Relevance Scores for Triples from Type-Like Relations

Reproducability Material

Download everything needed to reproduce our experimental results (4.1 GB, 16GB uncompressed)

Files included:

  • benchmark_profession.txt Tab separated benchmark for the profession relation. Columns: Person, Freebase-mid, Profession, Score between 0 and 7 (obtained from the crowdsourcing experiment).
  • benchmark_nationality.txt Tab separated benchmark for the nationality relation. Columns: Person, Freebase-mid, Nationality, Score between 0 and 7 (obtained from the crowdsourcing experiment).
  • README.txt A step-by-step explanation of how to reproduce our results locally and the required libraries and disk-space for each approach.
  • entity_contexts.txt The associated text for each entity. One line per enttiy, semantic contexts separated by tabs. 3.6GB uncompressed.
  • entity_tf_file Word occurrence counts per entity. 1.1GB uncompressed.
  • freebase_descriptions.txt All entity descriptions from freebase. Needed to reproduce the "first" baseline. 2.2Gb uncompressed.
  • mturk_judgments_profession The human judgments obtained from the crowdsourcing task for the profession relation.
  • mturk_judgments_nationality The human judgments obtained from the crowdsourcing task for the nationality relation.
  • entity_mid_map A map from entities with readable names (used everywhere) to the original mid in Freebase.
  • person-profession-freebase_full Legacy input format needed by the words_regression appraoch.
  • entity_features_normalized Normalized feature values for the words_regression approach.
  • Makefile Controls dependencies and has targets to clean and produce result files and to print tables for evaluation.
  • All result files A file per appraoch with the final scores assigned to all triples. This allows that results can be examined and tables can be print without long running times. In order to reproduce a result from scratch, there is an associated target in the Makefile.
  • 20 python scripts All scripts print usage. To simply reproduce the experiments, only calls via the Makefile are needed
  • Several intermediate or trivial files These files are either trivial to derive from the input files (e.g., a list of persons to classify can easily be derived from the list of human judgments) or will be cleaned by the corresponding clean target when an appraoch is supposed to be reproduced from scratch (e.g., a list of all word probabilities by profession). Trivial files are included because our approaches evolved over time and so did input formats.