Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Discovering informative connection subgraphs in multi-relational graphs
ACM SIGKDD Explorations Newsletter
Processing-in-memory technology for knowledge discovery algorithms
DaMoN '06 Proceedings of the 2nd international workshop on Data management on new hardware
Mining for offender group detection and story of a police operation
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Hi-index | 0.01 |
Link discovery is a new challenge in data mining whose primary concerns are to identify strong links and discover hidden relationships among entities and organizations based on low-level, incomplete and noisy evidence data. To address this challenge, we are developing a hybrid link discovery system called KOJAK that combines state-of-the-art knowledge representation and reasoning (KR&R) technology with statistical clustering and analysis techniques from the area of data mining. In this paper we report on the architecture and technology of its first fully completed module called the KOJAK Group Finder. The Group Finder is capable of finding hidden groups and group members in large evidence databases. Our group finding approach addresses a variety of important LD challenges, such as being able to exploit heterogeneous and structurally rich evidence, handling the connectivity curse, noise and corruption as well as the capability to scale up to very large, realistic data sets. The first version of the KOJAK Group Finder has been successfully tested and evaluated on a variety of synthetic datasets.