Integrating Uncertainty in the Semantic Web Stack

International audienceThe current Semantic Web Standards allow the existence of incomplete, invalid or more generally, uncertain data. This work introduces a framework to handle uncertainty in the Semantic Web : Uncertainty representation, reasoning over uncertain data, belief revision and propagation

Towards a Linked Open Code

International audienceIn the last two decades, the Linked Open Data paradigm has been experiencing exponential growth. Regularly, new datasets and ontologies are made publicly available, and novel projects are initiated to stimulate their continuous development and reuse, pushing more and more actors to adhere to the Semantic Web principles. The guidelines provided by the Semantic Web community allow to (i) homogeneously represent, (ii) uniquely identify, and (iii) uniformly reference any piece of information. However, the same standards do not allow defining and referencing the methods to exploit it: functions, procedures, algorithms, and code in general, are left out of this interconnected world. In this paper, we present our vision for a Web with Linked Open Code in which functions could be accessed and used as Linked Data, allowing logic harnessing the latter to be semantically described and FAIRly accessible. Hereafter, we describe the challenges presented by the implementation of our vision. We propose first insights on how to concretize it, and we provide a non-exhaustive list of communities that could benefit from such an ideal

Gestion de l'Incertitude pour la fiabilité des Données Liées dans le Web Sémantique

The Semantic Web has evolved to reach different applications. It was designed to enable machines to understand available resources on the World Wide Web and use the extracted information in the decision-making and reasoning processes. Hence, the Web is an open world where people can say whatever they want, and users -in this case, people and machines- can relate to it.One of the main challenges nowadays is to deal with information from multiple and mostly unreliable sources. The Linked Open Data is luckily presented in a machine-readable format. Whether automatically extracted from Web documents, directly introduced from data sources, or inferred by reasoning processes, the Linked Open Data can be outdated, incorrect, incomplete, vague, ambiguous, or more generally, uncertain. Dealing with Uncertain Linked Data faces multiple challenges like uncertainty qualification (or quantification), calculus, representation, and in the Semantic Web case: publishing and reusability. In this thesis, we analyze the existing results treating uncertainty in the Semantic Web. To introduce the proper terminology, we describe in Chapter 2 the preliminary notions related to uncertainty and the Semantic Web. We provide an overview of the technologies used in the Semantic Web stack and the limits of uncertain data. Afterward, we deliver in Chapter 3 a representation for uncertainty on the Semantic Web. We discuss our contribution of the “Uncertainty ontology” and the methods for annotating statements with uncertainty. Chapter 4 discusses uncertainty management and access in a contextualized view and the reading of uncertainty inside contexts. For sources without explicit uncertainty information, we present a framework in Chapter 5 enabling the evaluation of uncertainty based on syntactical and semantic similarities with entities from a reference source and within a specific use case. We conclude with a discussion about the dialogue between data sources and the consensual knowledge in the Semantic Web. We follow that with our perception of the reality and perspectives of this researchLe Web Sémantique a évolué pour faire partie de différents domaines d'application. Il a été conçu pour permettre aux machines de comprendre les ressources disponibles sur le World Wide Web et d'utiliser les informations extraites dans les processus de décision et de raisonnement. Le Web est donc un monde ouvert où les gens peuvent dire ce qu'ils veulent et où les utilisateurs - dans ce cas, les personnes et les machines - peuvent s'y retrouver. L'un des principaux défis actuels consiste à traiter des informations provenant de multiples sources, généralement peu fiables. Les données ouvertes liées sont présentées dans un format lisible par les machines. Qu'elles soient extraites automatiquement de documents Web, introduites directement à partir de sources de données ou déduites par des processus de raisonnement, les données ouvertes liées peuvent être périmées, incorrectes, incomplètes, vagues, ambiguës ou -plus généralement- incertaines. Le traitement des données liées incertaines fait face à de multiples défis tels que la qualification (ou quantification) de l'incertitude, le formalisme de calcul, la représentation, la publication et la réutilisation dans le Web sémantique.Dans cette thèse, nous analysons les résultats existants traitant l'incertitude dans le Web sémantique. Afin d'introduire la terminologie adéquate, nous décrivons au chapitre 2 les notions préliminaires liées à l'incertitude et au Web sémantique. Nous donnons un aperçu des technologies utilisées dans la pile du Web sémantique et des limites des données incertaines. Ensuite, nous livrons dans le chapitre 3 une représentation de l'incertitude sur le Web sémantique. Nous proposons une ontologie de l'incertitude et des méthodes d'annotation des données avec de l'incertitude. Le chapitre 4 considère l'incertitude des données dans une vue contextualisée et traite les différentes lectures de l'incertitude dans ces contextes. Pour les sources sans information explicite sur l'incertitude, nous présentons au chapitre 5 un cadre permettant l'évaluation de l'incertitude en se basant sur les similarités syntaxiques et sémantiques avec les entités d'une source de référence et dans un cas d'utilisation spécifique. Nous concluons par une discussion sur le dialogue entre les sources de données et la connaissance consensuelle dans le Web sémantique

Gestion de l'Incertitude pour la fiabilité des Données Liées dans le Web Sémantique

The Semantic Web has evolved to reach different applications. It was designed to enable machines to understand available resources on the World Wide Web and use the extracted information in the decision-making and reasoning processes. Hence, the Web is an open world where people can say whatever they want, and users -in this case, people and machines- can relate to it.One of the main challenges nowadays is to deal with information from multiple and mostly unreliable sources. The Linked Open Data is luckily presented in a machine-readable format. Whether automatically extracted from Web documents, directly introduced from data sources, or inferred by reasoning processes, the Linked Open Data can be outdated, incorrect, incomplete, vague, ambiguous, or more generally, uncertain. Dealing with Uncertain Linked Data faces multiple challenges like uncertainty qualification (or quantification), calculus, representation, and in the Semantic Web case: publishing and reusability. In this thesis, we analyze the existing results treating uncertainty in the Semantic Web. To introduce the proper terminology, we describe in Chapter 2 the preliminary notions related to uncertainty and the Semantic Web. We provide an overview of the technologies used in the Semantic Web stack and the limits of uncertain data. Afterward, we deliver in Chapter 3 a representation for uncertainty on the Semantic Web. We discuss our contribution of the “Uncertainty ontology” and the methods for annotating statements with uncertainty. Chapter 4 discusses uncertainty management and access in a contextualized view and the reading of uncertainty inside contexts. For sources without explicit uncertainty information, we present a framework in Chapter 5 enabling the evaluation of uncertainty based on syntactical and semantic similarities with entities from a reference source and within a specific use case. We conclude with a discussion about the dialogue between data sources and the consensual knowledge in the Semantic Web. We follow that with our perception of the reality and perspectives of this researchLe Web Sémantique a évolué pour faire partie de différents domaines d'application. Il a été conçu pour permettre aux machines de comprendre les ressources disponibles sur le World Wide Web et d'utiliser les informations extraites dans les processus de décision et de raisonnement. Le Web est donc un monde ouvert où les gens peuvent dire ce qu'ils veulent et où les utilisateurs - dans ce cas, les personnes et les machines - peuvent s'y retrouver. L'un des principaux défis actuels consiste à traiter des informations provenant de multiples sources, généralement peu fiables. Les données ouvertes liées sont présentées dans un format lisible par les machines. Qu'elles soient extraites automatiquement de documents Web, introduites directement à partir de sources de données ou déduites par des processus de raisonnement, les données ouvertes liées peuvent être périmées, incorrectes, incomplètes, vagues, ambiguës ou -plus généralement- incertaines. Le traitement des données liées incertaines fait face à de multiples défis tels que la qualification (ou quantification) de l'incertitude, le formalisme de calcul, la représentation, la publication et la réutilisation dans le Web sémantique.Dans cette thèse, nous analysons les résultats existants traitant l'incertitude dans le Web sémantique. Afin d'introduire la terminologie adéquate, nous décrivons au chapitre 2 les notions préliminaires liées à l'incertitude et au Web sémantique. Nous donnons un aperçu des technologies utilisées dans la pile du Web sémantique et des limites des données incertaines. Ensuite, nous livrons dans le chapitre 3 une représentation de l'incertitude sur le Web sémantique. Nous proposons une ontologie de l'incertitude et des méthodes d'annotation des données avec de l'incertitude. Le chapitre 4 considère l'incertitude des données dans une vue contextualisée et traite les différentes lectures de l'incertitude dans ces contextes. Pour les sources sans information explicite sur l'incertitude, nous présentons au chapitre 5 un cadre permettant l'évaluation de l'incertitude en se basant sur les similarités syntaxiques et sémantiques avec les entités d'une source de référence et dans un cas d'utilisation spécifique. Nous concluons par une discussion sur le dialogue entre les sources de données et la connaissance consensuelle dans le Web sémantique

Towards a Linked Open Code

International audienceIn the last two decades, the Linked Open Data paradigm has been experiencing exponential growth. Regularly, new datasets and ontologies are made publicly available, and novel projects are initiated to stimulate their continuous development and reuse, pushing more and more actors to adhere to the Semantic Web principles. The guidelines provided by the Semantic Web community allow to (i) homogeneously represent, (ii) uniquely identify, and (iii) uniformly reference any piece of information. However, the same standards do not allow defining and referencing the methods to exploit it: functions, procedures, algorithms, and code in general, are left out of this interconnected world. In this paper, we present our vision for a Web with Linked Open Code in which functions could be accessed and used as Linked Data, allowing logic harnessing the latter to be semantically described and FAIRly accessible. Hereafter, we describe the challenges presented by the implementation of our vision. We propose first insights on how to concretize it, and we provide a non-exhaustive list of communities that could benefit from such an ideal

Linking and Negotiating Uncertainty Theories Over Linked Data

International audienceThere is no credibility insurance measure for the information provided by the Web. In most cases, information cannot be checked for accuracy. Semantic Web technologies aimed to give structure and sense to information published on the Web and to provide us with a machine-readable data format for interlinked data. However, Semantic Web standards do not offer the possibility to represent and attach uncertainty to such data in a way allowing the reasoning over the latter. Moreover, uncertainty is context-dependent and may be represented by multiple theories which apply different calculi. In this paper, we present a new vocabulary and a framework for handling generic uncertainty representation and reasoning. The meta-Uncertainty vocabulary offers a way to represent uncertainty theories and annotate Linked Data with uncertainty information. We provide the tools to represent uncertainty calculi linked to the previous theories using the LDScript function scripting language. Moreover, we describe the semantics of contexts in uncertainty reasoning with meta-uncertainty. We describe the mapping between RDF triples and their uncertainty information, and we demonstrate the effect on the query writing process in Corese. We discuss the translatability of uncertainty theories and, finally, the negotiation of an answer annotated with uncertainty information

Linked Open Data Validity -- A Technical Report from ISWS 2018

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue

Linked Open Data Validity -- A Technical Report from ISWS 2018

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue