Automatic Generation of Structured Domain Knowledge for Dialogue-based XAI Systems (2025)

Abstract: Explanatory dialogue systems serve as intuitive interface between non-expert users and explainable AI (XAI) systems. The interaction with these kind of systems benefits especially from the integration of structured domain knowledge, e.g., by means of bipolar argumentation trees. So far, these domain-specific structures need to be created manually, therewith impairing the flexibility of the system with respect to the domain. We address this limitation by adapting an existing pipeline for topic-independent acquisition of argumentation trees in the field of persuasive, argumentative dialogue to the area of explanatory dialogue. This shift is achieved by a) introducing and investigating different formulations of auxiliary claims per feature of the explanation of the AI model, b) exploring the influence of pre-grouping of the arguments with respect to the feature they address, c) suggesting adaptions to the existing algorithm of the pipeline for obtaining a tree structure, and d) utilizing a new approach for determining the type of the relationship between the arguments. Through a step-wise expert evaluation for the domain titanic survival, we identify the best performing variant of our pipeline. With this variant we conduct a user study comparing the automatically generated argumentation trees against their manually created counterpart in the domains titanic survival and credit acquisition. This assessment of the suitability of the generated argumentation trees for a later integration into dialogue-based XAI systems as domain knowledge yields promising results.

## introduction explainable artificial intelligence (xai) is recently gaining considerable attention as a means to improve the transparency of ai models and therewith enabling humans to understand the decisions made by them @xcite . however, due to the complexity of ai-based systems, it can be challenging to provide xai explanations that are comprehensible also to non-expert users. by integrating xai explanations into human-machine dialogue, users can ask clarifying questions and receive tailored explanations @xcite . in addition, the combination with domain knowledge has the potential to foster a deeper understanding of the behavior of the ai system @xcite . we follow this line of research by introducing an automatized approach for the retrieval of the required domain knowledge from arbitrary documents. viewing explanatory reasoning as argumentative @xcite , we encode the domain knowledge as bipolar argumentation trees @xcite for the use in explanatory dialogue systems. within these tree structures, the domain knowledge is encoded as arguments with supporting or attacking relationships among each other. while the integration of domain knowledge can be beneficial for explanatory systems, the manual effort for creating structured domain knowledge impairs the flexibility of a corresponding system with respect to the domains it can provide meaningful explanations for. to overcome this limitation and therewith make the integration of domain-specific knowledge more feasible, we propose a modular pipeline based on argument search @xcite for automatically generating argumentation trees modeling the domain knowledge. given a domain, a set of features that are utilized in the xai explanations, and a collection of document that contains the information for the domain knowledge, we automatically generate domain-specific argumentation trees for xai dialogues by adapting the pipeline proposed by @xcite to the field of explanatory dialogue. through an expert evaluation, we identify the best configuration of our pipeline. in addition, we evaluate our overall approach by manually generating explanatory dialogues according to the formal model by @xcite with human-as well as automatically generated domain knowledge. a user study assessing the coherence of the generated dialogues, yields promising results for including the automatically generated tree structures into actual dialogue-based xai systems. additionally, we discuss the dependence of the results on the given collection of documents and the way the structured domain knowledge is utilized in the dialogue model. the remainder of this work is organized as follows: section 2 gives an overview over related work and section 3 details our approach to the automatic generation of structured domain knowledge. after identifying the best performing configuration of our pipeline in section 4, section 5 evaluates our approach in a user study. we discuss our results in section 6, before concluding in section 7. ## related work current dialogue-based xai systems primarily function as question-and-answer (q&a) systems that provide explicit verbalizations of the explanations generated by xai methods (e. g., @xcite ). while these systems are effective in providing direct insights, they lack the integration of additional domain-specific information, which has the potential to enhance the context and relevance of the explanations. incorporating domain-specific information into xai itself is not a new idea. @xcite demonstrated how knowledge graphs derived from ontologies can be utilized to create semantic explanations. similarly, @xcite integrated domain-specific information into visual explanations, with annotations provided by domain experts. these approaches illustrate the potential of leveraging domain knowledge to enhance the interpretability of ai systems. while knowledge-based dialogue is a wellestablished research field encompassing a variety of approaches and applications @xcite , the connection between such knowledge-based dialogue systems and xai so far remains mostly unexplored. to the best of our knowledge, the only work exploring this connection is @xcite . they employ bipolar argumentation trees within a dialogue-based xai system to provide access to domain knowledge during conversational exchange. their pilot study shows that incorporating domain knowledge not only improves the overall dialogue experience but also enriches the accessibility and utility of the explanations within the system. since they created the structured domain knowledge through manual annotation, their system can benefit from the herein presented work. ## from documents to structured domain knowledge the pipeline by @xcite , in the following referred to as the existing pipeline, offers a solution to automatically generating topic-specific argumentation trees for persuasive, argumentative dialogues. there, per dialogue, a single argumentation tree is created where all arguments are having a positive or negative stance towards the topic of the dialogue. to allow the explanatory dialogue system to link feature-based xai explanations to the respective domain knowledge in an argumentative manner, multiple argumentation trees per xai feature are required @xcite , where each tree is entailing arguments for a different feature-outcome relation. since we need to create multiple argumentation trees per xai feature and not a single tree for the domain, the existing pipeline cannot be applied to our scenario directly. nevertheless, being successfully evaluated in an argumentative dialogue context, the existing pipeline constitutes a promising basis for our work. the procedure of the existing pipeline is as follows: after utilizing argument search @xcite to retrieve arguments along with their stance towards the topic from a web crawl, the arguments are optionally getting pre-grouped, before performing argumentative relation classification and determining the type of the relations between the arguments through stance propagation. thereby, the argumentative relation classification entails the tasks of predicting the probability for a relationship between the arguments and then creating a tree structure out of these probabilities. in the following, we first define the target structure, i. e., we describe how the domain knowledge is structured when modeled through argumentation trees. afterwards, we detail our pipeline for the automatic generation of this structured domain knowledge. an overview over the processing steps of our pipeline is provided in figure 1 . ## configuration of the pipeline through expert evaluation the above description of our pipeline gives rise to the following possibilities for configuration: we can query the classify api of the search engine either with the domain or the auxiliary claims, apply a pre-grouping of the arguments by xai feature or not, and employ tmg all or tmg feature . for the auxiliary claims, we moreover test four different formulations to identify the best configuration of our pipeline, we perform an expert evaluation in the domain titanic survival with the xai features age, fare, gender, and passenger class and make use of the same collection of documents utilized by @xcite for creating the structured domain knowledge manually. after querying the argument search engine with the domain and all formulations of the auxiliary claims, we annotate the retrieved sentences to determine the best method for the retrieval and whether to include pre-grouping as a step in the pipeline. the decision on the variant of the tmg algorithm is driven by the comparison of the depth and width of the resulting argumentation trees. the expert evaluation is conducted by the authors of the paper. since we are not performing a hypothesis test but merely identify the best configuration of our pipeline, we see no conflict of interest. ## user study with the following user study, we aim to assess the feasibility of our approach for automatically generating structured domain knowledge for dialoguebased xai systems. to this end, we manually generate explanatory dialogues with human annotated and automatically generated domain knowledge and compare the coherence of the resulting dialogues. after presenting how structured domain knowledge in the form of argumentation trees can be utilized in an existing explanation dialogue game, we detail the study setup and present our results. ## discussion to close the gap between the human and our automatically generated argumentation trees for domain-specific knowledge, our results suggest that only an improvement of the references made between the arguments is required. this room for improvement might be attributed to the following areas: first, our pipeline could be identifying the relationships between the arguments in a non-suited way for domain knowledge. this could be improved by fine-tuning the pairwise bert model on a dataset that is tailored more towards the modeling of domain knowledge or by further adapting or even exchanging the process of creating the final argumentation trees through tmg. second, our instantiation of the explanatory dialogue game model could have contributed to the results. while we have utilized the same strategy for generating the dialogues, the underlying argumentation trees are having different characteristics: the human generated domain knowledge shows an almost equal amount of supporting and attacking relations, whereas the automatically generated ones are consisting nearly only of supporting relations. therefore, with the automatically generated trees, the chains of reasoning within the dialogues became increasingly larger and the interaction between the questioner and the explainer was also more singlesided. this potential cause is also underpinned by a comment from one of the study participants: "when the answer of the explainer [...] didn't really fit the question asked but still fit the topic of the conversation i was a bit unsure if [i should an- ## conclusion and future work we have presented an approach to automatically generate structured domain knowledge for dialogue-based xai systems. to this end, we adapted an existing pipeline @xcite from the field of persuasive, argumentative dialogue to the field of explanatory dialogue. our approach combines methods from formal argumentation with data-driven techniques to ensure a flexible, yet reliable knowledge base. through an expert evaluation, we identified the best configuration of our pipeline. utilizing this configuration in a user study, we compare the automatically generated argumentation trees to human-generated ones by assessing the coherence of manually generated explanatory dialogues including the respective trees as domain knowledge. the study concludes that the human-generated argumentation trees are performing better than the automatically generated ones since the reference of the arguments leaves room for improvement. however, we discussed that this might be attributed to the instantiation of the employed explanatory dialogue game and the documents utilized for extracting the domain knowledge. therefore, besides improving the argumentative relation classification of the pipeline itself, a task for future work could be the optimization of the selection of the documents entailing the domain knowledge when not provided with these documents by a human. additionally, the pipeline could become more robust by including validations based on established methods from the field of computational argumentation. last but not least, a more large scale user study evaluating the automatically generated argumentation trees in an actual interaction with a dialogue system providing contextualized xai explanations would provide further valuable insights.