Author name cluster

Nabil Layaïda

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4 papers

2 author rows

NeSy Conference 2025 Conference Paper

A Comparative Analysis of Neurosymbolic Methods for Link Prediction

Guillaume Delplanque
Luisa Werner
Nabil Layaïda
Pierre Genevès

Link prediction on knowledge graphs is relevant to various applications, such as recommendation systems, question answering, and entity search. This task has been approached from different perspectives: symbolic methods leverage rule-based reasoning but struggle with scalability and noise, while knowledge graph embeddings (KGE) represent entities and relations in a continuous space, enabling scalability but often neglecting logical constraints from ontologies. Recently, neurosymbolic approaches have emerged to bridge this gap by integrating embedding-based learning with symbolic reasoning. This paper provides a structured review of state-of-the-art neurosymbolic methods for link prediction. Beyond a qualitative analysis, a key contribution of this work is a comprehensive experimental benchmarking, where we systematically compare these methods on the same datasets using the same metrics. This unified experimental setup allows for a fair assessment of their strengths and limitations, bringing elements of answers to following key questions: How accurate are these methods? How scalable are they? How beneficial are they for different levels of provided knowledge and to which extent are they robust to incorrect knowledge?

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AAAI Conference 2024 Conference Paper

Reproduce, Replicate, Reevaluate. The Long but Safe Way to Extend Machine Learning Methods

Luisa Werner
Nabil Layaïda
Pierre Genevès
Jérôme Euzenat
Damien Graux

Reproducibility is a desirable property of scientific research. On the one hand, it increases confidence in results. On the other hand, reproducible results can be extended on a solid basis. In rapidly developing fields such as machine learning, the latter is particularly important to ensure the reliability of research. In this paper, we present a systematic approach to reproducing (using the available implementation), replicating (using an alternative implementation) and reevaluating (using different datasets) state-of-the-art experiments. This approach enables the early detection and correction of deficiencies and thus the development of more robust and transparent machine learning methods. We detail the independent reproduction, replication, and reevaluation of the initially published experiments with a method that we want to extend. For each step, we identify issues and draw lessons learned. We further discuss solutions that have proven effective in overcoming the encountered problems. This work can serve as a guide for further reproducibility studies and generally improve reproducibility in machine learning.

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TCS Journal 2020 Journal Article

Backward type inference for XML queries

Hyeonseung Im
Pierre Genevès
Nils Gesbert
Nabil Layaïda

Although XQuery is a statically typed, functional query language for XML data, some of its features such as upward and horizontal XPath axes are typed imprecisely. The main reason is that while the XQuery data model allows us to navigate upwards and between siblings from a given XML node, the type model, e. g. , regular tree types, can describe only the subtree structure of the given node. To alleviate this limitation, precise forward type inference systems for XQuery were recently proposed using an extended regular type language that can describe not only a given XML node but also its context. In this paper, as a different approach, we propose a novel backward type inference system for XQuery, based on a type language extended with logical formulas. Our backward type inference system provides an exact typing result for XPath axes and a sound typing result for XQuery expressions.

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AAAI Conference 2012 Conference Paper

SPARQL Query Containment Under SHI Axioms

Melisachew Wudage Chekol
Jérôme Euzenat
Pierre Genevès
Nabil Layaïda

SPARQL query containment under schema axioms is the problem of determining whether, for any RDF graph satisfying a given set of schema axioms, the answers to a query are contained in the answers of another query. This problem has major applications for verification and optimization of queries. In order to solve it, we rely on the µ-calculus. Firstly, we provide a mapping from RDF graphs into transition systems. Secondly, SPARQL queries and RDFS and SH I axioms are encoded into µ-calculus formulas. This allows us to reduce query containment and equivalence to satisfiability in the µcalculus. Finally, we prove a double exponential upper bound for containment under SH I schema axioms.

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