Large knowledge graphs such as DBpedia, Wikidata, and YAGO are rich sources of structured information, widely used in domains like information retrieval and recommendation. However, their potential for supporting knowledge workers remains underexploited. As in other complex networks where specialized metrics have emerged (e.g., bibliometrics), knowledge graphs offer promising opportunities for domain-specific analysis – particularly in areas lacking established quantitative tools. In this paper, we present Rankingdom , a web application for knowledge workers to analyze any entity from Wikidata despite its heterogeneity and sheer volume. For this purpose, we introduce complementary indicators to position or evaluate any Wikidata entity within its domain, addressing analysis tasks that are challenging for traditional methods. We propose an on-demand analysis architecture that distributes computation to clients while centralizing results for frugality, by benefiting from a client-side SPARQL parallelization engine ( SParaQL ). We demonstrate the effectiveness of SParaQL through performance tests on DBpedia, Wikidata, and YAGO with two analytical queries, as well as via a real-world deployment including caching of over 10,000 entities and a user study.

Ranking indicators are essential tools for comparing the importance of various entities such as cities or scientists. While extensively used in fields like econometrics and scientometrics, many other domains lack systematic approaches for developing these indicators. In this paper, we introduce a novel method for automatically discovering ranking indicators from very large knowledge graphs. To this end, we formalize the notion of counting graph pattern (CG) as a special SPARQL query, and the concept of ideal ranking indicator as a CG whose result induces a strict total order on a set of entities. To assess the interestingness of ranking indicators, we employ the proportion of covered entities along with an inequality measure, namely the Gini coefficient. We further present Algorithm Ranking Indicator Pattern Miner (RIPM) , to efficiently identify interesting ranking indicators for a given field, thanks to pruning techniques for handling the very large search space. Our experimental study shows the effectiveness of our optimizations. It also validates that RIPM extracts transparent, diverse, and understandable indicators through a user survey and a comparison with two baselines. This work has significant implications for fields lacking dedicated communities working on ranking tasks, providing a robust tool to automatically produce ranking indicators, and the associated rankings.

In this paper, we present a semantic based approach for integrating heterogeneous data sources, and we implement our approach on the scientific domain of Cultural Heritage Conservation-Restoration. First, we evaluate the context and the current needs; the observations confirmed the critical need of a data integration system taking advantage of semantics. Second, we describe the process that we followed in order to build an ontology dedicated to provide a unified understanding of the data. Third, we describe the ontology-based mediator system designed for effectively querying the heterogeneous distributed sources.

We present the whole querying process of our ontology-based data integration proposal, that we call Semantic Mediator. The global schema (a TBox) is composed of the source schemas (also Tboxes) and a taxonomy, which links the sources to each other. The querying process is based on the global-schema's structure and consists of three steps: global query rewriting, source querying and global answer building. We describe the overall distributed system and the query-rewriting algorithm. Then we present an application of such a semantic mediation, the Personae project, which is for enabling historians to share their prosopographic data from the Middle Ages and the Renaissance.

Data integration involves combining data residing in different sources and providing users with a unified view of these data through what is called a "global schema". We address here the problem of automatic construction of this global schema in the semantic Web context, where data sources are annotated with ontologies. We aim in other words to automatically build a common vocabulary (ontology) that will serve as a shared conceptual level for several heterogeneous data sources needing to share their data in a specific application domain. We propose a solution based on the use of a domain reference ontology (or "background knowledge") as a mediation support.

Updating XML documents submitted to schema constraints requires incremental validation, i.e. checking the parts of the document concerned by the updates. We propose to correct subtrees for which re-validation fails: if the validator fails at node p, a correction routine is called in order to compute corrections of the subtree rooted at p, within a given threshold. Then validation continues. In the correction process, we limit ourselves to single typed tree language. The correction routine uses tree edit distance matrices. Different correction versions are proposed to the user.