Corpus ID: 1591091

The Linked Data Mining Challenge 2016

@inproceedings{Ristoski2015TheLD,
  title={The Linked Data Mining Challenge 2016},
  author={Petar Ristoski and H. Paulheim and V. Sv{\'a}tek and Vaclav Zeman},
  booktitle={@ESWC},
  year={2015}
}
The 2015 edition of the Linked Data Mining Challenge, con- ducted 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 for improvement, as the majority vote of the three best systems reaches an even higher accuracy. 
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References

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The Linked Data Mining Challenge 2014: Results and Experiences
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Predicting Metacritic Film Reviews Using Linked Open Data and Semantic Technologies
Adoption of the Linked Data Best Practices in Different Topical Domains
Data Mining with Background Knowledge from the Web
Mining the Web of Linked Data with RapidMiner
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