Beyond One-Shot Explanations: A Systematic Literature Review of Dialogue-Based XAI Approaches - Mindlin, Dimitry; Beer, Fabian; Sieger, Leonie Nora; Heindorf, Stefan; Cimiano, Philipp; Esposito, Elena; Ngonga-Ngomo, Axel-Cyrille
In the last decade, there has been increasing interest in allowing users to understand how the predictions of machine-learned models come about, thus increasing transparency and empowering users to understand and potentially contest those decisions.Dialogue-based approaches, in contrast to traditional one-shot eXplainable Artificial Intelligence (XAI) methods, facilitate interactive, in-depth exploration through multi-turn dialogues, simulating expert conversations. This paper reviews the current state of dialogue-based XAI, presenting a systematic review of 1,339 publications, narrowed down to 14 based on inclusion criteria. We explore theoretical foundations of the systems, propose key dimensions along which different solutions to dialogue-based XAI differ, and identify key use cases, target audiences, system components, and the types of supported queries and responses. Furthermore, we investigate the current paradigms by which systems are evaluated and highlight their key limitations. Key findings include identifying the main use cases, objectives, and audiences targeted by dialogue-based XAI methods, and summarize the main types of questions and information needs. Beyond discussing avenues for future work, we present a meta-architecture for these systems from existing literature and outlined prevalent theoretical frameworks.

Universal Knowledge Graph Embeddings - KOUAGOU, N'Dah Jean; Demir, Caglar; Zahera, Hamada Mohamed Abdelsamee; Wilke, Adrian; Heindorf, Stefan; Li, Jiayi; Ngonga Ngomo, Axel-Cyrille (Companion Proceedings of the ACM on Web Conference 2024)

Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks - Köhler, Dominik; Heindorf, Stefan (arXiv:2405.12654)
Graph Neural Networks (GNNs) are effective for node classification in graph-structured data, but they lack explainability, especially at the global level. Current research mainly utilizes subgraphs of the input as local explanations or generates new graphs as global explanations. However, these graph-based methods are limited in their ability to explain classes with multiple sufficient explanations. To provide more expressive explanations, we propose utilizing class expressions (CEs) from the field of description logic (DL). Our approach explains heterogeneous graphs with different types of nodes using CEs in the EL description logic. To identify the best explanation among multiple candidate explanations, we employ and compare two different scoring functions: (1) For a given CE, we construct multiple graphs, have the GNN make a prediction for each graph, and aggregate the predicted scores. (2) We score the CE in terms of fidelity, i.e., we compare the predictions of the GNN to the predictions by the CE on a separate validation set. Instead of subgraph-based explanations, we offer CE-based explanations.

Causal Question Answering with Reinforcement Learning - Blübaum, Lukas; Heindorf, Stefan (The World Wide Web Conference (WWW))

Neural Class Expression Synthesis - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023))
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Keywords: Neural network; Concept learning; Description logics

Class Expression Learning with Multiple Representations - Ngonga Ngomo, Axel-Cyrille; Demir, Caglar; Kouagou, N'Dah Jean; Heindorf, Stefan; Karalis, Nikoloas; Bigerl, Alexander (Compendium of Neurosymbolic Artificial Intelligence)

LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals - Demir, Caglar; Wiebesiek, Michel; Lu, Renzhong; Ngonga Ngomo, Axel-Cyrille; Heindorf, Stefan (ECML PKDD)

Neural Class Expression Synthesis in ALCHIQ(D) - Kouagou, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (Machine Learning and Knowledge Discovery in Databases: Research Track)
Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement (ALC). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic ALCHIQ(D). Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2.

Explaining ELH Concept Descriptions through Counterfactual Reasoning - Sieger, Leonie Nora; Heindorf, Stefan; Blübaum, Lukas; Ngonga Ngomo, Axel-Cyrille (arXiv:2301.05109)
Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations.

Accelerating Concept Learning via Sampling - Baci, Alkid; Heindorf, Stefan (CIKM)