Knowledge discovery is an 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 or 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 are posing challenges to 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 the Linked Data and Knowledge Discovery communities, and to explore novel directions for joint research. On the one hand, we wish to stimulate a discussion about how state-of-the-art algorithms for knowledge discovery and data mining can be adapted to fit the characteristics of Linked Data, such as its distributed nature, incompleteness (incl. absence of negative examples), and identify concrete use cases and applications. On the other hand, we hope to show that Linked Data can support traditional knowledge discovery tasks (e.g., as a source of additional background knowledge and of predictive features) for mining from existing, not natively linked data like, for instance, in business intelligence settings.
The workshop addresses researchers and practitioners from the fields of knowledge discovery in databases and data mining, as well as researchers from the Semantic Web community applying such techniques to Linked Data. The goal of the workshop is to provide a platform for knowledge exchange between the different research communities, and to foster future collaborations. We expect at least 30 participants. 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.
This workshop continues a successful series of past events. It follows the first four editions of Know@LOD at ESWC 2012, 2013, 2104, and 2015, each of which were attended by 25 or more participants, respectively, as well as the Data Mining on Linked Data (DMoLD) workshop, which was held at ECML/PKDD 2013 with around 40 participants.
Besides a track for research papers and a keynote talk, the workshop will host the fourth Linked Data Mining Challenge. The proceedings of the workshop including research and challenge papers will be published with CEUR-WS.
Aachen, Germany: Rheinisch-Westfaelische Technische Hochschule Aachen University , 2016.
1st International Workshop on Completing and Debugging the Semantic Web, Heraklion, Greece, May 30th, 2016