Adaptive Koopman-Based Models for Holistic Controller and Observer Design

Authors: Annika Junker, Keno Pape, Julia Timmermann, Ansgar Trächtler

We present a method to obtain a data-driven Koopman operator-based model that adapts itself during operation and can be straightforwardly used for the controller and observer design. The adaptive model is able to accurately describe different state-space regions and additionally consider unpredictable system changes that occur during operation. Furthermore, we show that this adaptive model is applicable to state-space control, which requires complete knowledge of the state vector. (...)

Social exclusion in personnel selection – The risk of discriminating AI biases

Authors: Clarissa Sabrina Arlinghaus, Günter W. Maier

Work plays a central role in the life of adults as it opens up access to a wide range of valuable resources (e.g., financial security, time structure, social contacts). Thereby work contributes to the social inclusion of people in most societies. Therefore, personnel selection processes carry a high level of social responsibility. Nowadays, artificial intelligence (AI) is widely used in human resources (HR), but the unreflected use of AI in recruitment can lead to the exclusion of vulnerable groups. (...)

Being ignored is not the only possible form of social exclusion in human-agent interaction

Authors: Clarissa Sabrina Arlinghaus, Günter W. Maier

In a world where humans and technical agents (e.g., robots, AI) work collaboratively, processes of social inclusion and exclusion in human-agent interaction (HAI) gain importance. However, the current focus of social exclusion in HAI is too narrowminded and neglects many forms of social exclusion (e.g., averted eye gazes, microaggressions, hurtful laughter). To change this, the effects of different types of social exclusion will be explored in a series of experiments against the background of William's need-threat mode (...)

Robust Training with Adversarial Examples on Industrial Data

Authors: Julian Knaup, Christoph-Alexander Holst, Volker Lohweg

n an era where deep learning models are increasingly deployed in safety-critical domains, ensuring their reliability is paramount. The emergence of adversarial examples, which can lead to severe model misbehavior, underscores this need for robustness. Adversarial training, a technique aimed at fortifying models against such threats, is of particular interest. This paper presents an approach tailored to adversarial training on tabular data within industrial environments.

Active Learning for Regression Problems with Ensemble Methods

Authors: Bjarne Jaster, Martin Kohlhase

Traditional machine learning paradigms depend on the availability of labeled data, a luxury that is not often the reality in real-world scenarios. In domains such as industry, healthcare, autonomous systems and finances a massive amount of unlabeled data is produced every day. As the demand for accurate and robust models to deal with this data grows, the inefficiency and the cost of manual labeling motivates the research field active learning (...)

Predicting grounding state for adaptive explanation generation in analogical problem-solving

Authors: Lina Mavrina, Stefan Kopp

This paper’s main contribution is a Bayesian hierarchical grounding state prediction model implemented in an adaptive explainer agent assisting users with analogical problem-solving. This model lets the agent adapt dialogue moves regarding previously unmentioned domain entities that are similar to the ones already explained when they are instances of the same generalised schema in different domains. Learning such schemata facilitates knowledge transfer between domains and plays an important role in analogical reasoning (...)

Adaptive local Principal Component Analysis improves the clustering of high-dimensional data

Authors: Nico Migenda, Ralf Möller, Wolfram Schenck

In local Principal Component Analysis (PCA), a distribution is approximated by multiple units, each representing a local region by a hyper-ellipsoid obtained through PCA. We present an extension for local PCA which adaptively adjusts both the learning rate of each unit and the potential function which guides the competition between the local units. Our local PCA method is an online neural network method where (...)

A Topic Model for the Data Web

Authors: Michael Röder, Denis Kuchelev, Axel Ngonga

The usage of knowledge graphs in industry and at Web scale has increased steadily within recent years. However, the decentralized approach to data creation which underpins the popularity of knowledge graphs also comes with significant challenges. In particular, gaining an overview of the topics covered by existing datasets manually becomes a gargantuan if not impossible feat. Several dataset catalogs (...)

Neural Class Expression Synthesis in ALCHIQ(D)

Authors: N’Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo

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 (...)

LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

Authors: Caglar Demir, Michel Wiebesiek, Renzhong Lu, Axel-Cyrille Ngonga Ngomo, Stefan Heindorf

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, these approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD (...)

COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge Graphs

Authors: Alexander Becker, Abdullah Ahmed, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo

Data integration and applications across knowledge graphs (KGs) rely heavily on the discovery of links between resources within these KGs. Geospatial link discovery algorithms have to deal with millions of point sets containing billions of points. (...)

Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras

Authors: Caglar Demir, Axel-Cyrille Ngonga Ngomo

A growing number of knowledge graph embedding models exploit the characteristics of division algebras (e.g., R, C, H, and O) to learn embeddings. Yet, recent empirical results suggest that the suitability of algebras is contingent upon the knowledge graph being embedded. In this work, we tackle the challenge of selecting the algebra within which a given knowledge graph should be embedded by exploiting the fact that Clifford algebras (...)

Native Execution of GraphQL Queries over RDF Graphs Using Multi-way Joins

Authors: Nikolaos Karalis, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo

The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Most of the storage solutions that support GraphQL rely on a translation layer to map the said language to another query language that they support natively, for example SPARQL. (...)

Neuro-Symbolic Class Expression Learning

Authors: Caglar Demir, Axel-Cyrille Ngonga Ngomo

Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable. In contrast, class expressions in description logics are ante-hoc and globally explainable. Although state-of-the-art symbolic machine learning approaches are being successfully applied to learn class expressions, their application at large scale has been hindered by their impractical runtimes. Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose (...)

Beyond the Bias: Unveiling the Quality of Implicit Causality Prompt Continuations in Language Models

Authors: Judith Sieker, Oliver Bott, Torgrim Solstad, Sina Zarrieß

Recent studies have used human continuations of Implicit Causality (IC) prompts collected in linguistic experiments to evaluate discourse understanding in large language models (LLMs), focusing on the well-known IC coreference bias in the LLMs’ predictions of the next word following the prompt. In this study, we investigate how continuations of IC prompts can be used to evaluate the text generation capabilities of LLMs in a linguistically controlled setting. We conduct an experiment using two open-source GPT-based models (...)

Tutorial: Interactive Adaptive Learning

Authors: Mirko Bunse, Georg Krempl, Alaa Tharwat, Amal Saadallah

We summarize the contents of the tutorial we present as a part of the 7th Interactive Adaptive Learning workshop. This workshop is co-located with the ECML-PKDD conference, where it takes place on September 22nd, 2023 in Turin, Italy.

NELLIE: Never-Ending Linking for Linked Open Data

Authors: Abdullah Fathi Ahmed, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo

Knowledge graphs (KGs) that follow the Linked Data principles are created daily. However, there are no holistic models for the Linked Open Data (LOD). Building these models( i.e., engineering a pipeline system) is still a big challenge in order to make the LOD vision comes true. In this paper, we address this challenge by presenting NELLIE (...)

Explainable Integration of Knowledge Graphs using Large Language Models

Authors: Abdullah Fathi Ahmed, Asep Fajar Firmansyah, Mohamed Ahmed Sherif, Diego Moussallem, Axel-Cyrille Ngonga Ngomo

Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by (...)

Neural Class Expression Synthesis

Authors: N’Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo

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 (...)

A Unifying Formal Approach to Importance Values in Boolean Functions

Authors: Hans Harder, Simon Jantsch, Christel Baier, Clemens Dubslaff

Boolean functions and their representation through logics, circuits, machine learning classifers, or binary decision diagrams (BDDs) play a central role in the design and analysis of computing systems. Quantifying the relative impact of variables on the truth value by means of importance values can provide useful insights to steer system design and debugging. In this paper, we introduce a uniform framework for reasoning about such value (...)

Identifying Slurs and Lexical Hate Speech via Light-Weight Dimension Projection in Embedding Space

Authors: Sanne Hoeken, Sina Zarrieß, Ozge Alacam

The prevalence of hate speech on online platforms has become a pressing concern for society, leading to increased attention towards detecting hate speech. Prior work in this area has primarily focused on identifying hate speech at the utterance level that reflects the complex nature of hate speech. In this paper, we propose a targeted and efficient approach to identifying hate speech by detecting slurs at the lexical level using contextualized word embeddings. We hypothesize that slurs (...)

Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis

Authors: Dominik Stallmann, Barbara Hammer

Novel neural network models that can handle complex tasks with fewer examples than before are being developed for a wide range of applications. In some fields, even the creation of a few labels is a laborious task and impractical, especially for data that require more than a few seconds to generate each label. In the biotechnological domain, cell cultivation experiments are usually done by varying the circumstances of the experiments (...)

A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions

Authors: Alaa Tharwat, Wolfram Schenck

Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensive and time-consuming labeling process is still an obstacle to labeling a sufficient amount of training data, which is essential for building supervised learning models. Here, with low labeling cost, the active learning (AL) technique could be a solution (...)

Modeling Referential Gaze in Task-oriented Settings of Varying Referential Complexity

Authors: Özge Alacam, Eugen Ruppert, Sina Zarrieß, Ganeshan Malhotra, Chris Biemann

Referential gaze is a fundamental phenomenon for psycholinguistics and human-human communication. However, modeling referential gaze for real-world scenarios, e.g. for task-oriented communication, is lacking the well-deserved attention from the NLP community. In this paper, we address this challenging issue by proposing a novel multimodal NLP task; namely predicting when the gaze is referential. We further investigate (...)

Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency

Authors: Özge Alacam, Simeon Schüz, Martin Wegrzyn, Johanna Kißler, Sina Zarrieß

In this work, we explore the fitness of various word/concept representations in analyzing an experimental verbal fluency dataset providing human responses to 10 different category enumeration tasks. Based on human annotations of so-called clusters and switches between sub-categories in the verbal fluency sequences, we analyze whether lexical semantic knowledge represented in word embedding spaces (GloVe, fastText, ConceptNet, BERT) is suitable for detecting (...)

Towards designing assistants for well-being: clarifying the relationship between users’ intrinsic motivation and expectations from assistants

Authors: Hitesh Dhiman, Yutaro Nemoto, Holger Mühlan, Michael Fellmann, Carsten Röcker

Although considerable research effort has been devoted to understanding the adoption and use of commercially available intelligent assistants, the relationship between user expectations from assistants and users’ endogenous intrinsic motivation to perform an activity has not been explored. Doing so is important to meet user expectations, prevent adoption failures, and design for well-being. In this paper, we investigate whether a person's intrinsic motivation (...)

Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis

Authors: Ashwin Prasad Shivarpatna Venkatesh, Jiawei Wang, Li Li, Eric Bodden

Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating and sharing machine-learning based solutions, primarily written in Python. Recent studies have demonstrated, however, that a large portion of Jupyter notebooks available on public platforms are undocumented and lacks a narrative structure. This reduces the readability of these notebooks. To address this shortcoming, this paper presents HeaderGen (...)

Learning Permutation-Invariant Embeddings for Description Logic Concepts

Authors: Caglar Demir, Axel-Cyrille Ngonga Ngomo

Concept learning deals with learning description logic concepts from a background knowledge and input examples. The goal is to learn a concept that covers all positive examples, while not covering any negative examples. This non-trivial task is often formulated as a search problem within an infinite quasi-ordered concept space. Although state-of-the-art models have been successfully applied to tackle this problem, their large-scale applications have been severely hindered due to their excessive exploration incurring impractical runtimes. Here, we propose a remedy for this limitation. (...)

Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs

Authors: Stefan Werner, Sebastian Peitz

The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution(...)