In the modern data-driven world, answering queries over heterogeneous and semantically inconsistent data remains a significant challenge. Modern datasets originate from diverse sources, such as relational databases, semi-structured repositories, and unstructured documents, leading to substantial variability in schemas, terminologies, and data formats. Traditional systems, constrained by rigid syntactic matching and strict data binding, struggle to capture critical semantic connections and schema ambiguities, failing to meet the growing demand among data scientists for advanced forms of flexibility and context-awareness in query answering. In parallel, the advent of Large Language Models (LLMs) has introduced new capabilities in natural language interpretation, making them highly promising for addressing such challenges. However, LLMs alone lack the systematic rigor and explainability required for robust query processing and decision-making in high-stakes domains. In this paper, we propose Soft Query Answering (Soft QA), a novel hybrid approach that integrates LLMs as an intermediate semantic layer within the query processing pipeline. Soft QA enhances query answering adaptability and flexibility by injecting semantic understanding through context-aware, schema-informed prompts, and leverages LLMs to semantically link entities, resolve ambiguities, and deliver accurate query results in complex settings. We demonstrate its practical effectiveness through real-world examples, highlighting its ability to resolve semantic mismatches and improve query outcomes without requiring extensive data cleaning or restructuring.

Abstract We provide a framework for probabilistic reasoning in Vadalog-based Knowledge Graphs (KGs), satisfying the requirements of ontological reasoning: full recursion, powerful existential quantification, expression of inductive definitions. Vadalog is a Knowledge Representation and Reasoning (KRR) language based on Warded Datalog+/–, a logical core language of existential rules, with a good balance between computational complexity and expressive power. Handling uncertainty is essential for reasoning with KGs. Yet Vadalog and Warded Datalog+/– are not covered by the existing probabilistic logic programming and statistical relational learning approaches for several reasons, including insufficient support for recursion with existential quantification and the impossibility to express inductive definitions. In this work, we introduce Soft Vadalog, a probabilistic extension to Vadalog, satisfying these desiderata. A Soft Vadalog program induces what we call a Probabilistic Knowledge Graph (PKG), which consists of a probability distribution on a network of chase instances, structures obtained by grounding the rules over a database using the chase procedure. We exploit PKGs for probabilistic marginal inference. We discuss the theory and present MCMC-chase, a Monte Carlo method to use Soft Vadalog in practice. We apply our framework to solve data management and industrial problems and experimentally evaluate it in the Vadalog system.