scientists, students, and practitioners involved in the fields of Knowledge Discovery on Data Mining, Information Retrieval, and Semantic Web, for presenting and discussing novel and emerging ideas. KDWeb 2021 will contribute to discuss and compare suitable novel solutions based on intelligent techniques and applied in real-world applications.
The workshop welcomes submissions of fresh investigations concerning experimental and applied studies on web Knowledge Discovery. The topics include but are not limited to:
(Department of Mathematics and Computer Science -University of Cagliari, Italy)
(Department of Computer Science -University of Verona, Italy)
(CITERA Interdepartmental Centre, Sapienza University of Rome, Italy)