An Improved Link Forecasting Framework for Temporal Knowledge Graphs

Abstract

Representing knowledge in a diagrammatic form has been a long-standing goal of humanity. Early efforts in the field of knowledge representation, such as symbolic logic and semantic net, have led to the development of The Semantic Web, which provided a strong foundation for the development of Knowledge Graphs (KGs). KGs were first introduced in 1986 and were popularized recently in 2012 with the introduction of the Google knowledge graph. Most KGs suffer from the problem of incompleteness, which has led to many efforts in the field of KG completion. In this thesis, we address a specific subproblem from this field called link forecasting. Link forecasting is the problem of predicting future links in a given temporal knowledge graph (TKG) using the existing data. Although several link forecasting frameworks exist in the literature, most previous studies suffer from reduced performance as they cannot efficiently capture the time dynamics of a TKG. We present a novel rule-based link forecasting framework by introducing two new concepts: relaxed temporal random walks and link-star rules. The former concept involves generating rules by performing random walks on a TKG, considering the real-world phenomenon that the order of any two events may be ignored if their occurrence time gap is within a threshold value. The latter concept defines a class of acyclic rules generated based on the natural phenomenon that history repeats after a particular time. Our framework also accounts for the problem of combinatorial rule explosion, making our framework practicable. Experimental results demonstrate that our framework outperforms the state-of-the-art by a substantial margin