Date of Award


Publication Type


Degree Name



Computer Science


Error Detection;Knowledge Graph Embeddings;Knowledge Graphs;Path Ranking


Ziad Kobti



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Knowledge graphs (KGs) use triples to describe real-world facts. They have seen widespread use in intelligent analysis and applications. However, the automatic construction process of KGs unavoidably introduces possible noises and errors. Furthermore, KG-based tasks and applications assume that the knowledge in the KG is entirely correct, which leads to potential deviations. Error detection is critical in KGs, where errors are rare but significant. Various error detection methodologies, primarily path ranking (PR) and representation learning, have been proposed to ad- dress this issue. In this thesis, we introduced the Enhanced Path Ranking Guided Embedding (EPRGE), which is an improved version of an existing model, the Path Ranking Guided Embedding (PRGE) that uses path-ranking confidence scores to guide TransE embeddings. To improve PRGE, we use a rotational-based embedding model (RotatE) instead of TransE, which uses a self-adversarial negative sampling technique to train the model efficiently and effectively. EPRGE, unlike PRGE, avoids generating meaningless false triples during training by employing the self-adversarial negative sampling method. We compare various methods on two benchmark datasets and one real-world dataset, demonstrating the potential of our approach and providing enhanced insights on graph embeddings when dealing with noisy KGs.