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Mitchell Joblin

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3 papers

2 author rows

AAAI Conference 2022 Conference Paper

TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs

Yushan Liu
Yunpu Ma
Marcel Hildebrandt
Mitchell Joblin
Volker Tresp

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. Embeddingbased 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 with state-of-the-art baselines on three benchmark datasets and show better overall performance while our method also provides explanations that preserve time consistency. Furthermore, in contrast to most state-ofthe-art embedding-based methods, TLogic works well in the inductive setting where already learned rules are transferred to related datasets with a common vocabulary.

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NeSy Conference 2021 Conference Paper

A New Concept for Explaining Graph Neural Networks

Anna Himmelhuber
Sonja Zillner
Stephan Grimm
Martin Ringsquandl
Mitchell Joblin
Thomas A. Runkler

Graph neural networks (GNNs), similarly to other connectionist models, lack transparency in their decision-making. A number of sub-symbolic approaches, such as generating importance masks, have been developed to provide insights into the decision making process of such GNNs. These are first important steps on the way to model explainability, but leaving the interpretation of these sub-symbolic explanations to human analysts can be problematic since humans naturally rely on their background knowledge and therefore also their biases about the data and its domain. To overcome this problem we introduce a conceptual approach by suggesting model-level explanation rule extraction through a standard white-box learning method from the generated importance masks.

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AAAI Conference 2020 Conference Paper

Reasoning on Knowledge Graphs with Debate Dynamics

Marcel Hildebrandt
Jorge Andres Quintero Serna
Yunpu Ma
Martin Ringsquandl
Mitchell Joblin
Volker Tresp

We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main 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 promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classiﬁer, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classiﬁcation and link prediction task. Thereby, we ﬁnd that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and ﬁnd that the extracted arguments are informative for users.

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