Know@LOD 2013 - Second International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data

After last year’s successful debut, the second international workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know-AT-LOD) will be held at the 10th Extended Semantic Web Conference (ESWC).
Knowledge discovery and data mining (KDD) is a well-established field with a large community investigating methods for the discovery of patterns and regularities in large data sets, including relational databases and unstructured text. Research in this field has led to the development of practically relevant and scalable approaches such as association rule mining, subgroup discovery, graph mining, and clustering. At the same time, the Web of Data has grown to one of the largest publicly available collections of structured, cross-domain data sets. While the growing success of Linked Data and its use in applications, e.g., in the e-Government area, has provided numerous novel opportunities, its scale and heterogeneity is posing challenges to the field of knowledge discovery and data mining:
The extraction and discovery of knowledge from very large data sets;
The maintenance of high quality data and provenance information;
The scalability of processing and mining the distributed Web of Data; and
The discovery of novel links, both on the instance and the schema level.
Contributions from the knowledge discovery field may help foster the future growth of Linked Open Data. Some recent works on statistical schema induction, mapping, and link mining have already shown that there is a fruitful intersection of both fields. With the proposed workshop, we want to investigate possible synergies between both the Linked Data community and the field of Knowledge Discovery, and to explore novel directions for mutual research. We wish to stimulate a discussion about how state-of-the-art algorithms for knowledge discovery and data mining could be adapted to fit the characteristics of Linked Data, such as its distributed nature, incompleteness (i.e., absence of negative examples), and identify concrete use cases and applications.
Authors of contributed papers are especially encouraged to publish their data sets and/or the implementation of their algorithms, and to discuss these implementations and data sets with other attendees. The goal is to establish a common benchmark that can be used for competitive evaluations of algorithms and tools.
A selection of the best papers of each ESWC workshops will be included in an additional volume edited by Springer.