Marrying Uncertainty and Time in Knowledge Graphs
Authors: Melisachew Chekol, Giuseppe Pirr, Joerg Schoenfisch, Heiner Stuckenschmidt
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report on an experimental evaluation comparing the MLN and PSL approaches. We carry out a set of experiments using state-of-the-art MLN solvers and their scalable variants. We conducted two different kinds of experiments: (i) performance test in terms of running times for MAP inference comparing three state-of-the-art solvers, and (ii) conflict detection in a noisy setting. |
| Researcher Affiliation | Academia | Melisachew Wudage Chekol Data and Web Science Group, University of Mannheim mel@informatik.uni-mannheim.de Giuseppe Pirr o Rende(CS), Italy pirro@icar.cnr.it Joerg Schoenfisch Data and Web Science Group University of Mannheim joerg@dwslab.de Heiner Stuckenschmidt Data and Web Science Group University of Mannheim heiner@informatik.uni-mannheim.de |
| Pseudocode | Yes | Figure 1: A set of temporal RDF inference rules that we denote by F. check(T1, T2) = false if T1 T2 = and true otherwise. α denotes the RDF type relation and q is a shorthand for quad. Moreover, all of the formulas are universally quantified over all the variables. |
| Open Source Code | No | The paper mentions using and implementing extensions for third-party tools (Tuffy, nRockIt, PSL, nPSL) but does not state that their specific implementations or modifications are open-source or provide a link to their code. |
| Open Datasets | Yes | We experimented with YAGO (Gal arraga et al. 2015) to mine temporal rules as discussed below. Mined: With a workaround to AIME, we were able to learn rules of the following form from the YAGO dataset. |
| Dataset Splits | No | The paper describes how erroneous facts were injected into datasets and how weights were assigned, but it does not specify train, validation, or test dataset splits. |
| Hardware Specification | Yes | We ran the experiments on a 2GHz 24-core processor with 386GB of RAM running Debian 8. |
| Software Dependencies | No | The paper mentions the tools used (Tuffy, n Rock It, PSL) and cites papers for them, but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We generated erroneous temporal facts specifying the plays For, birthdate, and deathdate relations. We injected a fraction of 10%, 25%, 50%, 75%, and 100% incorrect facts to the Wikidata dataset. For instance, injecting 25% erroneous facts means that we added 25% additional wrong facts for each of the three relations. We randomly assigned weights in the range [0.5, 1.0] to the newly added facts and [0.8, 1.0] to each of the original temporal facts. We repeated each experiment 10 times and present average scores. |