At the AKSW Colloquium on Friday 7th of July, at 10:40 AM there will be two paper presentations concerning genetic algorithms to learn linkage rules, and differentiable learning of logical rules for knowledge base reasoning.
Tommaso Soru will present the paper Differentiable Learning of Logical Rules for Knowledge Base Reasoning, currently a pre-print, by Fan Yang, Zhilin Yang, and William W. Cohen.
Abstract
“We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog, where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method obtains state-of-the-art results on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.”
Daniel Obraczka will present the paper Learning Expressive Linkage Rules using Genetic Programming of Isele and Bizer accepted at VLDB 2012. This work presents an algorithm to learn record linkage rules utilizing genetic programming.
Abstract
“A central problem in data integration and data cleansing is to find entities in different data sources that describe the same real-world object. Many existing methods for identifying such entities rely on explicit linkage rules which specify the conditions that entities must fulfill in order to be considered to describe the same real-world object. In this paper, we present the GenLink algorithm for learning expressive linkage rules from a set of existing reference links using genetic programming. The algorithm is capable of generating linkage rules which select discriminative properties for comparison, apply chains of data transformations to normalize property values, choose appropriate distance measures and thresholds and combine the results of multiple comparisons using non-linear aggregation functions. Our experiments show that the GenLink algorithm outperforms the state-of-the-art genetic programming approach to learning linkage rules recently presented by Carvalho et. al. and is capable of learning linkage rules which achieve a similar accuracy as human written rules for the same problem.”
About the AKSW Colloquium
This event is part of a series of events about Semantic Web technology. Please see http://wiki.aksw.org/public/colloquium for further information about previous and future events. As always, Bachelor and Master students are able to get points for attendance and there is complimentary coffee and cake after the session.
