2020/01/09 by Marcel Hildebrandt, Jorge Andres Quintero Serna, Hildebrandt, Marcel +9
Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Topic Modeling #cs.AI #cs.CL #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2001.03436
AAAI 2019 Fall Symposium Series. arXiv admin note: substantial text overlap with arXiv:2001.00461
arxiv created 2020/01/09 · openalex publication_date 2020/01/09 · arxiv updated 2020/01/13 · openalex created_date 2020/01/23 · openalex updated_date 2026/07/28
We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, referred to as the judge, decides whether the fact is true or false. The two agents can be considered as sparse feature extractors that present interpretable evidence for either the thesis or the antithesis. In contrast to black-box methods, the arguments enable the user to gain an understanding for the decision of the judge. Moreover, our method allows for interactive reasoning on knowledge graphs where the users can raise additional arguments or evaluate the debate taking common sense reasoning and external information into account. Such interactive systems can increase the acceptance of various AI applications based on knowledge graphs and can further lead to higher efficiency, robustness, and fairness.