Linked Data Mining Challenge (LDMC) 2013 Summary
نویسندگان
چکیده
The paper summarizes the conception, data preparation and result evaluation of the LDMC, which has been organized in connection with the DMoLD’13 Data Mining on Linked Data Workshop, Prague, September 23 (as part of the ECML/PKDD conference program).
منابع مشابه
The Linked Data Mining Challenge 2014: Results and Experiences
The 2014 edition of the Linked Data Mining Challenge, conducted in conjunction with Know@LOD 2014, has been the third edition of this challenge. The underlying data came from two domains: public procurement, and researcher collaboration. Like in the previous year, when the challenge was held at the Data Mining on Linked Data workshop co-located with the European Conference on Machine Learning a...
متن کاملGraph Kernels for Task 1 and 2 of the Linked Data Data Mining Challenge 2013
In this paper we present the application of two RDF graph kernels to task 1 and 2 of the linked data data-mining challenge. Both graph kernels use term vectors to handle RDF literals. Based on experiments with the task data, we use the Weisfeiler-Lehman RDF graph kernel for task 1 and the intersection path tree kernel for task 2 in our final classifiers for the challenge. Applying these graph k...
متن کاملThe Linked Data Mining Challenge 2015
The 2015 edition of the Linked Data Mining Challenge, conducted in conjunction with Know@LOD 2015, has been the third edition of this challenge. This year’s dataset collected movie ratings, where the task was to classify well and badly rated movies. The solutions submitted reached an accuracy of almost 95%, which is a clear advancement over the baseline of 60%. However, there is still headroom ...
متن کاملUsing Data Mining on Linked Open Data for Analyzing E-Procurement Information - A Machine Learning approach to the Linked Data Mining Challenge 2013
Understanding complex procurement information landscapes and exploring how procurement information can be used to support strategic decision-making is important with the increasing amount of information available in the WWW. In this paper, we cope with this challenge and describe how data mining techniques can be applied on semantically linked data to estimate the number of bidders in public co...
متن کاملTowards a Benchmark for LOD-Enhanced Knowledge Discovery from Structured Data
To leverage on KDD architectures designed for business databases, original unbounded RDF data has to be transformed. We report on a use case consisting in adaptation of linked data, around a nucleus of public procurement data, for a data mining challenge event. The generic problems addressed are: linked data sampling; (generalised) concise bounded description extraction; propositionalisation to...
متن کامل