Extension du langage de requêtes LISQL pour la représentation et l'exploration d'expressions mathématiques en RDF

National audienceLes expressions mathématiques comptent pour une part importante dans les connaissances humaines. Nous en proposons une représentation en RDF afin de pouvoir les intégrer aux autres connaissances dans le Web sémantique. Nous étendons ensuite le langage de description et d'interrogation LISQL afin de concilier des représentations non-ambiguës, des requêtes expressives et des notations naturelles et concises. Par exemple, la requête \textttint(...?X $\hat~$ 2...,?X) permet de trouver les intégrales en~$x$ dont le corps contient la sous-expression~$x^2$. Tout cela permet d'utiliser Sewelis, un système d'information logique pour le Web sémantique, pour la représentation et l'exploration guidée d'expressions mathématiques. Ce guidage dispense les utilisateurs de maîtriser la syntaxe de LISQL et le vocabulaire tout en leur garantissant des expressions bien formées et des résultats à leurs requêtes

Handling Spatial Relations in Logical Concept Analysis to Explore Geographical Data ⋆

Abstract. Because of the expansion of geo-positioning tools and the democratization of geographical information, the amount of geo-localized data that is available around the world keeps increasing. So, the ability to efficiently retrieve informations in function of their geographical facet is an important issue. In addition to individual properties such as position and shape, spatial relations between objects are an important criteria for selecting and reaching objects of interest: e.g., given a set of touristic points, selecting those having a nearby hotel or reaching the nearby hotels. In this paper, we propose Logical Concept Analysis (LCA) and its handling of relations for representing and reasoning on various kinds of spatial relations: e.g., Euclidean distance, topological relations. Furthermore, we present an original way of navigating in geolocalized data, and compare the benefits of our approach with traditional Geographical Information Systems (GIS).

Semantic Faceted Search: Safe and Expressive Navigation in RDF Graphs

Faceted search and querying are the two main paradigms to search the Semantic Web. Querying languages, such as SPARQL, oer expressive means for searching knowledge bases, but they are dicult to use. Query assistants help users to write well-formed queries, but they do not prevent empty results. Faceted search supports exploratory search, i.e., guided navigation that returns rich feedbacks to users, and prevents them to make navigation steps that lead to empty results (dead-ends). However, faceted search systems do not oer the same expressiveness as query languages. We introduce semantic faceted search, the combination of an expressive query language and faceted search to reconcile the two paradigms. The query language is basically SPARQL, but with a syntax that extends Turtle with disjunction and negation, and that better ts in a faceted search interface: LISQL. We formalize the navigation of faceted search as a navigation graph, where nodes are queries, and navigation links are query transformations. We prove that this navigation graph is safe (no dead-end), and complete (every query that is not a dead-end can be reached by navigation). That formalization itself is a contribution to faceted search. A prototype, Camelis 2, has been implemented, and a usability evaluation with graduate students demonstrated that semantic faceted search retains the ease-of-use of faceted search, and enables most users to build complex queries with little training

KG-MDL: Mining Graph Patterns in Knowledge Graphs with the MDL Principle

Nowadays, increasingly more data are available as knowledge graphs (KGs). While this data model supports advanced reasoning and querying, they remain difficult to mine due to their size and complexity. Graph mining approaches can be used to extract patterns from KGs. However this presents two main issues. First, graph mining approaches tend to extract too many patterns for a human analyst to interpret (pattern explosion). Second, real-life KGs tend to differ from the graphs usually treated in graph mining: they are multigraphs, their vertex degrees tend to follow a power-law, and the way in which they model knowledge can produce spurious patterns. Recently, a graph mining approach named GraphMDL+ has been proposed to tackle the problem of pattern explosion, using the Minimum Description Length (MDL) principle. However, GraphMDL+, like other graph mining approaches, is not suited for KGs without adaptations. In this paper we propose KG-MDL, a graph pattern mining approach based on the MDL principle that, given a KG, generates a human-sized and descriptive set of graph patterns, and so in a parameter-less and anytime way. We report on experiments on medium-sized KGs showing that our approach generates sets of patterns that are both small enough to be interpreted by humans and descriptive of the KG. We show that the extracted patterns highlight relevant characteristics of the data: both of the schema used to create the data, and of the concrete facts it contains. We also discuss the issues related to mining graph patterns on knowledge graphs, as opposed to other types of graph data

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

SQUALL: a Controlled Natural Language for Querying and Updating RDF Graphs

International audienceFormal languages play a central role in the Semantic Web. An important aspect regarding their design is syntax as it plays a crucial role in the wide acceptance of the Semantic Web approach. The main advantage of controlled natural languages (CNL) is to reconcile the high-level and natural syntax of natural languages, and the precision and lack of ambiguity of formal languages. In the context of the Semantic Web and Linked Open Data, CNL could not only allow more people to contribute by abstracting from the low-level details, but also make experienced people more productive, and make the produced documents easier to share and maintain. We introduce SQUALL, a controlled natural language for querying and updating RDF graphs. It has a strong adequacy with RDF, an expressiveness close to SPARQL 1.1, and a CNL syntax that completely abstracts from low-level notions such as bindings and relational algebra. We formally define the syntax and semantics of SQUALL as a Montague grammar, and its translation to SPARQL. It features disjunction, negation, quantifiers, built-in predicates, aggregations with grouping, and n-ary relations through reification

Exploring the Application of Graph-FCA to the Problem of Knowledge Graph Alignment

International audienceKnowledge Graphs (KG) have become a widespread knowledge representation. When different KGs exist for some domain, it is valuable to merge them into a richer KG. This is known as the problem of KG alignement, which encompasses related problems such as entity alignement or ontology matching. Although most recent approaches rely on supervised representation learning, Formal Concept Analysis (FCA) has also been proposed as a basis for symbolic and unsupervised approaches. We here explore the application of Graph-FCA, an extension of FCA for KGs, to different scenarios of KG alignments: (A) when the two KGs have common values, and (B) when pre-aligned pairs are known. We show that, compared to previous FCA-based approaches, Graph-FCA allows for a more natural and scalable representation of the KGs to be aligned, and makes it simpler to extract alignments from the concepts. It also features flexibility w.r.t. different alignment scenarios

Adding Structure and Removing Duplicates in SPARQL Results with Nested Tables

International audienceThe results of a SPARQL query are generally presented as a table with one row per result, and one column per projected variable. This is an immediate consequence of the formal definition of SPARQL results as a sequence of mappings from variables to RDF terms. However, because of the flat structure of tables, some of the RDF graph structure is lost. This often leads to duplicates in the contents of the table, and difficulties to read and interpret results. We propose to use nested tables to improve the presentation of SPARQL results. A nested table is a table where cells may contain embedded tables instead of RDF terms, and so recursively. We introduce an automated procedure that lifts flat tables into nested tables, based on an analysis of the query. We have implemented the procedure on top of Sparklis, a guided query builder in natural language, in order to further improve the readability of its UI. It can as well be implemented on any SPARQL querying interface as it only depends on the query and its flat results. We illustrate our proposal in the domain of pharmacovigilance, and evaluate it on complex queries over Wikidata

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

Concepts de plus proches voisins dans des graphes de connaissances.

National audienceNous introduisons la notion de concept de voisins comme alternative à la notion de distance numérique dans le but d'identifier les objets les plus similaires à un objet requête, comme dans la méthode des plus proches voisins. Chaque concept de voisins est composé d'une intension qui décrit symboliquement ce que deux objets ont en commun et d'une extension qui englobe les objets qui se trouvent entre les deux. Nous définissons ces concepts de voisins pour des données complexes, les graphes de connaissances, où les n\oe{}uds jouent le rôle d'objets. Nous décrivons un algorithme anytime permettant d'affronter la complexité élevée de la tâche et nous présentons de premières expérimentations sur un graphe RDF de plus de 120.000 triplets