# Farspredict: A benchmark dataset for link prediction

arXiv: https://arxiv.org/abs/2303.14647 · HTML: https://mlsanity.com/paper/2303.14647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 26 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [VisualSem: A High-quality Knowledge Graph for Vision and Language](https://mlsanity.com/paper/2008.09150/) (2020)
- [TransG : A Generative Mixture Model for Knowledge Graph Embedding](https://mlsanity.com/paper/1509.05488/) (2015)
- [ProjE: Embedding Projection for Knowledge Graph Completion](https://mlsanity.com/paper/1611.05425/) (2016)
- [ArCo: the Italian Cultural Heritage Knowledge Graph](https://mlsanity.com/paper/1905.02840/) (2019)
- [Finding Streams in Knowledge Graphs to Support Fact Checking](https://mlsanity.com/paper/1708.07239/) (2017)
- [cgSpan: Closed Graph-Based Substructure Pattern Mining](https://mlsanity.com/paper/2112.09573/) (2021)
- [Knowledge4COVID-19: A Semantic-based Approach for Constructing a COVID-19 related Knowledge Graph from Various Sources and Analysing Treatments' Toxicities](https://mlsanity.com/paper/2206.07375/) (2022)
- [The SeaLiT Ontology -- An Extension of CIDOC-CRM for the Modeling and Integration of Maritime History Information](https://mlsanity.com/paper/2301.04493/) (2023)

## Abstract (arXiv)

Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a low-dimensional representation of a knowledge graph's entities and relations. This paper proposes "Farspredict" a Persian knowledge graph based on Farsbase (the most comprehensive knowledge graph in Persian). It also explains how the knowledge graph structure affects link prediction accuracy in KGE. To evaluate Farspredict, we implemented the popular models of KGE on it and compared the results with Freebase. Given the analysis results, some optimizations on the knowledge graph are carried out to improve its functionality in the KGE. As a result, a new Persian knowledge graph is achieved. Implementation results in the KGE models on Farspredict outperforming Freebases in many cases. At last, we discuss what improvements could be effective in enhancing the quality of Farspredict and how much it improves.