Generating and Evaluating Plausible Explanations for Knowledge Graph Completion (2024)

Abstract: Explanations for AI should aid human users, yet this ultimate goal remains under-explored. This paper aims to bridge this gap by investigating the specific explanatory needs of human users in the context of Knowledge Graph Completion (KGC) systems. In contrast to the prevailing approaches that primarily focus on mathematical theories, we recognize the potential limitations of explanations that may end up being overly complex or nonsensical for users. Through in-depth user interviews, we gain valuable insights into the types of KGC explanations users seek. Building upon these insights, we introduce GradPath, a novel path-based explanation method designed to meet human-centric explainability constraints and enhance plausibility. Additionally, GradPath harnesses the gradients of the trained KGC model to maintain a certain level of faithfulness. We verify the effectiveness of GradPath through well-designed human-centric evaluations. The results confirm that our method provides explanations that users consider more plausible than previous ones.

## introduction explainability is an essential requirement of ai, especially in high-risk areas, such as healthcare, which directly influence the life and health of humans. only if users understand why an ai system arrives at a particular result can they trust the prediction and engage with the ai-driven system. this makes explainable ai (xai) an essential ingredient for the adoption of ai in high-risk areas @xcite . however, most existing xai approaches aim to learn algorithmic explanations, which are supported by mathematical theories (like other ml methods), but are figure 1 : explain a predicted triple (in red) with the top 5 most influential training triples (in blue), learned by gradient rollback @xcite . the algorithmic explanations are not plausible for users. with this concern, we focus to improve plausibility of explanations, which refers to how convincing explanations are for human users @xcite . for the prediction task with knowledge graph data, a.k.a. knowledge graph completion (kgc), the concept of plausibility is especially tricky, because non-expert users are not familiar with graph structures and the existing explanation methods do not aim to generate graph explanations in a human understandable manner @xcite @xcite . fig. 1 illustrates the challenge with a kgc prediction (in red) and its explanations (in blue) generated by gradient rollback (gr) @xcite . in particular, the predicted triple (angela, is_sister_of, marco) is explained with the top 5 most influential training triples that are selected as per gradients of the learned kgc model. although the provided explanations are faithful subject to a bound on the approximation error @xcite , it is difficult for users to understand. for example, how can the training triple (charlotte, is_sister_of, colin) explain the prediction? it is not plausible for users. to better understand what kind of explanations would be more plausible for users in the context of kgc, we started with a series of interviews. we provided users with predicted triples and gr-based explanations and interviewed them to understand their opinions about the explanations. this investigation led to an insightful finding: interviewees remarked that the explanations linked to a path are the most plausible, see e.g. fig. 2 (a ). here we learned that angela is indeed the sister of marco because angela is the daughter of pierre whereas marco is the son of pierre, therefore, explaining the prediction plausibly. the explanations are no longer scattered; instead, they form a connection between the entities in the predicted triples. this allows users to link them for reasoning. building upon this insight, we introduce grad-path, a novel method for generating human-centric explanations to enhance plausibility. the path based explanations intend to establish reasoning loops akin to human reasoning. technically, the existence of the explanation path can increase the likelihood of the predicted triple, compared with the scenario wherein the path does not exist. to approximate the probabilities, gradpath utilizes gradients collected by gr during training, which ensures a certain level of faithfulness of the generated explanations. these explanation paths can vary in length, as illustrated in fig. 2 . evaluating plausibility is also challenging, as it inherently relies on human perception @xcite . there is not yet a standard way in the literature to evaluate plausibility of explanations (see also appendix a.1). to draw solid conclusion for plausibility, we investigate diverse aspects of a human evaluation study, and introduce a comprehensive humancentric evaluation framework. we utilize humanunderstandable benchmark data and quantify plausibility with different metrics. the human evaluations validate that gradpath produces more plausible explanations for users in comparison to previous xai kgc methods. the major contributions of the paper can be summarized as follows: ## background and notation assume a knowledge graph g, consisting of entities and relation types. two entities and a relation are combined to form a triple in the format (subject, relation, object), shortened as @xmath0, r, o). the subject and object are entities that can be visualized as the nodes of the graph g, while the relation specifies a labeled link from the subject entity to the object one. typically knowledge graphs are incomplete. therefore, knowledge graph completion (kgc) is the task of predicting unknown triples in the knowledge graph. for this, two parts of a triple are given to a prediction system and the third is inferred. for example, t * = (s * , r * , ?) would predict the object given the particular subject and relation. we consider differential kgc methods, such as distmult @xcite and complex @xcite . they learn embedding vectors x e for each entity e and x r for each relation type r. given the vectors, the likelihood of a triple is defined via a score function ϕ(x s , x r , x o ). during training, the vectors are optimized, such that the likelihood of a training triple is larger than that of a randomly sampled unknown triple. these methods can often well predict an object o * for a test triple t * = (s * , r * , ?), but they are black boxes without explanations for users' trust. the task of our work is to explain why the kgc model trained on the graph g arrives at a particular prediction. existing work achieved this by focusing on purely mathematical aspects. for instance, criage @xcite utilized influence functions, and gradient rollback @xcite tracked gradients during training to produce explanations. in contrast to previous work, which focused on algorithmic faithfulness, we place our focus on the human factor: extending algorithmic xai to also respect human needs and produce explanations that are plausible for users. with this, we aim to combine the best of both worlds and therefore move kgcs closer to real-world applications with human-centric explanations to facilitate trust. ## gradpath: generate plausible explanations for kgc we propose the method gradpath to learn humancentric explanations for kgc methods, such that the explanations are plausible for human users, and facilitate them to assess whether the kgc predictions are reasonable or not. ## evaluation of plausibility gradpath is designed to improve plausibility of explanations, which necessarily leads to a question: how do we evaluate plausibility? to address this, we present a human-centric evaluation framework to assess plausibility in a rigorous manner. plausibility refers to how convincing explanations are for users @xcite . in analogy to, for instance, accuracy in classification, there is lack of solid and deterministic ground truth to automatically compute a single score as a measurement of plausibility. the measurement of plausibility is intrinsically based on human perception. thus, we design a comprehensive human evaluation study to quantify plausibility. ## experiment settings and results following the human-centric evaluation framework outlined in sec. 4, we investigated plausibility of gradpath with two human-understandable benchmark datasets: kinship @xcite and codex @xcite (see details of the datasets in @xcite ). an online evaluation platform was developed to collect feedback from testers, as illustrated in fig. 4 . we conducted a comparison between our human-centric method gradpath and the algorithmic explanation method gradient rollback (gr) @xcite to investigate whether gradpath can offer more plausible explanations. ## conclusion explanations for ai should be plausible for humans. to this end, we propose a novel explanation method, gradpath, to improve plausibility for kgc. moreover, a comprehensive human-centric evaluation framework is introduced to evaluate plausibility reliably. we evaluate our gradpath method with three human evaluations using two benchmark datasets. the evaluation results confirm that grad-path indeed provides more plausible explanations than previous methods. one future direction can be to collect a new kgc dataset with the goal of exploring xai kgc for users, as we realize in the human evaluations that datasets should exhibit a high level of human understandability to enable meaningful human evaluations, which is an area where existing kgc datasets may have limitations (see sec. 7 and appendix a.4).