Graph Based Relational Features for Collective Classification
2015-05-09, Bayer, Immanuel, Nagel, Uwe, Rendle, Steffen
Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.
Bisociative Discovery of Interesting Relations between Domains
2011, Nagel, Uwe, Thiel, Kilian, Kötter, Tobias, Piatek, Dawid, Berthold, Michael R.
The discovery of surprising relations in large, heterogeneous information repositories is gaining increasing importance in real world data analysis. If these repositories come from diverse origins, forming different domains, domain bridging associations between otherwise weakly connected domains can provide insights into the data that can otherwise not be accomplished. In this paper, we propose a first formalization for the detection of such potentially interesting, domain-crossing relations based purely on structural properties of a relational knowledge description.
Pure spreading activation is pointless
2009, Berthold, Michael R., Brandes, Ulrik, Kötter, Tobias, Mader, Martin, Nagel, Uwe, Thiel, Kilian
Almost every application of spreading activation is accompanied by its own set of often heuristic restrictions on the dynamics. We show that in constraint-free scenarios spreading activation would actually yield query-independent results, so that the specific choice of restrictions is not only a pragmatic computational issue, but crucially determines the outcome.