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.- Anthology ID:
- 2021.wanlp-1.24
- Volume:
- Proceedings of the Sixth Arabic Natural Language Processing Workshop
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine (Virtual)
- Editors:
- Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 219–225
- Language:
- URL:
- https://aclanthology.org/2021.wanlp-1.24
- DOI:
- Cite (ACL):
- Saher Esmeir. 2021. SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 219–225, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
- Cite (Informal):
- SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs (Esmeir, WANLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2021.wanlp-1.24.pdf