Arabic DEv2
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).
Bloomberg L.P.
SERAG: Semantic Entity Retrieval from Arabic knowledge Graphs
entity retrieval from knowledge graphs 
Workshop on Arabic Natural Language Processing
Saher Esmeir
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.
