Vers un cadre de collaboration pour l'ingénierie de l'ontologie : Vers un cadre de collaboration pour limpact sur l'évolution de l'ontologie et les pièges dans les réseaux d'ontologie et les ontologies versionnées

Ontologies are at the heart of the semantic web. Using ontologies leads to a better understanding, sharing and analyzing of knowledge in a specific domain. However, domains’ description are subject to changes, thus arises the need to evolve ontologies in order to have an adequate representation of the targeted domain. In this thesis, we assume that studying how the development and evolution of ontologies affect and is affected by the evolution of related artifacts, may help knowledge engineers in their tasks. Artifacts can be either external ontologies that are connected to a specific ontology or a service that take advantage of a specific ontology. Hence, we build up upon a comprehensive ontology evolution life-cycle. We introduce the following contributions: 1. a definition for a situation to detect the need of ontology evolution, 2. an original approach for ontology enrichment using external knowledge bases, 3. a new definition related to ontology evolution, named “ontology co-evolution” is used to assess the impact of ontology evolution, and 4. a new categorization of ontology pitfalls along with an evaluation of their importance and potential impact on versioned ontologies and ontology networks.Les ontologies sont au cœur du web sémantique. L’utilisation d’ontologies permet de mieux comprendre, partager et analyser les connaissances dans un domaine spécifique. Cependant, la description des domaines est sujette à modifications, d’où la nécessité de faire évoluer des ontologies afin d’avoir une représentation adéquate du domaine visé. Dans cette thèse, nous supposons que l’étude du développement et l’évolution des ontologies affectent et sont affectées par l’évolution des artefacts peut aider les ingénieurs du savoir dans leurs tâches. Par conséquent, nous nous appuyons sur un cycle de vie complet d’évolution d’ontologie. Nos contributions sont les suivantes: 1. une définition d’une situation pour détecter le besoin d’évolution des ontologies, 2. une approche originale pour l’enrichissement des ontologies utilisant des bases de connaissances externes, 3. une nouvelle définition liée à l’évolution des ontologies, nommée “co-évolution d’ontologie” est utilisé pour évaluer l’impact de l’évolution des ontologies, et 4. une nouvelle catégorisation des écueils dans le développement ou l’évolution des ontologies ainsi qu’une évaluation de leur importance et de leur impact potentiel sur les ontologies versionnées et les réseaux d’ontologies

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