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International audience; Data linking in knowledge graphs is a crucial and long-standing problem; it involves finding links between descriptions of entities in these graphs that refer to the same real-world entity. Keys, which are subsets of properties that identify each entity, are important elements in finding these identity links. The classical key-based approach if data linking is based on the discovery of a set of keys in each graph, and then apply a merge (e.g. intersection) of these keys. But this approach can be very time consuming and can sometimes lead to very few common keys between the two graphs. In this work, in order to reduce the computational complexity of the data linking task using keys, we study the issue of transferability of keys discovered in one graph to another graph. More precisely, we conducted experiments on DBpedia and Wikidata in order to evaluate the performance impact of key transferability both in terms of computation time and quality of transferred keys.; Le liage de données dans des graphes de connaissances est un problème crucial et de longue date ; il consiste à déterminer des liens entre les descriptions des entités de ces graphes désignant une même entité du monde réel. Les clés, qui sont des sous-ensembles de propriétés permettant d'identifier chaque instance d'un graphe, sont des éléments importants pour la découverte de ces liens d'identité. L'approche classique de liage de données fondée sur les clés consiste à découvrir un ensemble de clés dans chaque graphe, et ensuite appliquer une procédure de fusion (e.g., le produit cartésien des clés). Mais cette approche peut être très coûteuse en temps et peut parfois conduire à très peu de clés communes entre les deux graphes. Dans ce travail, afin

International audience; Redirection of URIs is widely used in the LOD cloud, and is even part of the best practice guidelines as an approach to the "curation problem" on the semantic web (i.e. how to repair imperfections). When dereferencing, one URI is redirected to another URI. Such a redirection could be the result of an update of the namespace, a different encoding scheme, or some other reasons. In this paper, we study the semantics of redirection and examine if redirection indicates how entities in the LOD cloud evolve. More specifically, we focus on entities in the identity graphs: subgraphs in the semantic web restricted to identity links. The entities we study are from sameAs.cc, an identity graph extracted from a crawl of the semantic web in 2015. Our analytical results include an examination of edges and chains of redirection as well as a statistical analysis of the redirection behavior of sampled entities. Additionally, we present properties of the graphs formed by redirection relations.

Un certain nombre de graphes de connaissances (GC) sur le Web de données contiennent des déclarations contradictoires et sont donc logiquement incohérents. Des méthodes existent pour expliquer une seule contradiction en trouvant ses justifications, qui représente l'ensemble minimal d'axiomes suffisant pour la produire. Dans les grands GC, ces justifications peuvent être fréquentes et peuvent faire référence de manière redondante au même type d'erreur en modélisation. De plus, ces justifications sont par définition dépendantes du domaine, donc difficiles à interpréter ou à comparer. Cet article utilise la notion d'anti-motif (antipattern) pour généraliser ces justifications, et présente une approche pour détecter presque tous les anti-motifs à partir de n'importe quel GC incohérent. Des expériences sur des GC de plus de 28 milliards de faits montrent l'évolutivité de cette approche et les avantages des anti-motifs pour analyser et comparer les erreurs logiques entre différents GC. Cet article a été déjà publié à ESWC 2021.

A review of: - Methodologies, methods and tools used for the construction and evaluation of food ontologies - Integration of food ontologies in real world application to solve real world problems

A century ago, the 1918–19 influenza pandemic swept across the globe, taking the lives of over 50 million people. We use data from the Dutch civil registry to show which regions in the Netherlands were most affected by the 1918–19 pandemic. We do so for the entire 1918 year as well as the first, second, and third wave that hit the Netherlands in summer 1918, autumn 1918, and winter 1919. Our analyses show that excess mortality was highest in Oost-Brabant, Zuid-Limburg, Noord-Holland, and Drenthe, Groningen, and Overijssel, whereas excess mortality was low in Zuid-Beveland, the Utrechtse Heuvelrug, and the Achterhoek. Furthermore, neighboring municipalities resembled one another in how severely they were affected, but only for the second wave that hit the Netherlands in autumn 1918. This non-random spatial distribution of excess mortality in autumn 1918 suggests that regional differences affected the spread of the disease.

Finding and linking different appearances of the same entity in an open Web setting is one of the primary challenges of the Semantic Web. In social and economic history, record linkage has dealt with this problem for a long time, linking historical individual records at a local database level. With the advent of semantic technologies, Knowledge Graphs containing these records have been published, raising the need for large-scale linking techniques that consider the particularities of historical individual linking. In this paper we focus on our current investigation of such techniques to link the Dutch civil certificates in the LINKS/CLARIAH project. We describe the production of the LINKS Knowledge Graph, and we show its potential at answering domain research questions through its large number of owl:sameAs links.

In the absence of a central naming authority on the Web of data, it is common for different knowledge graphs to refer to the same thing by different names (IRIs). Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Such identity statements have strict logical semantics, indicating that every property asserted to one name, will also be inferred to the other, and vice versa. While such inferences can be extremely useful in enabling and enhancing knowledge-based systems such as search engines and recommendation systems, incorrect use of identity can have wide-ranging effects in a global knowledge space like the Web of data. With several studies showing that owl:sameAs is indeed misused for different reasons, a proper approach towards the handling of identity links is required in order to make the Web of data succeed as an integrated knowledge space. This thesis investigates the identity problem at hand, and provides different, yet complementary solutions. Firstly, it presents the largest dataset of identity statements that has been gathered from the LOD Cloud to date, and a web service from which the data and its equivalence closure can be queried. Such resource has both practical impacts (it helps data users and providers to find different names for the same entity), as well as analytical value (it reveals important aspects of the connectivity of the LOD Cloud). In addition, by relying on this collection of 558 million identity statements, we show how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect possibly erroneous identity assertions. For this, we assign an error degree for each owl:sameAs based on the density of the community(ies) in which they occur, and their symmetrical characteristics. One benefit of this approach is that it does not rely on any additional knowledge. Finally, as a way to limit the excessive and incorrect use of owl:sameAs, we define a new relation for asserting the identity of two ontology instances in a specific context (a sub-ontology). This identity relation is accompanied with an approach for automatically detecting these links, with the ability of using certain expert constraints for filtering irrelevant contexts. As a first experiment, the detection and exploitation of the detected contextual identity links are conducted on two knowledge graphs for life sciences, constructed in a mutual effort with domain experts from the French National Institute of Agricultural Research (INRA).; En l'absence d'une autorité de nommage centrale sur le Web de données, il est fréquent que différents graphes de connaissances utilisent des noms (IRIs) différents pour référer à la même entité. Chaque fois que plusieurs noms sont utilisés pour désigner la même entité, les faits owl:sameAs sont nécessaires pour déclarer des liens d’identité et améliorer l’exploitation des données disponibles. De telles déclarations d'identité ont une sémantique logique stricte, indiquant que chaque propriété affirmée à un nom sera également déduite à l'autre et vice versa. Bien que ces inférences puissent être extrêmement utiles pour améliorer les systèmes fondés sur les connaissances tels que les moteurs de recherche et les systèmes de recommandation, l'utilisation incorrecte de l'identité peut avoir des effets négatifs importants dans un espace de connaissances global comme le Web de données. En effet, plusieurs études ont montré que owl:sameAs est parfois incorrectement utilisé sur le Web des données. Cette thèse étudie le problème de liens d’identité erronés ou inappropriés qui sont exprimés par des liens owl:sameAs et propose des solutions différentes mais complémentaires. Premièrement, elle présente une ressource contenant la plus grande collection de liens d’identité collectés du LOD Cloud, avec un service Web à partir duquel les données et leur clôture transitive peuvent être interrogées. Une telle ressource a à la fois des impacts pratiques (elle aide les utilisateurs à trouver différents noms pour la même entité), ainsi qu'une valeur analytique (elle révèle des aspects importants de la connectivité du LOD Cloud). En outre, en s’appuyant sur cette collection de 558 millions liens d’identité, nous montrons comment des mesures de réseau telles que la structure de communauté du réseau owl:sameAs peuvent être utilisées afin de détecter des liens d’identité éventuellement erronées. Pour cela, nous attribuons un degré d'erreur pour chaque lien owl:sameAs en fonction de la densité de la ou des communautés dans lesquelles elles se produisent et de leurs caractéristiques symétriques. L'un des avantages de cette approche est qu'elle ne repose sur aucune connaissance supplémentaire. Finalement, afin de limiter l'utilisation excessive et incorrecte du owl:sameAs, nous définissons une nouvelle relation pour représenter l'identité de deux instances d’une classe dans un contexte spécifique (une sous-partie de l’ontologie). Cette relation d'identité s'accompagne d'une approche permettant de détecter automatiquement ces liens, avec la possibilité d'utiliser certaines contraintes expertes pour filtrer des contextes non pertinents. La détection et l’exploitation des liens d’identité contextuels détectés sont effectuées sur deux graphes de connaissances pour les sciences de la vie, construits en collaboration avec des experts du domaine de l’institut national de la recherche agronomique (INRA).

This paper presents a use case about knowledge representation and integration of data from different domains in food science. An ontology named PO2DG, the Process and Observation Ontology for the production of Dairy Gels, has been designed in order to provide a shared vocabulary for domain experts. The available data have been semantically structured using PO2DG and are stored in an RDF repository named PO2DG dataset. This use case identifies some of the challenges when dealing with a multi domain representation problem, gives some hints about possible solutions and suggests some further work.

Different studies have observed that the semantic web identity predicate owl:SameAs is sometimes used incorrectly. In this paper, we show how network metrics such as the community structure of the owl:SameAs graph can be used in order to detect such possibly erroneous statements. One benefit of the here presented approach is that it can be applied to the network of owl:SameAs links, and does not rely on any additional knowledge. We evaluate our approach on 558M owl:SameAs statements scraped from the LOD cloud. This evaluation shows the ability of our approach to scale, and its efficiency in detecting erroneous identity links.

Chapter 13 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings; International audience; This paper focuses on the knowledge representation task for an interdisciplinary project called Delicious concerning the production and transformation processes in food science. The originality of this project is to combine data from different disciplines like food composition, food structure, sensorial perception and nutrition. Available data sets are described using different vocabularies and are stored in different formats. Therefore there is a need to define an ontology, called PO2PO2 (Process and Observation Ontology), as a common and standardized vocabulary for this project. The scenario 6 of the NeON methodology was used for building PO2PO2 and the core component is implemented in OWL. By making use of PO2PO2 , data from the project were structured and an use case is presented here. PO2PO2 aims to play a key role as the representation layer of the querying and simulation systems of Delicious project.

This data set contains sameAs links collected from the LOD cloud. Every line of the links contains the following: Subject, Predicate, Object, Assertion*, Source, Folder path ** * if a triple is not asserted in the LOD cloud, then it's replaced by the tag so that we have the assertions in a single column. ** The folder pathwhere the sameAs link was found in the LOD cloud