Name: Arabic DEv2
Description: The dataset is a translation of a subset (139/467) of the queries in DBpedia Entity v2 (https://github.com/iai-group/DBpedia-Entity) to Modern Standard Arabic. We used the “stopped” version of DBpedia Entity v2 (queries-v2_stopped.txt).
Provider: Bloomberg L.P.
Paper Title: SERAG: Semantic Entity Retrieval from Arabic knowledge Graphs
Tasks: entity retrieval from knowledge graphs 
Venue Name: Workshop on Arabic Natural Language Processing
Authors: Saher Esmeir
Abstract: Knowledge graphs (KGs) are widely used to
  store and access information about entities and
  their relationships. Given a query, the task of
  entity retrieval from a KG aims at presenting
  a ranked list of entities relevant to the query.
  Lately, an increasing number of models for entity retrieval have shown a significant improvement over traditional methods. These models,
  however, were developed for English KGs. In
  this work, we build on one such system, named
  KEWER, to propose SERAG (Semantic Entity Retrieval from Arabic knowledge Graphs).
  Like KEWER, SERAG uses random walks to
  generate entity embeddings. DBpedia-Entity
  v2 is considered the standard test collection
  for entity retrieval. We discuss the challenges
  of using it for non-English languages in general and Arabic in particular. We provide an
  Arabic version of this standard collection, and
  use it to evaluate SERAG. SERAG is shown
  to significantly outperform the popular BM25
  model thanks to its multi-hop reasoning.
  