Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution

Optimisation des techniques merchandising à travers l’utilisation de données sémantiques

The overall objective of this PhD is to explore and propose new approaches leveraging a large volume of heterogeneous data that needs to be integrated and semantically enriched, and recent advances in machine and deep learning techniques, in order to exploit both the increased variety of offers that an airline can make to its customers as well as the knowledge it has of its customers with the ultimate goal of optimizing conversion and purchase. The overall goal of this thesis can be broken down into three main research questions: 1) What piece of content (ancillary services, third-party content) should be recommended and personalized to each traveler? 2) When should a recommendation be made and for which communication channel to optimize conversion? 3) How do we group ancillary services and third-party content and can we learn what often goes together based on purchase logs?L'objectif général de ce doctorat consiste à explorer et proposer de nouvelles approches s'appuyant sur un grand volume de données hétérogènes qui doivent être intégrées et enrichies sémantiquement, et sur les progrès récents dans les techniques d'apprentissage automatique et profond, afin d'exploiter à la fois la variété accrue d'offres qu'une compagnie aérienne peut faire à ses clients ainsi que la connaissance qu'elle a de ses clients dans le but ultime d'optimiser la conversion et l'achat. L'objectif général de cette thèse peut être décomposé en trois questions de recherche principales : 1) Quel élément de contenu (services auxiliaires, contenu tiers) devrait être recommandé et personnalisé à chaque voyageur ? 2) Quand une recommandation doit-elle être faite et pour quel canal de communication afin d'optimiser la conversion ? 3) Comment regrouper les services auxiliaires et le contenu tiers et pouvons-nous apprendre ce qui va souvent ensemble selon les journaux d'achat

Optimisation des techniques merchandising à travers l’utilisation de données sémantiques

L'objectif général de ce doctorat consiste à explorer et proposer de nouvelles approches s'appuyant sur un grand volume de données hétérogènes qui doivent être intégrées et enrichies sémantiquement, et sur les progrès récents dans les techniques d'apprentissage automatique et profond, afin d'exploiter à la fois la variété accrue d'offres qu'une compagnie aérienne peut faire à ses clients ainsi que la connaissance qu'elle a de ses clients dans le but ultime d'optimiser la conversion et l'achat. L'objectif général de cette thèse peut être décomposé en trois questions de recherche principales : 1) Quel élément de contenu (services auxiliaires, contenu tiers) devrait être recommandé et personnalisé à chaque voyageur ? 2) Quand une recommandation doit-elle être faite et pour quel canal de communication afin d'optimiser la conversion ? 3) Comment regrouper les services auxiliaires et le contenu tiers et pouvons-nous apprendre ce qui va souvent ensemble selon les journaux d'achat ?The overall objective of this PhD is to explore and propose new approaches leveraging a large volume of heterogeneous data that needs to be integrated and semantically enriched, and recent advances in machine and deep learning techniques, in order to exploit both the increased variety of offers that an airline can make to its customers as well as the knowledge it has of its customers with the ultimate goal of optimizing conversion and purchase. The overall goal of this thesis can be broken down into three main research questions: 1) What piece of content (ancillary services, third-party content) should be recommended and personalized to each traveler? 2) When should a recommendation be made and for which communication channel to optimize conversion? 3) How do we group ancillary services and third-party content and can we learn what often goes together based on purchase logs