Reconciliation of RDF* and Property Graphs

Both the notion of Property Graphs (PG) and the Resource Description Framework (RDF) are commonly used models for representing graph-shaped data. While there exist some system-specific solutions to convert data from one model to the other, these solutions are not entirely compatible with one another and none of them appears to be based on a formal foundation. In fact, for the PG model, there does not even exist a commonly agreed-upon formal definition. The aim of this document is to reconcile both models formally. To this end, the document proposes a formalization of the PG model and introduces well-defined transformations between PGs and RDF. As a result, the document provides a basis for the following two innovations: On one hand, by implementing the RDF-to-PG transformations defined in this document, PG-based systems can enable their users to load RDF data and make it accessible in a compatible, system-independent manner using, e.g., the graph traversal language Gremlin or the declarative graph query language Cypher. On the other hand, the PG-to-RDF transformation in this document enables RDF data management systems to support compatible, system-independent queries over the content of Property Graphs by using the standard RDF query language SPARQL. Additionally, this document represents a foundation for systematic research on relationships between the two models and between their query languages.Comment: slightly changed the definition of PGs and added the notion of property uniquenes

Algebraic Property Graphs

In this paper, we use algebraic data types to define a formal basis for the property graph data models supported by popular open source and commercial graph databases. Developed as a kind of inter-lingua for enterprise data integration, algebraic property graphs encode the binary edges and key-value pairs typical of property graphs, and also provide a well-defined notion of schema and support straightforward mappings to and from non-graph datasets, including relational, streaming, and microservice data commonly encountered in enterprise environments. We propose algebraic property graphs as a simple but mathematically rigorous bridge between graph and non-graph data models, broadening the scope of graph computing by removing obstacles to the construction of virtual graphs

Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection

Bridging graph data models: RDF, RDF-star, and property graphs as directed acyclic graphs

Graph database users today face a choice between two technology stacks: the Resource Description Framework (RDF), on one side, is a data model with built-in semantics that was originally developed by the W3C to exchange interconnected data on the Web; on the other side, Labeled Property Graphs (LPGs) are geared towards efficient graph processing and have strong roots in developer and engineering communities. The two models look at graphs from different abstraction layers (triples in RDF vs. edges connecting vertices with inlined properties in LPGs), expose - at least at the surface - distinct features, come with different query languages, and are embedded into their own software ecosystems. In this short paper, we introduce a novel unifying graph data model called Statement Graphs, which combines the traits of both RDF and LPG and achieves interoperability at different levels: it (a) provides the ability to manage RDF and LPG data as a single, interconnected graph, (b) supports querying over the integrated graph using any RDF or LPG query language, while (c) clearing the way for graph stack independent data exchange mechanisms and formats. We formalize our new model as directed acyclic graphs and sketch a system of bidirectional mappings between RDF, LPGs, and Statement Graphs. Our mappings implicitly define read query semantics for RDF and LPGs query languages over the unified data model, thus providing graph users with the flexibility to use the query language of their choice for their graph use cases. As a proof of concept for our ideas, we also present the 1G Playground; an in-memory DBMS built on the concepts of Statement Graphs, which facilitates storage of both RDF and LPG data, and allows for cross-model querying using both SPARQL and Gremlin

Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study

Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining approach that relies on a set of constraints (e.g., support or degree thresholds) and the monotonicity property. As our motivation comes from the mining of real-world knowledge graphs, we illustrate our approach with PGxLOD, a biomedical knowledge graph

Mejorando la Ciencia Abierta Usando Datos Abiertos Enlazados: Caso de Uso CONICET Digital

Los servicios de publicación científica están cambiando drásticamente, los investigadores demandan servicios de búsqueda inteligentes para descubrir y relacionar publicaciones científicas. Los editores deben incorporar información semántica para organizar mejor sus activos digitales y hacer que las publicaciones sean más visibles. En este documento, presentamos el trabajo en curso para publicar un subconjunto de publicaciones científicas de CONICET Digital como datos abiertos enlazados. El objetivo de este trabajo es mejorar la recuperación y la reutilización de datos a través de tecnologías de Web Semántica y Datos Enlazados en el dominio de las publicaciones científicas. Para lograr estos objetivos, se han tenido en cuenta los estándares de la Web Semántica y los esquemas RDF (Dublín Core, FOAF, VoID, etc.). El proceso de conversión y publicación se basa en las pautas metodológicas para publicar datos vinculados de gobierno. También describimos como estos datos se pueden vincular a otros conjuntos de datos como DBLP, Wikidata y DBPedia. Finalmente, mostramos algunos ejemplos de consultas que responden a preguntas que inicialmente no permite CONICET Digital.Scientific publication services are changing drastically, researchers demand intelligent search services to discover and relate scientific publications. Publishersneed to incorporate semantic information to better organize their digital assets and make publications more discoverable. In this paper, we present the on-going work to publish a subset of scientific publications of CONICET Digital as Linked Open Data. The objective of this work is to improve the recovery andreuse of data through Semantic Web technologies and Linked Data in the domain of scientific publications.To achieve these goals, Semantic Web standards and reference RDF schema?s have been taken into account (Dublin Core, FOAF, VoID, etc.). The conversion and publication process is guided by the methodological guidelines for publishing government linked data. We also outline how these data can be linked to other datasets DBLP, WIKIDATA and DBPEDIA on the web of data. Finally, we show some examples of queries that answer questions that initially CONICET Digital does not allowFil: Zárate, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Carlos Buckle. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Mazzanti, Renato. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Samec, Gustavo Daniel. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentin

Living the Transition. A Bottom-up Perspective on Rwanda’s Political Transition

Political transitions are dominantly analyzed top-down and focus on a narrow range of political processes and institutions. Critical rethinkings of the ‘transition paradigm’ entail that structural factors, such as historical legacies and ethnic make-up, determine the trajectory of political transitions. In this paper we intend to complement top-down approaches by offering a bottom-up perspective revealing what it means to live through a transition in the ordinary perception. We use the Rwandan transition as case-study. An analysis of over 400 life histories of ordinary Rwandan peasants and their subjective ranking exercises over time on a ‘ladder of life’ portrays the trajectory of the Rwandan transition as perceived from below. The ethnicity of the respondents functions as pivot to shed light on the structural factor underlying the Rwandan transition: the Hutu-Tutsi bi-polarity.

A Semantic Model for Enhancing Data-Driven Open Banking Services

In current Open Banking services, the European Payment Services Directive (PSD2) allows the secure collection of bank customer information, on their behalf and with their consent, to analyze their financial status and needs. The PSD2 directive has lead to a massive number of daily transactions between Fintech entities which require the automatic management of the data involved, generally coming from multiple and heterogeneous sources and formats. In this context, one of the main challenges lies in defining and implementing common data integration schemes to easily merge them into knowledge-base repositories, hence allowing data reconciliation and sophisticated analysis. In this sense, Semantic Web technologies constitute a suitable framework for the semantic integration of data that makes linking with external sources possible and enhances systematic querying. With this motivation, an ontology approach is proposed in this work to operate as a semantic data mediator in real-world open banking operations. According to semantic reconciliation mechanisms, the underpinning knowledge graph is populated with data involved in PSD2 open banking transactions, which are aligned with information from invoices. A series of semantic rules is defined in this work to show how the financial solvency classification of client entities and transaction concept suggestions can be inferred from the proposed semantic model.This research has been partially funded by the Spanish Ministry of Science and Innovation via the Aether Project with grant number PID2020-112540RB-C41 (AEI/FEDER, UE), the Ministry of Industry, Commerce and Tourism via the Helix initiative with grant number AEI-010500-2020-34, and the Andalusian PAIDI program with grant number P18-RT-2799. Partial funding for open access charge: Universidad de Málag