Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Abstract

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

“…xERTE (Han et al, 2020a) learns to find the query-related subgraphs of a fixed hop number. CluSTeR (Li et al, 2021a) and TITer (Sun et al, 2021) both adopt reinforcement learning to discover evolutional patterns in query-related paths of a fixed length. Unlike the query-specific models, entire graph based models encode the latest historical KG sequence of a fixed-length.…”

“…There are two kinds of models to model evolutional patterns, namely, query-specific and entire graph based models. The first kind of models (Jin et al, 2020;Li et al, 2021a;Sun et al, 2021;Han et al, 2020aZhu et al, 2021) extract useful structures (i.e., paths or subgraphs) for each individual query from the historical KG sequence and further predict the future facts by mining evolutional patterns from these structures. This kind of models may inevitably neglect some useful evolutional patterns.…”

“…This kind of models may inevitably neglect some useful evolutional patterns. Therefore, the entire graph based models (Deng et al, 2020;Li et al, 2021a) take a sequence of entire KGs as the input and encode evolutional patterns among them, which exhibit superiority to the query-specific models.…”

IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs

“…Knowledge graphs (KGs) are collections of triples, such as Freebase [ 1 ] and YAGO [ 2 ]. Temporal KGs introduce a new dimension into static knowledge graphs [ 3 ], i.e., a timestamp for each triple to form a quadruple. Although there are billions of triples in temporal KGs, they are still incomplete.…”

“…Most current research on temporal KG completion focuses on interpolation [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Recently, there have been attempts to investigate temporal KG forecasting [ 3 , 4 , 7 , 11 , 12 , 13 ]. According to the interpretability, research on temporal KG forecasting can be divided into two categories.…”