arXiv subjects

Bertram Ludäscher

Publications and source records attributed to Bertram Ludäscher.

At least 19 recordsLinked to original sources

arXiv2026

ABDA-NL: A Natural-Language Scenario Explorer for Argument-Based Reasoning

ABDA-NL adds a natural-language interface to ABDA, a system for argument-based discussion using ASPIC- knowledge bases under grounded semantics. Users see which conclusions are accepted, rejected, or undecided, open an interactive rendering of the grounded discussion game to learn why, explore what-if alternatives by suspending assumptions and rules or changing preferences, ask questions that are answered from a scenario's reference documents, and author new facts, assumptions, and rules in plain English. A large language model provides the bridge between language and formalism: it answers questions from the documents and the current state of the scenario, and it translates plain-English edits into candidate formal statements. The deterministic ABDA engine remains the sole source of arguments, attacks, and acceptance labels, and every proposal of the model is validated and confirmed by the user before it takes effect.

cs.AI↗

arXiv2026

Diamonds Are Forever: Stabilization Semantics for Unrestricted Aggregation and Recursion in Logica

Logica is an open-source logic programming language that compiles to SQL and runs on DuckDB, SQLite, PostgreSQL, and BigQuery. Unlike classic Datalog, it freely combines recursion and aggregation, concisely expressing algorithms from shortest paths to PageRank. This expressiveness raises semantic challenges: aggregates update by replacement rather than accumulation, evaluation depends on rule scheduling, and programs may converge to meaningful results without reaching a fixpoint, placing them outside traditional fixpoint semantics. We address this with Defendant-Opponent (DO) semantics, a stabilization-based framework for nonmonotonic logic programs. Evaluation is modeled as a rewrite system over derivation states, and a ground atom is true if, from every reachable state, some continuation makes the atom persist in all further derivations. This admits two equivalent characterizations: game-theoretically, truth is what a Defendant can defend against any Opponent in a three-turn game; and modally, truth corresponds to []<>[]t in the derivation graph viewed as a Kripke structure, placing nonmonotonic reasoning within S4. DO semantics coincides with least fixpoint semantics for positive Datalog and is compatible with both Well-Founded and Stable Model Semantics. For programs that converge without a fixpoint, ω-limit interpretations give rigorous meaning to iterative computations such as PageRank.

cs.LO↗

arXiv2026

Logical Robots: Declarative Multi-Agent Programming in Logica

We present Logical Robots, an interactive multi-agent simulation platform where autonomous robot behavior is specified declaratively in the logic programming language Logica. Robot behavior is defined by logical predicates that map observations from simulated radar arrays and shared memory to desired motor outputs. This approach allows low-level reactive control and high-level planning to coexist within a single programming environment, providing a coherent framework for exploring multi-agent robot behavior.

cs.MA↗

arXiv2025

CRABS: A syntactic-semantic pincer strategy for bounding LLM interpretation of Python notebooks

Recognizing the information flows and operations comprising data science and machine learning Python notebooks is critical for evaluating, reusing, and adapting notebooks for new tasks. Investigating a notebook via re-execution often is impractical due to the challenges of resolving data and software dependencies. While Large Language Models (LLMs) pre-trained on large codebases have demonstrated effectiveness in understanding code without running it, we observe that they fail to understand some realistic notebooks due to hallucinations and long-context challenges. To address these issues, we propose a notebook understanding task yielding an information flow graph and corresponding cell execution dependency graph for a notebook, and demonstrate the effectiveness of a pincer strategy that uses limited syntactic analysis to assist full comprehension of the notebook using an LLM. Our Capture and Resolve Assisted Bounding Strategy (CRABS) employs shallow syntactic parsing and analysis of the abstract syntax tree (AST) to capture the correct interpretation of a notebook between lower and upper estimates of the inter-cell I/O set$\unicode{x2014}$the flows of information into or out of cells via variables$\unicode{x2014}$then uses an LLM to resolve remaining ambiguities via cell-by-cell zero-shot learning, thereby identifying the true data inputs and outputs of each cell. We evaluate and demonstrate the effectiveness of our approach using an annotated dataset of 50 representative, highly up-voted Kaggle notebooks that together represent 3454 actual cell inputs and outputs. The LLM correctly resolves 1397 of 1425 (98%) ambiguities left by analyzing the syntactic structure of these notebooks. Across 50 notebooks, CRABS achieves average F1 scores of 98% identifying cell-to-cell information flows and 99% identifying transitive cell execution dependencies.

cs.CL↗

arXiv2025

AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark

Data cleaning is a time-consuming and error-prone manual process, even with modern workflow tools such as OpenRefine. We present AutoDCWorkflow, an LLM-based pipeline for automatically generating data-cleaning workflows. The pipeline takes a raw table and a data analysis purpose, and generates a sequence of OpenRefine operations designed to produce a minimal, clean table sufficient to address the purpose. Six operations correspond to common data quality issues, including format inconsistencies, type errors, and duplicates. To evaluate AutoDCWorkflow, we create a benchmark with metrics assessing answers, data, and workflow quality for 142 purposes using 96 tables across six topics. The evaluation covers three key dimensions: (1) Purpose Answer: can the cleaned table produce a correct answer? (2) Column (Value): how closely does it match the ground truth table? (3) Workflow (Operations): to what extent does the generated workflow resemble the human-curated ground truth? Experiments show that Llama 3.1, Mistral, and Gemma 2 significantly enhance data quality, outperforming the baseline across all metrics. Gemma 2-27B consistently generates high-quality tables and answers, while Gemma 2-9B excels in producing workflows that closely resemble human-annotated versions.

cs.DB↗

arXiv2025

On the Structure of Game Provenance and its Applications

Provenance in databases has been thoroughly studied for positive and for recursive queries, then for first-order (FO) queries, i.e., having negation but no recursion. Query evaluation can be understood as a two-player game where the opponents argue whether or not a tuple is in the query answer. This game-theoretic approach yields a natural provenance model for FO queries, unifying how and why-not provenance. Here, we study the fine-grain structure of game provenance. A game $G=(V,E)$ consists of positions $V$ and moves $E$ and can be solved by computing the well-founded model of a single, unstratifiable rule: \[ \text{win}(X) \leftarrow \text{move}(X, Y), \neg \, \text{win}(Y). \] In the solved game $G^λ$, the value of a position $x\,{\in}\,V$ is either won, lost, or drawn. This value is explained by the provenance $\mathscr{P}$(x), i.e., certain (annotated) edges reachable from $x$. We identify seven edge types that give rise to new kinds of provenance, i.e., potential, actual, and primary, and demonstrate that "not all moves are created equal". We describe the new provenance types, show how they can be computed while solving games, and discuss applications, e.g., for abstract argumentation frameworks.

cs.AI↗

arXiv2025

AF-XRAY: Visual Explanation and Resolution of Ambiguity in Legal Argumentation Frameworks

Argumentation frameworks (AFs) provide formal approaches for legal reasoning, but identifying sources of ambiguity and explaining argument acceptance remains challenging for non-experts. We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning. AF-XRAY introduces: (i) layered visualizations based on game-theoretic argument length revealing well-founded derivation structures; (ii) classification of attack edges by semantic roles (primary, secondary, blunders); (iii) overlay visualizations of alternative 2-valued solutions on ambiguous 3-valued grounded semantics; and (iv) identification of critical attack sets whose suspension resolves undecided arguments. Through systematic generation of critical attack sets, AF-XRAY transforms ambiguous scenarios into grounded solutions, enabling users to pinpoint specific causes of ambiguity and explore alternative resolutions. We use real-world legal cases (e.g., Wild Animals as modeled by Bench-Capon) to show that our tool supports teleological legal reasoning by revealing how different assumptions lead to different justified conclusions.

cs.AI↗

arXiv2025

Choices and their Provenance: Explaining Stable Solutions of Abstract Argumentation Frameworks

The rule $\mathrm{Defeated}(x) \leftarrow \mathrm{Attacks}(y,x),\, \neg \, \mathrm{Defeated}(y)$, evaluated under the well-founded semantics (WFS), yields a unique 3-valued (skeptical) solution of an abstract argumentation framework (AF). An argument $x$ is defeated ($\mathrm{OUT}$) if there exists an undefeated argument $y$ that attacks it. For 2-valued (stable) solutions, this is the case iff $y$ is accepted ($\mathrm{IN}$), i.e., if all of $y$'s attackers are defeated. Under WFS, arguments that are neither accepted nor defeated are undecided ($\mathrm{UNDEC}$). As shown in prior work, well-founded solutions (a.k.a. grounded labelings) "explain themselves": The provenance of arguments is given by subgraphs (definable via regular path queries) rooted at the node of interest. This provenance is closely related to winning strategies of a two-player argumentation game. We present a novel approach for extending this provenance to stable AF solutions. Unlike grounded solutions, which can be constructed via a bottom-up alternating fixpoint procedure, stable models often involve non-deterministic choice as part of the search for models. Thus, the provenance of stable solutions is of a different nature, and reflects a more expressive generate & test paradigm. Our approach identifies minimal sets of critical attacks, pinpointing choices and assumptions made by a stable model. These critical attack edges provide additional insights into the provenance of an argument's status, combining well-founded derivation steps with choice steps. Our approach can be understood as a form of diagnosis that finds minimal "repairs" to an AF graph such that the well-founded solution of the repaired graph coincides with the desired stable model of the original AF graph.

cs.AI↗

arXiv2025

Logica-TGD: Transforming Graph Databases Logically

Graph transformations are a powerful computational model for manipulating complex networks, but handling temporal aspects and scalability remain significant challenges. We present a novel approach to implementing these transformations using Logica, an open-source logic programming language and system that operates on parallel databases like DuckDB and BigQuery. Leveraging the parallelism of these engines, our method enhances performance and accessibility, while also offering a practical way to handle time-varying graphs. We illustrate Logica's graph querying and transformation capabilities with several examples, including the computation of the well-founded solution to the classic "Win-Move" game, a declarative program for pathfinding in a dynamic graph, and the application of Logica to the collection of all current facts of Wikidata for taxonomic relations analysis. We argue that clear declarative syntax, built-in visualization and powerful supported engines make Logica a convenient tool for graph transformations.

cs.DB↗

arXiv2024

SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics

Prompt-based fine-tuning has become an essential method for eliciting information encoded in pre-trained language models for a variety of tasks, including text classification. For multi-class classification tasks, prompt-based fine-tuning under low-resource scenarios has resulted in performance levels comparable to those of fully fine-tuning methods. Previous studies have used crafted prompt templates and verbalizers, mapping from the label terms space to the class space, to solve the classification problem as a masked language modeling task. However, cross-domain and fine-grained prompt-based fine-tuning with an automatically enriched verbalizer remains unexplored, mainly due to the difficulty and costs of manually selecting domain label terms for the verbalizer, which requires humans with domain expertise. To address this challenge, we introduce SciPrompt, a framework designed to automatically retrieve scientific topic-related terms for low-resource text classification tasks. To this end, we select semantically correlated and domain-specific label terms within the context of scientific literature for verbalizer augmentation. Furthermore, we propose a new verbalization strategy that uses correlation scores as additional weights to enhance the prediction performance of the language model during model tuning. Our method outperforms state-of-the-art, prompt-based fine-tuning methods on scientific text classification tasks under few and zero-shot settings, especially in classifying fine-grained and emerging scientific topics.

cs.CL↗

arXiv2024

Context, Composition, Automation, and Communication -- The C2AC Roadmap for Modeling and Simulation

Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing these limitations. The first is to provide better support for capturing, representing, and evaluating the context of simulation studies, including research questions, assumptions, requirements, and activities contributing to a simulation study. In addition, the composition of simulation models and other simulation studies' products must be supported beyond syntactical coherence, including aspects of semantics and purpose, enabling their effective reuse. A higher degree of automating simulation studies will contribute to more systematic, standardized simulation studies and their efficiency. Finally, it is essential to invest increased effort into effectively communicating results and the processes involved in simulation studies to enable their use in research and decision-making. These goals are not pursued independently of each other, but they will benefit from and sometimes even rely on advances in other subfields. In the present paper, we explore the basis and interdependencies evident in current research and practice and delineate future research directions based on these considerations.

cs.CE↗

arXiv2024

Reconciling Conflicting Data Curation Actions: Transparency Through Argumentation

We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal \emph{argumentation framework}(AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program $P_{AF}$ whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

cs.DB↗

arXiv2023

Games and Argumentation: Time for a Family Reunion!

The rule "defeated(X) $\leftarrow$ attacks(Y,X), $\neg$ defeated(Y)" states that an argument is defeated if it is attacked by an argument that is not defeated. The rule "win(X) $\leftarrow$ move(X,Y), $\neg$ win(Y)" states that in a game a position is won if there is a move to a position that is not won. Both logic rules can be seen as close relatives (even identical twins) and both rules have been at the center of attention at various times in different communities: The first rule lies at the core of argumentation frameworks and has spawned a large family of models and semantics of abstract argumentation. The second rule has played a key role in the quest to find the "right" semantics for logic programs with recursion through negation, and has given rise to the stable and well-founded semantics. Both semantics have been widely studied by the logic programming and nonmonotonic reasoning community. The second rule has also received much attention by the database and finite model theory community, e.g., when studying the expressive power of query languages and fixpoint logics. Although close connections between argumentation frameworks, logic programming, and dialogue games have been known for a long time, the overlap and cross-fertilization between the communities appears to be smaller than one might expect. To this end, we recall some of the key results from database theory in which the win-move query has played a central role, e.g., on normal forms and expressive power of query languages. We introduce some notions that naturally emerge from games and that may provide new perspectives and research opportunities for argumentation frameworks. We discuss how solved query evaluation games reveal how- and why-not provenance of query answers. These techniques can be used to explain how results were derived via the given query, game, or argumentation framework.

cs.LO↗

arXiv2023

Provenance for Lattice QCD workflows

We present a provenance model for the generic workflow of numerical Lattice Quantum Chromodynamics (QCD) calculations, which constitute an important component of particle physics research. These calculations are carried out on the largest supercomputers worldwide with data in the multi-PetaByte range being generated and analyzed. In the Lattice QCD community, a custom metadata standard (QCDml) that includes certain provenance information already exists for one part of the workflow, the so-called generation of configurations. In this paper, we follow the W3C PROV standard and formulate a provenance model that includes both the generation part and the so-called measurement part of the Lattice QCD workflow. We demonstrate the applicability of this model and show how the model can be used to answer some provenance-related research questions. However, many important provenance questions in the Lattice QCD community require extensions of this provenance model. To this end, we propose a multi-layered provenance approach that combines prospective and retrospective elements.

hep-lat↗

arXiv2023

KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution

Entity resolution has been an essential and well-studied task in data cleaning research for decades. Existing work has discussed the feasibility of utilizing pre-trained language models to perform entity resolution and achieved promising results. However, few works have discussed injecting domain knowledge to improve the performance of pre-trained language models on entity resolution tasks. In this study, we propose Knowledge Augmented Entity Resolution (KAER), a novel framework named for augmenting pre-trained language models with external knowledge for entity resolution. We discuss the results of utilizing different knowledge augmentation and prompting methods to improve entity resolution performance. Our model improves on Ditto, the existing state-of-the-art entity resolution method. In particular, 1) KAER performs more robustly and achieves better results on "dirty data", and 2) with more general knowledge injection, KAER outperforms the existing baseline models on the textual dataset and dataset from the online product domain. 3) KAER achieves competitive results on highly domain-specific datasets, such as citation datasets, requiring the injection of expert knowledge in future work.

cs.CL↗

arXiv2018

Validation and Inference of Schema-Level Workflow Data-Dependency Annotations

An advantage of scientific workflow systems is their ability to collect runtime provenance information as an execution trace. Traces include the computation steps invoked as part of the workflow run along with the corresponding data consumed and produced by each workflow step. The information captured by a trace is used to infer "lineage" relationships among data items, which can help answer provenance queries to find workflow inputs that were involved in producing specific workflow outputs. Determining lineage relationships, however, requires an understanding of the dependency patterns that exist between each workflow step's inputs and outputs, and this information is often under-specified or generally assumed by workflow systems. For instance, most approaches assume all outputs depend on all inputs, which can lead to lineage "false positives". In prior work, we defined annotations for specifying detailed dependency relationships between inputs and outputs of computation steps. These annotations are used to define corresponding rules for inferring fine-grained data dependencies from a trace. In this paper, we extend our previous work by considering the impact of dependency annotations on workflow specifications. In particular, we provide a reasoning framework to ensure the set of dependency annotations on a workflow specification is consistent. The framework can also infer a complete set of annotations given a partially annotated workflow. Finally, we describe an implementation of the reasoning framework using answer-set programming.

cs.DB↗

arXiv2018

Computing Environments for Reproducibility: Capturing the "Whole Tale"

The act of sharing scientific knowledge is rapidly evolving away from traditional articles and presentations to the delivery of executable objects that integrate the data and computational details (e.g., scripts and workflows) upon which the findings rely. This envisioned coupling of data and process is essential to advancing science but faces technical and institutional barriers. The Whole Tale project aims to address these barriers by connecting computational, data-intensive research efforts with the larger research process--transforming the knowledge discovery and dissemination process into one where data products are united with research articles to create "living publications" or "tales". The Whole Tale focuses on the full spectrum of science, empowering users in the long tail of science, and power users with demands for access to big data and compute resources. We report here on the design, architecture, and implementation of the Whole Tale environment.

cs.CY↗

arXiv2014

Euler/X: A Toolkit for Logic-based Taxonomy Integration

We introduce Euler/X, a toolkit for logic-based taxonomy integration. Given two taxonomies and a set of alignment constraints between them, Euler/X provides tools for detecting, explaining, and reconciling inconsistencies; finding all possible merges between (consistent) taxonomies; and visualizing merge results. Euler/X employs a number of different underlying reasoning systems, including first-order reasoners (Prover9 and Mace4), answer set programming (DLV and Potassco), and RCC reasoners (PyRCC8). We demonstrate the features of Euler/X and provide experimental results showing its feasibility on various synthetic and real-world examples.

cs.LO↗