05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access eSPARQL : design and implementation of a query language for epistemic queries on knowledge graphs(2024) Pan, XinyiIn recent years, large-scale knowledge graphs have emerged, integrating data from various sources. Often, this data includes assertions about other assertions, establishing contexts in which these assertions hold. A recent enhancement to RDF, known as RDF-star, allows for statements about statements and is currently under consideration as a W3C standard. However, RDF-star lacks a defined semantics for such statements and lacks intrinsic mechanisms to operate on them. This thesis describes and implements a novel query language, termed eSPARQL, tailored for epistemic RDF-star metadata and grounded in four-valued logic. Our language builds on SPARQL-star, the query language for RDF-star, by incorporating an expanded FROM clause, called FROM BELIEF, designed to manage multiple, and occasionally conflicting, beliefs. eSPARQL’s capabilities are demonstrated through four example queries, showcasing its ability to (i) retrieve individual beliefs, (ii) aggregate beliefs, (iii) identify conflicts between individuals, and (iv) handle nested beliefs (beliefs about beliefs). The implementation of eSPARQL developed in this thesis is built on top of an existing SPARQL-star query engine. In this implementation, the execution process of a eSPARQL consists of two phases. First, the expression in the FROM BELIEF clause, called belief query, is translated into a SPARQL-star CONSTRUCT query that generates an intermediary graph, containing the beliefs of the subjects described in the belief query. In the second phase, This intermediary graph is then processed with the graph pattern of the eSPARQL by translating it to a graph pattern that can be processed by a standard SPARQ-star engine. In this last phase, the implementation translates eSPARQL operations to SPARQL-star, and checks if the pattern contains nested eSPARQL queries to be processed recursively. We study two research questions: (RQ1) Does the eSPARQL implementation scale? and (RQ2) How the eSPARQL implementation execution times compare with the execution time of manually written SPARQL-star queries? To answer these research questions, use the four example eSPARQL queries that showcase the abilities of eSPARQL and create a synthetic dataset generator that generates graphs of multiple sizes. Additionally, for research question RQ2, we manually generate SPARQL-star queries that are equivalent to the example eSPARQL queries. Regarding research question RQ1, our results show that eSPARQL has an execution time that is proportional with the data size. Regarding research question RQ2, except for one question, the manually written SPARQL-star queries are clearly faster than our implementation. Although the implementation showed to be slower than the manually generated SPARQL-star queries, the eSPARQL queries are shorter and easier to understand. This positive aspect of eSPARQL, can motivate further studies on how to optimize the eSPARQL implementation.Item Open Access Correntropy-based constructive one hidden layer neural network(2024) Nayyeri, Mojtaba; Rouhani, Modjtaba; Yazdi, Hadi Sadoghi; Mäkelä, Marko M.; Maskooki, Alaleh; Nikulin, YuryOne of the main disadvantages of the traditional mean square error (MSE)-based constructive networks is their poor performance in the presence of non-Gaussian noises. In this paper, we propose a new incremental constructive network based on the correntropy objective function (correntropy-based constructive neural network (C2N2)), which is robust to non-Gaussian noises. In the proposed learning method, input and output side optimizations are separated. It is proved theoretically that the new hidden node, which is obtained from the input side optimization problem, is not orthogonal to the residual error function. Regarding this fact, it is proved that the correntropy of the residual error converges to its optimum value. During the training process, the weighted linear least square problem is iteratively applied to update the parameters of the newly added node. Experiments on both synthetic and benchmark datasets demonstrate the robustness of the proposed method in comparison with the MSE-based constructive network, the radial basis function (RBF) network. Moreover, the proposed method outperforms other robust learning methods including the cascade correntropy network (CCOEN), Multi-Layer Perceptron based on the Minimum Error Entropy objective function (MLPMEE), Multi-Layer Perceptron based on the correntropy objective function (MLPMCC) and the Robust Least Square Support Vector Machine (RLS-SVM).Item Open Access Generating random knowledge graphs from rules(2024) Glaser, Gabriel TimonA knowledge graph is a datastructure that is capable of storing knowledge. Besides that, there are several methods that use knowledge graphs to derive more information. These methods need to be validated with example knowledge graphs. However, real data might not be available or not contain desired properties. Thus, there are use cases that benefit from the generation of synthetic knowledge graphs. To define a synthetic knowledge graph, there is the need of a characterization that expresses how the synthetic data should look. In this thesis, I use Horn clauses for this characterization because of their good balance of expressiveness and complexity, their use in the field of rule mining, and their base role in the logical language Datalog. As clauses are usually not represented perfectly in real data, the goal of this thesis is to generate a knowledge graph that does not perfectly fulfil given Horn clauses, but in a desired degree of fulfillment. During the thesis, I developed and implemented two modifiable algorithms to generate knowledge graphs. On the one hand, I adapted the general hill climbing technique to generate knowledge graphs. On the other hand, I implemented a greedy algorithm which orders a given set of Horn clauses using logical subsumptions between their bodies, and then add edges to fulfil one Horn clause after the other, in the computed order. Both algorithms aim to fulfil the goal of this thesis by generating synthetic knowledge graphs according to given Horn clauses, each with a degree of fulfilment. The degree of fulfilment of any Horn clauses is characterized by body support, the number of times the premise of the Horn clauses is fulfilled, and support, the number of times the premise and conclusion of the Horn clauses are fulfilled. Additionally, there is the confidence which is the fraction of support and body support, i.e., the percentage of cases the Horn clause is fulfilled. All code is published such that anyone can try it. During the evaluation, random sets of Horn clauses were produced and the implementations generated corresponding knowledge graphs. Generated knowledge graphs were compared by considering the difference between the expected and the actual degree of fulfilment for each Horn clause. The result is that generation variant hill climbing with the initial state set to the result of the greedy algorithm with rule order based on subsumption yields the best results. Also, the difficulty of generating a good knowledge graph increases along with the overlapping degree of the input set of Horn clauses. Note that the overlapping degree reflects how many relation names occur in how many Horn clauses of the set. Lastly, the state-of-the-art mining tool AMIE found many Horn clauses in the generated graphs which were not intended by the set given to the generator.Item Open Access Transferability to spectrogram-based anomaly detection : enhancing audio anomaly detection through vision derived methods(2024) Manea, RaduAnomaly detection in industrial audio data is crucial for ensuring smooth manufacturing processes, enabling predictive maintenance and quality control. Despite its importance, audio anomaly detection has received less attention compared to vision-based methods. This thesis explores the applicability and effectiveness of state-of-the-art vision-based anomaly detection methods, specifically designed for image data, in the context of industrial audio data using spectrograms. The research aims to bridge the gap between the two domains by investigating the potential of adapting vision-based approaches to enhance the performance of audio anomaly detection systems in industrial settings. The study focuses on three key questions: (1) the applicability of vision-based anomaly detection methods to industrial audio data, (2) the impact of replacing the image-based feature extractor with a spectrogram-specific feature extractor (AST transformer), and (3) the effect of fine-tuning the AST transformer on industrial spectrograms. The research employs state-of-the-art anomaly detection models, namely Patchcore, FastFlow, EfficientAD, and Reverse Distillation, and evaluates their performance on the DCASE2020 dataset and a real-world industrial dataset from BMW. The findings reveal that vision-based anomaly detection methods can be successfully applied to industrial audio data, with varying degrees of performance depending on the dataset, model architecture, and spectrogram representation used. The study identifies key factors that influence the performance of spectrogram anomaly detection and presents several ways to adapt vision-based approaches for use on spectrograms. These adaptations, such as replacing the image-based feature extractor with a spectrogram-specific feature extractor (AST transformer), have shown promising results in enhancing the performance of audio anomaly detection systems. Furthermore, the successful application of these approaches on the BMW dataset demonstrates their potential in real-world production environments, particularly when recordings are made under controlled conditions with minimal variance.Item Open Access End-to-End-Gebärdenspracherkennung durch Computer-Sehen(2024) Selim, Ziya CanWeltweit kommunizieren Menschen verbal in verschiedensten Sprachen. Dabei gibt es bereits schon dann Komplikationen, wenn zwei Personen verschiedene Sprachen sprechen. Dennoch gibt es auf der Welt mehrere Millionen Menschen, welche taub oder hörgeschädigt sind[WHO]. Damit sich Hörgeschädigte dennoch verständigen können, wird ein anderer Kommunikationskanal verwendet. Anstatt der gesprochenen Sprache verwendet man mithilfe der Zeichensprache den visuellen Kommunikationskanal. Für die Erleichterung der Kommunikation mit Gehörlosen benötigt man zunächst eine Möglichkeit, Handzeichen erkennen zu können, damit diese im Folgenden übersetzt werden können. Die Erkennung dieser Handzeichen lässt sich durch die Bilderkennung gut lösen. In dieser Arbeit wird erkundet, wie die Bild- und Videoerkennung eine Hilfe sein kann, die Kommunikation mit Hörgeschädigten zu erleichternItem Open Access Geometric relational embeddings(2024) Xiong, Bo; Staab, Steffen (Prof. Dr.)In classical AI, symbolic knowledge is typically represented as relational data within a graph-structured framework, a.k.a., relational knowledge bases (KBs). Relational KBs suffer from incompleteness and numerous efforts have been dedicated to KB completion. One prevalent approach involves mapping relational data into continuous representations within a low-dimensional vector space, referred to as relational representation learning. This facilitates the preservation of relational structures, allowing for effective inference of missing knowledge from the embedding space. Nevertheless, existing methods employ pure-vector embeddings and map each relational object, such as entities, concepts, or relations, as a simple point in a vector space (typically Euclidean. While these pure-vector embeddings are simple and adept at capturing object similarities, they fall short in capturing various discrete and symbolic properties inherent in relational data. This thesis surpasses conventional vector embeddings by embracing geometric embeddings to more effectively capture the relational structures and underlying discrete semantics of relational data. Geometric embeddings map data objects as geometric elements, such as points in hyperbolic space with constant negative curvature or convex regions (e.g., boxes, disks) in Euclidean vector space, offering superior modeling of discrete properties present in relational data. Specifically, this dissertation introduces various geometric relational embedding models capable of capturing: 1) complex structured patterns like hierarchies and cycles in networks and knowledge graphs; 2) intricate relational/logical patterns in knowledge graphs; 3) logical structures in ontologies and logical constraints applicable for constraining machine learning model outputs; and 4) high-order complex relationships between entities and relations. Our results obtained from benchmark and real-world datasets demonstrate the efficacy of geometric relational embeddings in adeptly capturing these discrete, symbolic, and structured properties inherent in relational data, which leads to performance improvements over various relational reasoning tasks.Item Open Access Enhancing online lecture engagement through gaze and emotion feedback integration(2025) Horn, FrederikThe shift towards online meetings, seminars, and lectures has rapidly gained momentum in recent years, particularly accelerated by the events surrounding the COVID-19 pandemic. However, the absence of nonverbal cues such as eye contact and gestures in virtual settings presents significant challenges for effective communication. Existing frameworks for virtual meetings often fail to adequately address this issue, making it difficult for presenters to accurately assess audience engagement and comprehension. This thesis investigates these challenges by capturing participant attention and emotion in real-time and evaluating how presenters interact with live feedback. Through the implementation and study of a feedback system that visualizes gaze and emotional data, we aimed to bridge the gap between in-person and online lecture experiences. To ensure an informed approach, we conducted a requirement analysis prior to our experiment, identifying key factors for effective feedback integration. In the experiment, we collected data through our tool by logging meeting interactions and analyzing responses from a post-experiment questionnaire. Our findings provide insights into the impact of such feedback on presenter motivation, delivery adjustments, and engagement levels, ultimately contributing to the improvement of educational quality in virtual environments.Item Open Access Bayesian symbolic regression in structured latent spaces(2025) Pei, ChenleiSymbolic regression is an interpretable machine learning method that learns mathematical expressions from given data. It naturally combines with Bayesian Inference which lets experts express their knowledge as prior distributions over equations. However, the infinite search space of mathematical expressions renders exhaustive search impractical, and Bayesian Inference remains costly. Therefore, we propose to execute the Bayesian Reasoning in the learned latent space of a trained Variational Autoencoder (VAE) and thereby exploit inherent structures in the search space. While latent spaces have been used to structure search spaces, our approach provides the probability of each mathematical expression rather than selecting the best one. We suggest practical approximations to the posterior distribution in latent space and obtain formula examples by sampling from the posterior using the Gaussian Process Hamiltonian Monte Carlo (GP-HMC) method. We have validated our method using various Koza, Nguyen, and self-generated datasets and compared it against genetic programming and SInDy concerning the Root Mean Square Error (RMSE). Keywords: Symbolic Regression, latent space, Variational Autoencoder, Character Variational Autoencoder, Grammar Variational Autoencoder, Bayesian Reasoning, Gaussian Process, Hamiltonian Monte Carlo, Gaussian Process Hamiltonian Monte CarloItem Open Access L2XGNN : learning to explain graph neural networks(2024) Serra, Giuseppe; Niepert, MathiasGraph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn , a framework for explainable GNNs which provides faithful explanations by design. L2xGnn learns a mechanism for selecting explanatory subgraphs (motifs) which are exclusively used in the GNNs message-passing operations. L2xGnn is able to select, for each input graph, a subgraph with specific properties such as being sparse and connected. Imposing such constraints on the motifs often leads to more interpretable and effective explanations. Experiments on several datasets suggest that L2xGnn achieves the same classification accuracy as baseline methods using the entire input graph while ensuring that only the provided explanations are used to make predictions. Moreover, we show that L2xGnn is able to identify motifs responsible for the graph’s properties it is intended to predict.Item Open Access Stochastic query synthesis for neural PDE solvers(2025) Ullrich, FinnPDEs are highly influential in physics and are describing various phenomena in the world, from wave movement to electro-magnetics. The problem arises when one tries to solve them, which requires enormous computing power for a numerical solution. To overcome the limitiations, neural PDE solvers have been proposed, using neural networks to approximate the solution trajectories. However, neural PDE solvers require training data from an computationally expensive numerical solver. Therefore, Musekamp et al. created a benchmark, which investigates active learning for neural PDE solvers. Active learning can reduce the amount of data required, while keeping the same performance. In this work, we will demonstrate a new strategy of selecting samples called stochastic query synthesis. Following this, we will remove the pool currently used and rather create a Markov chain directly from the input space containing unlabeled instances. The transition probability is based on the unadjusted Langevin algorithm, allowing us to sample by exploiting gradient information. To retrieve a better result, instead of just one chain, we will create multiple parallel chains, and only take the last state as input. We will show that this approach is equally effective as the currently implemented pool-based implementation. However, there are still performance problems that need to be solved in the future, to make it viable in practice.