TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, Volker, Tresp

TL;DR
TLogic introduces an explainable framework using temporal logical rules for link forecasting on temporal knowledge graphs, outperforming baselines and maintaining time consistency, especially effective in inductive settings.
Contribution
The paper presents TLogic, a novel explainable approach leveraging temporal logical rules for link prediction, addressing the lack of interpretability in existing embedding methods.
Findings
TLogic outperforms state-of-the-art baselines on benchmark datasets.
TLogic provides time-consistent explanations for link predictions.
TLogic effectively transfers rules in inductive scenarios.
Abstract
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which are expected to influence future situations. In temporal knowledge graphs, time information is integrated into the graph by equipping each edge with a timestamp or a time range. Embedding-based methods have been introduced for link prediction on temporal knowledge graphs, but they mostly lack explainability and comprehensible reasoning chains. Particularly, they are usually not designed to deal with link forecasting -- event prediction involving future timestamps. We address the task of link forecasting on temporal knowledge graphs and introduce TLogic, an explainable framework that is based on temporal logical rules extracted via temporal random walks. We compare TLogic…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling · Data Quality and Management
