Link prediction (retrieval) in knowledge graphs and issue tracking systems

In Chapter 3, we propose NDKGE, a strategy that constructs enriched entity descriptions by integrating neighbor information, enhancing semantic representation. Chapter 4 explores a novel task—unknown fact detection (UFD)—to retrieve factual triples from external graphs (e.g., Freebase enriching IMDB) and expand domain knowledge beyond the closed-world setting.<br><br>In issue tracking systems, link prediction has been applied to retrieve duplicate reports.

In Chapter 5, it incorporates pre-trained models like BERT to improve retrieval accuracy. Moreover, we propose a new task—issue link retrieval—to detect multiple link types (e.g., "relates", "subtask", "contains") beyond duplication, and evaluate it across four datasets.<br><br>Finally, Chapter 6 addresses concept drift in issue tracking systems by introducing incremental learning. The model is periodically updated with new issues to maintain performance over time.

Chapter 7 concludes the thesis and outlines directions for future work.<br>