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Nowadays data are continuously created, even if we never notice it is happening. Whenever we sign up for a shopping card, place a purchase using a credit card, or surf the Web, data are created and stored in large sets on powerful computers owned by the companies we deal with every day. With the increasing availability of data, novel tools and systems able to provide effective means of searching and retrieving information are required. Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from data, often based on underlying large data sets. A major aspect of Knowledge Discovery is Data Mining, the process for discovering valuable knowledge and information from data, is widespread in numerous fields, including science, engineering, healthcare, business, and medicine. In this scenario, Information Retrieval enables the reduction of the so-called "information overload". Information Retrieval 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, whose popular use as global information system led to a huge amount of digital data. Hence, there is the need of new techniques and systems able to easily extract information and knowledge from the Web.

Challenges imposed by the large scale of Web Data, Semantic Web, and Linked Data are leading to the adoption of useful tools based on semantic nets, ontologies, or taxonomies. In particular, taxonomies are becoming indispensable to support the mining and retrieval systems, as organizing digital items into hierarchies can help to better understand the information being extracted from data.

KDWeb 2015 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 will contribute to discuss and compare suitable novel solutions based on intelligent techniques and applied in real-world applications.

We particularly encourage to submit abstracts and papers focused on the fields of hierarchical text categorization and taxonomy building and assessment.

A selection of accepted papers will be published in a special issue of scientific journals.
- Papers related to data semantics will be published in a special issue of the “Journal of Data Semantics”.
- The other selected journal will be announced as soon as possible.