EntityRelatedness.md

Entity Relatedness

Datasets used as gold standard

Dataset	Structure	Size
KORE	main entity with a sorted list of 20 related entities	420 entities

Model

sim_scores = []
for each main entity as me:
  for each related entity as re:
    sim_scores.add(similarity_function(me,re))
  sort(sim_scores) //from more to less similar}

The similarity_function can be customized by the user.

Missing entities are managed as follows:

• if a main entity is missing, it is simply ignored;
• if one or more related entities attached to the same main entity are missing, first, the task compute the similarity among the available entities as reported in the model described in the Table~\ref{entity_relatedness}; then, all the missing related entities are randomly put in the tail of the sorted list, and, finally, the evaluation metric is calculated on the ranking obtained by the similarity score among all the available pairs concatenated with the missing entities.

Output of the evaluation

Metric	Range	Interpretation
Kendall's tau correlation coefficient	[-1,1]	Extreme values: correlation, Values close to 0: no correlation