Combining Flexible Queries and Knowledge Anchors to facilitate the exploration of Knowledge Graphs

Semantic web and information extraction technologies are enabling the creation of vast information and knowledge repositories, particularly in the form of knowledge graphs comprising entities and the relationships between them. Users are often unfamiliar with the complex structure and vast content of such graphs. Hence, users need to be assisted by tools that support interactive exploration and flexible querying. In this paper we draw on recent work in flexible querying for graph-structured data and identifying good anchors for knowledge graph exploration in order to demonstrate how users can be supported in incrementally querying, exploring and learning from large complex knowledge graphs. We demonstrate our techniques through a case study in the domain of lifelong learning and career guidance

Création et utilisation d'un résumé de métadonnées pour interroger efficacement des collections multimédias distribuées

National audienceActuellement, de nombreux contenus multimédias sont créés à partir de plusieurs sources et stockés dans des environnements distribués. Pour éviter de centraliser l'ensemble des métadonnées d'un système et répondre efficacement à une requête d'un utilisateur, nous proposons d'engendrer et d'utiliser un résumé de métadonnées. Ce dernier aura pour fonction de localiser certaines unités de stockage qui contiennent les données multimédias désirées. L'originalité de ce résumé réside en le fait qu'il soit construit automatiquement sur la base des métadonnées extraites durant l'indexation. Dans cet article, nous montrons comment construire un tel résumé et illustrons notre approche au moyen de technologies issues du Web Sémantique, telles que RDF et SPARQL pour représenter et interroger des métadonnées sémantiquement définies

Combining flexible queries and knowledge anchors to facilitate the exploration of knowledge graphs

Semantic web and information extraction technologies are enabling the creation of vast information and knowledge repositories, particularly in the form of knowledge graphs comprising entities and the relationships between them. Users are often unfamiliar with the complex structure and vast content of such graphs. Hence, users need to be assisted by tools that support interactive exploration and flexible querying. In this paper we draw on recent work in flexible querying for graph-structured data and identifying good anchors for knowledge graph exploration in order to demonstrate how users can be supported in incrementally querying, exploring and learning from large complex knowledge graphs. We demonstrate our techniques through a case study in the domain of lifelong learning and career guidance

Making Study Populations Visible through Knowledge Graphs

Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommendations, they need to understand how well their patient population matches the characteristics of those in the study cohort, and thus are confronted with the challenges of locating the study cohort information and making an analytic comparison. To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations. We build a Study Cohort Ontology (SCO) to encode the vocabulary of study population descriptions, that are often reported in the first table in the published work, thus they are often referred to as Table 1. We leverage the well-used Semanticscience Integrated Ontology (SIO) for defining property associations between classes. Further, we model the key components of Table 1s, i.e., collections of study subjects, subject characteristics, and statistical measures in RDF knowledge graphs. We design scenarios for medical practitioners to perform population analysis, and generate cohort similarity visualizations to determine the applicability of a study population to the clinical population of interest. Our semantic approach to make study populations visible, by standardized representations of Table 1s, allows users to quickly derive clinically relevant inferences about study populations.Comment: 16 pages, 4 figures, 1 table, accepted to the ISWC 2019 Resources Track (https://iswc2019.semanticweb.org/call-for-resources-track-papers/

Machine Learning-based Query Augmentation for SPARQL Endpoints

Linked Data repositories have become a popular source of publicly-available data. Users accessing this data through SPARQL endpoints usually launch several restrictive yet similar consecutive queries, either to find the information they need through trial-and-error or to query related resources. However, instead of executing each individual query separately, query augmentation aims at modifying the incoming queries to retrieve more data that is potentially relevant to subsequent requests. In this paper, we propose a novel approach to query augmentation for SPARQL endpoints based on machine learning. Our approach separates the structure of the query from its contents and measures two types of similarity, which are then used to predict the structure and contents of the augmented query. We test the approach on the real-world query logs of the Spanish and English DBpedia and show that our approach yields high-accuracy prediction. We also show that, by caching the results of the predicted (More)This work has been supported by the European Union's Horizon 2020 research and innovation program (grant H2020-MSCA-ITN-2014-642963), the Spanish Ministry of Science and Innovation (contract TIN2015-65316, project RTC-2016-4952-7 and contract TIN2016-78011-C4-4-R), the Spanish Ministry of Education, Culture and Sports (contract CAS18/00333) and the Generalitat de Catalunya (contract 2014-SGR-1051). The authors would also like to thank Toni Cortes for his feedback.Peer ReviewedPostprint (author's final draft

RDF Modelling and SPARQL Processing of SQL Abstract Syntax Trees

International audienceMost enterprise systems rely on relational databases, and therefore SQL queries, to populate dynamic documents such as business intelligence reports, dashboards or spreadsheets. These queries represent metadata about the documents, thus they can feed information retrieval systems such as recommender systems or search engines. In this paper we propose to automatically annotate documents with structured representations of their SQL queries expressed with RDF graphs. We show that SPARQL is a natural language to query these SQL queries, i.e. to perform meta-querying, and discuss challenges that arise from this approach

Aide à la création d'objets dans une base RDF(S) avec des règles de relaxation

National audienceQuand un utilisateur crée un nouvel objet dans le Web sémantique, les outils existants n'exploitent ni les objets existants et leurs propriétés, ni les propriétés déjà connues du nouvel objet. Nous proposons UTILIS, une méthode d'aide à la création de nouveaux objets. UTILIS cherche des objets similaires au nouvel objet en appliquant des règles de relaxation à sa description. Les propriétés des objets similaires servent de suggestions pour compléter la description du nouvel objet. Une étude utilisateur menée avec des étudiants en master montre que les suggestions d'UTILIS ont été utilisées. Les utilisateurs ont trouvé les suggestions pertinentes : dans la plupart des cas, ils pouvaient trouver l'élément recherché dans les trois premiers ensembles de suggestions. De plus, ils les ont appréciées, car la majorité souhaitent les avoir dans un éditeur de données du Web sémantique