News

Yiwen Peng

FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic

Knowledge graph alignment is the task of matching equivalent entities (that is, instances and classes) and relations across two knowledge graphs. Most existing methods focus on pure entity-level alignment, computing the similarity of entities in some embedding space. They lack interpretable reasoning and need training data to work. To solve these issues, we introduce FLORA, a simple yet effective method that (1) is unsupervised, i.e., does not require training data, (2) provides a holistic alignment for entities and relations iteratively, (3) is based on fuzzy logic and thus delivers interpretable results, (4) provably converges, (5) allows dangling entities, i.e., entities without a counterpart in the other KG, and (6) achieves state-of-the-art results on major benchmarks.

Fabian Suchanek

Fabian spent the month of June 2025 at the Sapienza in Rome, and will tell us about his experience.

Adrien Coulet

Data- and knowledge-driven approaches for step-by-step guidance to differential diagnosis

Diagnosis guidelines provide recommendations based on expert consensus that cover the majority of the population, but often overlook patients with uncommon conditions or multiple morbidities. We will present and compare two alternative approaches that provide a step-by-step guidance to the differential diagnosis of anemia and lupus. The first approach relies on reinforcement learning and observational data. The second on large langage models and domain knowledge.

Adrien Coulet (INRIA)

Data- and knowledge-driven approaches for step-by-step guidance to differential diagnosis

Diagnosis guidelines provide recommendations based on expert consensus that cover the majority of the population, but often overlook patients with uncommon conditions or multiple morbidities. We will present and compare two alternative approaches that provide a step-by-step guidance to the differential diagnosis of anemia and lupus. The first approach relies on reinforcement learning and observational data. The second on large langage models and domain knowledge.

Maximilian Egger

Robust Knowledge Graph Cleaning

Data quality is needed to properly and reliably use the information represented in the dataset. The increasing volume of data renders data preparation and cleaning increasingly difficult. Additionally, more diverse types of data structures for databases, like graphs, get used and need to be handled differently. This leads to the necessity of robust methods to increase data integrity, scalable approaches for finding and fixing errors, and local-oriented algorithms that can be used to pinpoint attention where needed.

This talk provides an overview of my past, present, and future projects on knowledge graphs, exploring their potential for improving data cleanliness and robustness.

Nikola Simidjievski

Synthesis & Augmentation of Tabular Data In the Age of Foundation Models

Foundation models – large pre-trained performant models – have shown remarkable success in applications that predominately focus on vision, language, and sound data. On the other hand, tabular data – one of the most prevalent data modalities in many critical domains of business, science, and healthcare – has seen limited benefits from these advances. Tabular data poses unique challenges that relate to heterogeneity, dimensionality, and scarcity as well as lack of explicit symmetries, implicit structures and incomplete prior knowledge — all of which have limiting effects on how we construct, train and apply/transfer large models for tabular data.

Data synthesis is one of the remedies for overcoming some of these challenges: It can help improve model performance in data-scarce but critical applications, but it can also be utilized as a data augmentation mechanism for training more robust models. Although previous research has sought to adapt the successes of generative modeling of homogeneous modalities to tabular modalities, defining an effective generator for tabular data remains an open problem. In this talk, I will present several novel data-centric approaches for data synthesis that focus on tabular data. Our key innovation is transforming recent pre-trained tabular classifiers into data generators and leveraging their learned information in the input and manifold space. These methods are fast, require no additional training, and can be applied to any downstream predictive model. They consistently improve performance, especially on small datasets where training well-performing models is hard. Consequently, we also uncover several properties and benefits that can help in the way how we design robust and performant general-purpose tabular foundation models.

Simon Razniewski (TU Dresden)

GPTKB: Comprehensively Materializing Factual LLM Knowledge

LLMs have majorly advanced NLP and AI, and next to their ability to perform a wide range of procedural tasks, a major success factor is their internalized factual knowledge. Since (Petroni et al., 2019), analyzing this knowledge has gained attention. However, most approaches investigate one question at a time via modest-sized pre-defined samples, introducing an “availability bias” (Tversky and Kahneman, 1973) that prevents the discovery of knowledge (or beliefs) of LLMs beyond the experimenter’s predisposition. To address this challenge, we propose a novel methodology to comprehensively materialize an LLM’s factual knowledge through recursive querying and result consolidation. As a prototype, we employ GPT-4o-mini to construct GPTKB, a large-scale knowledge base (KB) comprising 101 million triples for over 2.9 million entities. This work marks a milestone in two areas: For LLM research, for the first time, it provides constructive insights into the scope and structure of LLMs’ knowledge (or beliefs), and its strengths and weaknesses. For KB construction, it pioneers new pathways for the long-standing challenge of general-domain KB construction. GPTKB is accessible at https://gptkb.org.

Pratik Karmakar

ProvSQL: Provenance and Probabilistic Querying in Uncertain Databases

Probabilistic databases provide a powerful framework for managing and querying uncertain data, enabling principled reasoning under uncertainty. ProvSQL extends PostgreSQL to support provenance tracking and probability computation in probabilistic databases, leveraging provenance circuits to efficiently compute probabilities and Shapley-based data valuations. In this talk, we introduce ProvSQL, demonstrate its capabilities, and explore a key use case—content based image retrieval from the COCO dataset. We show how probabilistic query evaluation and data valuation techniques enhance explainability and trust in AI-driven decision-making.

Gaël Varoquaux (INRIA)

Tabular foundation models: priors for numbers and strings

Deep-learning typically does not outperform tree-based models on tabular data. Often this may be explained by the small size of such datasets. For images, sound, text, the solution has be pretrained models, leading to foundation models, adapted and reused for many tasks. I will discuss the challenges to bring these ideas to tabular learning, and the progress that we have made, building priors for tables, ie columns of different natures, with numbers and strings.

Pierre Monnin (INRIA)

Neuro-symbolic approaches for the knowledge graph lifecycle

In the Web of Data, an increasing number of knowledge graphs (KGs) are concurrently published, edited, and accessed by human and software agents. Their wide adoption makes essential the tasks of their lifecycle: construction, refinement (e.g., matching, link prediction), mining, and usage to support applications (e.g., explainable AI, recommender systems). However, all these tasks require facing the inherent heterogeneity of KGs, e.g., in terms of granularities, vocabularies, and completeness. Besides, scalability issues arise due to their increasing size and combinatorial nature. In my talk, I will present my research on neuro-symbolic approaches for the KG lifecycle, intertwining domain knowledge from ontologies, deductive reasoning, analogical reasoning, and machine learning models. Throughout my presentation, I will show that such approaches enhance models by improving their semantic awareness, frugality, and the semantic interpretability of their latent representation space.

Ken Satoh (National Institute of Informatics, Japan)

Translating German traffic cases into logical rules

This is a joint work with May Myo Zin at my center and Georg Borgess at University of Saarland. In this talk, I will report the work on extracting normative sentences from German traffic cases and translating them into logical rules. The development of autonomous vehicles (AVs) requires a comprehensive understanding of both explicit and implicit traffic rules to ensure legal compliance and safety. While explicit traffic laws are well-defined in statutes and regulations, implicit rules derived from judicial interpretations and case law are more nuanced and challenging to extract. This research firstly investigates the potential of Large Language Models (LLMs), particularly GPT-4o, in automating the extraction of implicit traffic normative sentences from judicial decisions. Then we investigate how to translate these normative sentences into a logical form. We explore to use large language models (LLMs) to automate the translation of traffic rules into PROLOG, a declarative programming language ideal for encoding logical rules and relationships. The proposed methodology consists of three key phases: extracting traffic rules from diverse textual sources, structuring them into Logical English (LE) for clarity and consistency, and translating them into PROLOG representations using advanced natural language processing (NLP) techniques, including in-context learning and fine-tuning. The experimental results demonstrate the effectiveness of LLMs in automating this process, achieving high accuracy in translation.