Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
Authors: Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multirelational setting. The large-scale experiments on two real world datasets show that our framework has consistently and significantly better performance for link prediction than stateof-arts that do not account for temporal and evolving non-linear dynamics. |
| Researcher Affiliation | Academia | 1College of Computing, Georgia Institute of Technology. Correspondence to: Rakshit Trivedi <rstrivedi@gatech.edu>, Le Song <lsong@cc.gatech.edu>. |
| Pseudocode | Yes | To address this challenge, we design an efficient Global BPTT algorithm (Algorithm 2, Appendix A) that creates mini-batches of events over global timeline in sliding window fashion and allows to capture dependencies across batches while retaining efficiency. Algorithm 1 presents the survival loss computation procedure. |
| Open Source Code | No | The paper does not provide a concrete access link or explicit statement for open-source code for the methodology. |
| Open Datasets | Yes | We use two datasets: Global Database of Events, Language, and Tone (GDELT) (Leetaru & Schrodt, 2013) and Integrated Crisis Early Warning System (ICEWS) (Boschee et al., 2017) which has recently gained attention in learning community (Schein et al., 2016) as useful temporal KGs. |
| Dataset Splits | No | We therefore partition our test set in 12 different slides and report results in each window. For both datasets, each slide included 2 weeks of time. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | Appendix C provides implementation details of our method and competitors. The provided text does not specify software dependencies with version numbers. |
| Experiment Setup | No | In our experiments, we choose d = l and d = c but they can be chosen differently. The paper mentions tanh as the activation function but does not provide specific hyperparameter values like learning rate, batch size, or optimizer settings. |