Reviewerly/exHarmony huggingface dataset:
Ontology-Compliant Knowledge Graphs

Ontologies can act as a schema for constructing knowledge graphs (KGs), offering explainability, interoperability, and reusability. We explore ontology-compliant KGs, aiming to build both internal and external ontology compliance. We discuss key tasks in ontology compliance and introduce our novel term-matching algorithms. We also propose a pattern-based compliance approach and novel compliance metrics. The building sector is a case study to test the validity of ontology-compliant KGs. We recommend using ontology-compliant KGs to pursue automatic matching, alignment, and harmonisation of heterogeneous KGs.
Integrating Faceted Search with Data Analytic Tools in the User Interface of ParliamentSampo – Parliament of Finland on the Semantic Web

This paper presents the idea and implementation of integrating faceted search and browsing with data-analytic tools in the user interface design of the new in-use semantic portal ParliamentSampo.
Piloting Topic-Aware Research Impact Assessment Features in BIP! Services

Various research activities rely on citation-based impact indicators. However these indicators are usually globally computed, hindering their proper interpretation in applications like research assessment and knowledge discovery. In this work, we advocate for the use of topic-aware categorical impact indicators, to alleviate the aforementioned problem. In addition, we extend BIP! Services to support those indicators and showcase their benefits in real-world research activities.
Adversarial Attacks on Skeleton-Based Sign Language Recognition

Despite the impressive performance achieved by sign language recognition systems based on skeleton information, our research has uncovered their vulnerability to malicious attacks. In response to this challenge, we present an adversarial attack specifically designed to sign language recognition models that rely on extracted human skeleton data as features. Our attack aims to assess the robustness and sensitivity of these models, and we propose adversarial training techniques to enhance their resilience. Moreover, we conduct transfer experiments using the generated adversarial samples to demonstrate the transferability of these adversarial examples across different models. Additionally, by conducting experiments on the sensitivity of sign language recognition models, we identify the optimal experimental parameter settings for achieving the most effective attacks. This research significantly contributes to future investigations into the security of sign language recognition.
Semantic and Efficient Symbolic Learning over Knowledge Graphs

In recent years, the rise of large Knowledge Graphs (KGs), which capture knowledge in machine-driven formats, has arisen broadly. KGs are the convergence of data and knowledge, and may be incomplete due to the Open World Assumption (OWA). Inductive Logic Programming (ILP) is a popular traditional approach for mining logical rules to complete the KGs. ILP approaches derive logical rules from ground facts in knowledge bases. Deducing new information or adding missing information to the KGs, identifying potential errors, and understanding the data more substantially can be accomplished by mining logical rules. Inference can be used to deduce new facts and complete KGs. To discover meaningful insights, traditional rule mining approaches first ignore axiomatic systems defining the semantics of the predicates and classes available in KGs. Second, most rule miners measure the impact of mined rules in terms of correlation rather than causation, and they are overwhelmed by the volume of data. Finally, existing frameworks implement blocking methods that require the processing of complete KGs to generate the mined rules. In this Ph.D. proposal, an outline of a rule-mining model explicitly tailored to mine Horn rules encapsulating semantics on top of KGs is reported. Additionally, the rule-mining approach is based on reliably estimating the cause-effect relationships and discovering new facts in the KGs considering data and metadata. Our approach follows an iterative process to inductively mine rules incorporating semantics to enhance completeness. Our experimental results suggest that by combining entailment regimes and querying KGs on demand, our approach outperforms the state-of-the-art in terms of accuracy. A publicly available Jupyter notebook that executes a demonstration is available ( https://mybinder.org/v2/gh/SDM-TIB/DIGGER-ESWC2023Demo/HEAD?labpath=Mining%20Symbolic%20Rules%20To%20Explain%20Lung%20Cancer%20Treatments.ipynb ).
Growing Memory Network with Random Weight 3DCNN for Continuous Human Action Recognition

Recent research has shown that continuous learning models, which emulate the learning mechanisms of the human brain, can effectively learn new data over time. However, many of these models require significant amounts of task-specific data to extract visual and temporal features using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Additionally, pre-training these data feature extractors is often computationally expensive and time-consuming. Furthermore, traditional continuous learning models require data labeling before learning new data, which is difficult to achieve in real-world applications such as human action recognition. To address these limitations, we present a novel Random Weight Convolutional-Growing Memory Network (RWC-GMN) model that enables real-time continuous learning of new human actions in the real world. Our model uses a fixed random weight 3DCNN as a feature extractor, thereby eliminating the need for pre-training and enabling our model to start learning new actions immediately. The random weight features will be fed into the Growing Memory Network (GMN) for learning. GMN is a self-organizing incremental network that emulates human episodic memory. The network size can grow and shrink to adapt to input data continuously. Moreover, our model does not require pre-labeling of new human actions, allowing for label-free learning in real-time. Finally, our model can learn new actions continuously while retaining previously acquired knowledge, avoiding the problem of catastrophic forgetting.
Motion Control and Simulation Analysis of a Manipulator Based on Computed Torque Control Method

The accurate position and motion control of the end-effector for robot execution is an important research direction of robot control. Based on the computed torque model, a Proportional-Integral-Derivative computed torque control (PID-CTC) method is introduced, in order to describe the process of implementing the end position control strategy for the robot manipulator. Compared with the traditional position control method, the computed torque control method can better handle the nonlinear problem when the parameters all known. Compared with the response and tracking performance of the robot manipulator to the two input trajectories, the results show that the PID-CTC control effect has low error and high accuracy, which is an efficient motion control method.
Descriptive Comparison of Visual Ontology Change Summarisation Methods

The ontology evolution lifecycle is crucial for usability of ontologies across applications. Changes that are applied to ontologies need to be communicated comprehensively to ontology users and engineers. Change visualisation is a simple, yet powerful way of explaining ontological changes, and different methods come with different shortcomings. This paper introduces and analyses the predominant methods of ontology change visualisations. As there exists no one-fits-all solution, we provide simple guidelines for which visualisation to use.
Exploiting Semantics for Explaining Link Prediction Over Knowledge Graphs

The use of Symbolic and sub-symbolic AI techniques on Knowledge Graphs (KGs) has shown significant progress in several applications. However, many of these methods remain opaque, and the decision-making process behind them can be perplexing. This can result in a lack of trust and reliability in the overall framework. While various explainable frameworks have been proposed to address these issues, do not always provide a complete understanding and may raise privacy concerns as sensitive data may be revealed during the explanation process. In contrast, our proposed approach leverages the semantics of KGs and causal relationships to enhance explainability while still maintaining a high level of trust and reliability. By focusing on XAI for link prediction models and considering entailment regimes (e.g., rdfs:subPropertyOf), the approach can provide more comprehensive and accurate explanations. Moreover, the use of symbolic reasoning allows for more transparent and interpretable explanations. The preliminary results show that our approach is capable of exploiting the semantics of an entity in KG and enhancing the explanations. Henceforth, more work needs to be conducted, to fully comprehend all impacting factors and to identify the most relevant explanations of the machine learning models over KGs.
A System for Repairing $${\mathcal{E}\mathcal{L}}$$ Ontologies Using Weakening and Completing

The quality of ontologies in terms of their correctness and completeness is crucial for developing high-quality ontology-based applications. Traditional debugging techniques repair ontologies by removing unwanted axioms, but may thereby remove consequences that are correct in the domain of the ontology. We propose an interactive approach to mitigate the negative effects of removing unwanted axioms for $${\mathcal{E}\mathcal{L}}$$ ontologies by axiom weakening and completing. This is the first approach that allows for different ways to combine removing, weakening and completing. The choice of combination strategy influences the amount of work for a domain expert as well as the completeness of the repaired ontology. In this paper we describe a system based on a repairing approach that allows for different such combinations (This is a companion paper to [3], a paper accepted at the main track of ESWC 2023, and presents one of the two systems proposed in [3].).
Explaining Deep Reinforcement Learning-Based Methods for Control of Building HVAC Systems

Deep reinforcement learning (DRL) has emerged as a powerful tool for controlling complex systems, by combining deep neural networks with reinforcement learning techniques. However, due to the black-box nature of these algorithms, the resulting control policies can be difficult to understand from a human perspective. This limitation is particularly relevant in real-world scenarios, where an understanding of the controller is required for reliability and safety reasons. In this paper we investigate the application of DRL methods for controlling the heating, ventilation and air-conditioning (HVAC) system of a building, and we propose an Explainable Artificial Intelligence (XAI) approach to provide interpretability to these models. This is accomplished by combining different XAI methods including surrogate models, Shapley values, and counterfactual examples. We show the results of the DRL-based controller in terms of energy consumption and thermal comfort and provide insights and explainability to the underlying control strategy using this XAI layer.
Explaining Black-Boxes in Federated Learning

Federated Learning has witnessed increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the work in the literature proposes algorithms and architectures for training neural networks, which although they present high performance in different predicting tasks and are easy to be learned with a cooperative mechanism, their predictive reasoning is obscure. Therefore, in this paper, we propose a variant of SHAP, one of the most widely used explanation methods, tailored to Horizontal server-based Federated Learning. The basic idea is having the possibility to explain an instance’s prediction performed by the trained Machine Leaning model as an aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.
Natural Example-Based Explainability: A Survey

Explainable Artificial Intelligence (XAI) has become increasingly significant for improving the interpretability and trustworthiness of machine learning models. While saliency maps have stolen the show for the last few years in the XAI field, their ability to reflect models’ internal processes has been questioned. Although less in the spotlight, example-based XAI methods have continued to improve. It encompasses methods that use examples as explanations for a machine learning model’s predictions. This aligns with the psychological mechanisms of human reasoning and makes example-based explanations natural and intuitive for users to understand. Indeed, humans learn and reason by forming mental representations of concepts based on examples. This paper provides an overview of the state-of-the-art in natural example-based XAI, describing the pros and cons of each approach. A “natural” example simply means that it is directly drawn from the training data without involving any generative process. The exclusion of methods that require generating examples is justified by the need for plausibility which is in some regards required to gain a user’s trust. Consequently, this paper will explore the following family of methods: similar examples, counterfactual and semi-factual, influential instances, prototypes, and concepts. In particular, it will compare their semantic definition, their cognitive impact, and added values. We hope it will encourage and facilitate future work on natural example-based XAI.
Hardness of Deceptive Certificate Selection

Recent progress towards theoretical interpretability guarantees for AI has been made with classifiers that are based on interactive proof systems. A prover selects a certificate from the datapoint and sends it to a verifier who decides the class. In the context of machine learning, such a certificate can be a feature that is informative of the class. For a setup with high soundness and completeness, the exchanged certificates must have a high mutual information with the true class of the datapoint. However, this guarantee relies on a bound on the Asymmetric Feature Correlation of the dataset, a property that so far is difficult to estimate for high-dimensional data. It was conjectured in [26] that it is computationally hard to exploit the AFC, which is what we prove here. We consider a malicious prover-verifier duo that aims to exploit the AFC to achieve high completeness and soundness while using uninformative certificates. We show that this task is $$\textsf{NP}$$ -hard and cannot be approximated better than $$\mathcal {O}(m^{1/8 - \epsilon })$$ , where m is the number of possible certificates, for $$\epsilon >0$$ under the Dense-vs-Random conjecture. This is some evidence that AFC should not prevent the use of interactive classification for real-world tasks, as it is computationally hard to be exploited.
Weighted Mutual Information for Out-Of-Distribution Detection

Out-of-distribution detection has become an important theme in machine learning (ML) field, since the recognition of unseen data either “similar” or not (in- or out-of-distribution) to the ones the ML system has been trained on may lead to potentially fatal consequences. Operational data compliance with the training data has to be verified by the data analyst, who must also understand, in operation, if the autonomous decision-making is still safe or not. In this paper, we study an out-of-distribution (OoD) detection approach based on a rule-based eXplainable Artificial Intelligence (XAI) model. Specifically, the method relies on an innovative metric, i.e., the weighted mutual information, able to capture the different way decision rules are used in case of in- and OoD data.
For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI

Abstract Counterfactual explanations (CFEs) are a popular approach in explainable artificial intelligence (xAI), highlighting changes to input data necessary for altering a model’s output. A CFE can either describe a scenario that is better than the factual state ( upward CFE), or a scenario that is worse than the factual state ( downward CFE). However, potential benefits and drawbacks of the directionality of CFEs for user behavior in xAI remain unclear. The current user study (N = 161) compares the impact of CFE directionality on behavior and experience of participants tasked to extract new knowledge from an automated system based on model predictions and CFEs. Results suggest that upward CFEs provide a significant performance advantage over other forms of counterfactual feedback. Moreover, the study highlights potential benefits of mixed CFEs improving user performance compared to downward CFEs or no explanations. In line with the performance results, users’ explicit knowledge of the system is statistically higher after receiving upward CFEs compared to downward comparisons. These findings imply that the alignment between explanation and task at hand, the so-called regulatory fit, may play a crucial role in determining the effectiveness of model explanations, informing future research directions in (xAI). To ensure reproducible research, the entire code, underlying models and user data of this study is openly available: https://github.com/ukuhl/DirectionalAlienZoo
Handling Missing Values in Local Post-hoc Explainability

Missing data are quite common in real scenarios when using Artificial Intelligence (AI) systems for decision-making with tabular data and effectively handling them poses a significant challenge for such systems. While some machine learning models used by AI systems can tackle this problem, the existing literature lacks post-hoc explainability approaches able to deal with predictors that encounter missing data. In this paper, we extend a widely used local model-agnostic post-hoc explanation approach that enables explainability in the presence of missing values by incorporating state-of-the-art imputation methods within the explanation process. Since our proposal returns explanations in the form of feature importance, the user will be aware also of the importance of a missing value in a given record for a particular prediction. Extensive experiments show the effectiveness of the proposed method with respect to some baseline solutions relying on traditional data imputation.
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies

Despite the increasing relevance of explainable AI, assessing the quality of explanations remains a challenging issue. Due to the high costs associated with human-subject experiments, various proxy metrics are often used to approximately quantify explanation quality. Generally, one possible interpretation of the quality of an explanation is its inherent value for teaching a related concept to a student. In this work, we extend artificial simulatability studies to the domain of graph neural networks. Instead of costly human trials, we use explanation-supervisable graph neural networks to perform simulatability studies to quantify the inherent usefulness of attributional graph explanations. We perform an extensive ablation study to investigate the conditions under which the proposed analyses are most meaningful. We additionally validate our method’s applicability on real-world graph classification and regression datasets. We find that relevant explanations can significantly boost the sample efficiency of graph neural networks and analyze the robustness towards noise and bias in the explanations. We believe that the notion of usefulness obtained from our proposed simulatability analysis provides a dimension of explanation quality that is largely orthogonal to the common practice of faithfulness and has great potential to expand the toolbox of explanation quality assessments, specifically for graph explanations.
A Novel Structured Argumentation Framework for Improved Explainability of Classification Tasks

This paper presents a novel framework for structured argumentation, named extended argumentative decision graph (xADG). It is an extension of argumentative decision graphs [10] built upon Dung’s abstract argumentation graphs. The xADG framework allows for arguments to use boolean logic operators and multiple premises (supports) within their internal structure, resulting in more concise argumentation graphs that may be easier for users to understand. The study presents a methodology for construction of xADGs from an input decision tree and evaluates their size and predictive capacity for classification tasks of varying magnitudes. Resulting xADGs achieved strong (balanced) accuracy, kept from the input decision tree, while also reducing the average number of supports needed to reach a conclusion. The results further indicated that it is possible to construct plausibly understandable xADGs that outperform other techniques for building ADGs in terms of predictive capacity and overall size. In summary, the study suggests that xADG represents a promising framework to developing more concise argumentative models that can be used for classification tasks and knowledge discovery, acquisition, and refinement.
Adaptive Goal-Biased Bi-RRT for Online Path Planning of Robotic Manipulators

Autonomous obstacle avoidance path planning plays a crucial role in enabling intelligent and safe operation of robotic manipulators. While the RRT algorithm exhibits promising performance in high-dimensional spaces by avoiding explicit environment modeling and exploring a wide search space, its efficiency is obstructed by random and blind sampling and expansion, limiting online planning and efficient motion capabilities. To address this, we propose the Adaptive Goal-Biased Bi-RRT (AGBBi-RRT) algorithm, which incorporates a goal-biased strategy and an adaptive step size strategy to enhance path planning efficiency in high-dimensional spaces. By combining the concept of Artificial Potential Fields (APF) with a goal-bias factor, we guide the expansion of tree nodes, mitigating the limitations of the RRT algorithm. Additionally, to overcome falling into ‘local minimum’, an adaptive step size strategy is proposed to enhance obstacle avoidance capabilities. Further improvements are achieved through bidirectional pruning and cubic non-uniform B-spline fitting, resulting in shorter and smoother paths. Simulation experiments are conducted to evaluate the performance of AGBBi-RRT algorithm on a six-dof robotic manipulator in single-obstacle and multi-obstacles scenarios, comparing three algorithms: Bi-RRT, GBBi-RRT, and AGBBi-RRT. The experimental results show significant improvements in AGBBi-RRT compared to the previous two algorithms. In the single-obstacle scenario, the AGBBi-RRT algorithm achieves a 15.28% and 9.11% reduction in calculation time, while in the multi-obstacles scenario, the reduction is 27.00% and 15.92% respectively. In addition, AGBBi-RRT exhibits encouraging performance in terms of number of nodes, number of waypoints, and path length.