A SPARQL 1.1 Query Builder for the Data Analytics of Vanilla RDF Graphs

As more and more data are available as RDF graphs, the availability of tools for data analytics beyond semantic search becomes a key issue of the Semantic Web. Previous work has focused on adapting OLAP-like approaches and question answering by modelling RDF data cubes on top of RDF graphs. We propose a more direct – and more expressive – approach by guiding users in the incremental building of SPARQL 1.1 queries that combine several computation features (aggregations, expressions, bindings and filters), and by evaluating those queries on unmodified (vanilla) RDF graphs. We rely on the N<A>F design pattern to hide SPARQL behind a natural language interface, and to provide results and suggestions at every step. We have implemented our approach on top of Sparklis, and we report on three experiments to assess its expressivity, usability, and scalability

Analytical Queries on Vanilla RDF Graphs with a Guided Query Builder Approach

International audienceAs more and more data are available as RDF graphs, the availability of tools for data analytics beyond semantic search becomes a key issue of the Semantic Web. Previous work require the modelling of data cubes on top of RDF graphs. We propose an approach that directly answers analytical queries on unmodified (vanilla) RDF graphs by exploiting the computation features of SPARQL 1.1. We rely on the NF design pattern to design a query builder that completely hides SPARQL behind a verbalization in natural language; and that gives intermediate results and suggestions at each step. Our evaluations show that our approach covers a large range of use cases, scales well on large datasets, and is easier to use than writing SPARQL queries

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Conceptual Navigation in Large Knowledge Graphs

International audienceA growing part of Big Data is made of knowledge graphs. Major knowledge graphs such as Wikidata, DBpedia or the Google Knowledge Graph count millions of entities and billions of semantic links. A major challenge is to enable their exploration and querying by end-users. The SPARQL query language is powerful but provides no support for exploration by endusers. Question answering is user-friendly but is limited in expressivity and reliability. Navigation in concept lattices supports exploration but is limited in expressivity and scalability. In this paper, we introduce a new exploration and querying paradigm, Abstract Conceptual Navigation (ACN), that merges querying and navigation in order to reconcile expressivity, usability, and scalability. ACN is founded on Formal Concept Analysis (FCA) by defining the navigation space as a concept lattice. We then instantiate the ACN paradigm to knowledge graphs (Graph-ACN) by relying on Graph-FCA, an extension of FCA to knowledge graphs. We continue by detailing how Graph-ACN can be efficiently implemented on top of SPARQL endpoints, and how its expressivity can be increased in a modular way. Finally, we present a concrete implementation available online, Sparklis, and a few application cases on large knowledge graphs

Enabling Complex Semantic Queries to Bioinformatics Databases through Intuitive Search Over Data

Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data already available publicly. However, the heterogene- ity of the existing data sources still poses significant challenges for achieving interoperability among biological databases. Furthermore, merely solving the technical challenges of data in- tegration, for example through the use of common data representation formats, leaves open the larger problem. Namely, the steep learning curve required for understanding the data models of each public source, as well as the technical language through which the sources can be queried and joined. As a consequence, most of the available biological data remain practically unexplored today. In this thesis, we address these problems jointly, by first introducing an ontology-based data integration solution in order to mitigate the data source heterogeneity problem. We illustrate through the concrete example of Bgee, a gene expression data source, how relational databases can be exposed as virtual Resource Description Framework (RDF) graphs, through relational-to-RDF mappings. This has the important advantage that the original data source can remain unmodified, while still becoming interoperable with external RDF sources. We complement our methods with applied case studies designed to guide domain experts in formulating expressive federated queries targeting the integrated data across the domains of evolutionary relationships and gene expression. More precisely, we introduce two com- parative analyses, first within the same domain (using orthology data from multiple, inter- operable, data sources) and second across domains, in order to study the relation between expression change and evolution rate following a duplication event. Finally, in order to bridge the semantic gap between users and data, we design and im- plement Bio-SODA, a question answering system over domain knowledge graphs, that does not require training data for translating user questions to SPARQL. Bio-SODA uses a novel ranking approach that combines syntactic and semantic similarity, while also incorporating node centrality metrics to rank candidate matches for a given user question. Our results in testing Bio-SODA across several real-world databases that span multiple domains (both within and outside bioinformatics) show that it can answer complex, multi-fact queries, be- yond the current state-of-the-art in the more well-studied open-domain question answering. -- L’intégration des données promet d’être l’un des principaux catalyseurs permettant d’extraire des nouveaux aperçus de la richesse des données biologiques déjà disponibles publiquement. Cependant, l’hétérogénéité des sources de données existantes pose encore des défis importants pour parvenir à l’interopérabilité des bases de données biologiques. De plus, en surmontant seulement les défis techniques de l’intégration des données, par exemple grâce à l’utilisation de formats standard de représentation de données, on laisse ouvert un problème encore plus grand. À savoir, la courbe d’apprentissage abrupte nécessaire pour comprendre la modéli- sation des données choisie par chaque source publique, ainsi que le langage technique par lequel les sources peuvent être interrogés et jointes. Par conséquent, la plupart des données biologiques publiquement disponibles restent pratiquement inexplorés aujourd’hui. Dans cette thèse, nous abordons l’ensemble des deux problèmes, en introduisant d’abord une solution d’intégration de données basée sur ontologies, afin d’atténuer le problème d’hété- rogénéité des sources de données. Nous montrons, à travers l’exemple de Bgee, une base de données d’expression de gènes, une approche permettant les bases de données relationnelles d’être publiés sous forme de graphes RDF (Resource Description Framework) virtuels, via des correspondances relationnel-vers-RDF (« relational-to-RDF mappings »). Cela présente l’important avantage que la source de données d’origine peut rester inchangé, tout en de- venant interopérable avec les sources RDF externes. Nous complétons nos méthodes avec des études de cas appliquées, conçues pour guider les experts du domaine dans la formulation de requêtes fédérées expressives, ciblant les don- nées intégrées dans les domaines des relations évolutionnaires et de l’expression des gènes. Plus précisément, nous introduisons deux analyses comparatives, d’abord dans le même do- maine (en utilisant des données d’orthologie provenant de plusieurs sources de données in- teropérables) et ensuite à travers des domaines interconnectés, afin d’étudier la relation entre le changement d’expression et le taux d’évolution suite à une duplication de gène. Enfin, afin de mitiger le décalage sémantique entre les utilisateurs et les données, nous concevons et implémentons Bio-SODA, un système de réponse aux questions sur des graphes de connaissances domaine-spécifique, qui ne nécessite pas de données de formation pour traduire les questions des utilisateurs vers SPARQL. Bio-SODA utilise une nouvelle ap- proche de classement qui combine la similarité syntactique et sémantique, tout en incorporant des métriques de centralité des nœuds, pour classer les possibles candidats en réponse à une question utilisateur donnée. Nos résultats suite aux tests effectués en utilisant Bio-SODA sur plusieurs bases de données à travers plusieurs domaines (tantôt liés à la bioinformatique qu’extérieurs) montrent que Bio-SODA réussit à répondre à des questions complexes, en- gendrant multiples entités, au-delà de l’état actuel de la technique en matière de systèmes de réponses aux questions sur les données structures, en particulier graphes de connaissances

Learning SPARQL Queries from Expected Results

We present LSQ, an algorithm learning SPARQL queries from a subset of expected results. The algorithm leverages grouping, aggregates and inline values of SPARQL 1.1 in order to move most of the complex computations to a SPARQL endpoint. It operates by building and testing hypotheses expressed as SPARQL queries and uses active learning to collect a small number of learning examples from the user. We provide an open-source implementation and an on-line interface to test the algorithm. In the experimental evaluation, we use real queries posed in the past to the official DBpedia SPARQL endpoint, and we show that the algorithm is able to learn them, 82 % of them in less than a minute and asking the user just once