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    Publicado el 29.10.2015 por Ricardo Alonso Maturana

    Choosing Between Graph Databases and RDF Engines for Consuming and Mining Linked Data (Universidad Simon Bolívar, Caracas, Venezuela)

    Abstract.

    Graphs naturally represent Linked Data and implementations of graph-based tasks are required not only for data consumption, but also for mining patterns among links. Despite efficient graph-based algorithms and engines have been implemented, there is no clear understanding of how these solutions may behave on Linked Data

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    Publicado el 29.10.2015 por Ricardo Alonso Maturana

    The Graph Database and the RDF Database

    In a twist that has inevitable written all over it, the database industry has at last begun to take heed of the power of consumerization. The once mighty RDBMS is now obliged to make room for an emerging and increasingly important partner in the data center: the graph database. Twitter’s doing it, Facebook’s doing it, even online dating sites are doing it; what they are doing is tracing relationship graphs. After all, social is social, and ultimately it’s all about relationships.

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    Publicado el 29.10.2015 por Ricardo Alonso Maturana

    What is the difference between triplestores and graph databases? - Stack Overflow

    There are triplestores (semantic databases), and there are general-purpose graph databases.

    Both are based on the similar concepts of linking one "item" to another via a relationship. Triplestores support RDF and are queried by SPARQL, but such add-ons can be (and are) implemented ontop of general-purpose graph databases as well.

    What is the fundamental difference that would make you prefer a semantic db / triplestore to a general purpose graph database like neo4j?

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    Publicado el 29.10.2015 por Ricardo Alonso Maturana

    What are the differences between a Graph database and a Triple store? (by Matt Allen in Quora)

    Graph Databases vs. RDF Triple Stores
    To summarize, both graph databases and triple stores are designed to store linked data. RDF is a specific kind of linked data that is queried using SPARQL, so it is fair to say that RDF triple stores are a kind of graph database. But, there are some subtle but important differences that are described below.
    How They Are Similar
    ·       Graph databases and rdf triple stores focus on the relationships between the data, often referred to as “linked data.” Data points are called nodes, and the relationship between one data point and another is called an edge.
    ·       A web of nodes and edges can be put together into interesting visualizations—a defining characteristic of graph databases.
    How They Are Different
    ·       Graph databases are more versatile with query languages:  Neo4J can run an RDF triple store and use SPARQL but generally focuses on its own proprietary language, Cypher. Other graph databases support G, GraphLog, GOOD, SoSQL, BiQL, SNQL, and more. RDF triple stores only use SPARQL as the query language.
    ·       Graph databases can store various types of graphs, including undirected graphs, weighted graphs, hypergraphs, etc. RDF triple stores focus solely on storing rows of RDF triples.
    ·       Graph databases are node, or property, centric whereas RDF triple stores are edge-centric. RDF triple stores are really just a list of graph edges, many of which are 'properties'  of a node and not critical to the graph structure itself.
    ·       Graph databases are better optimized for graph traversals (degrees of separation or shortest path algorithms). With RDF triple stores, the cost of traversing an edge tends to be logarithmic.
    ·       RDF triple stores also provide inferences on data but graph databases do not (e.g., if humans are a subclass of mammals and man is a subclass of humans, then it can be inferred that man is a subclass of mammals).
    ·       RDF triple stores are more synonymous with the “semantic web” and the standardized universe of knowledge being stored as RDF triples on DBpedia and other sources whereas graph databases are seen as more pragmatic rather than academic.

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    Publicado el 4.3.2013 por Equipo GNOSS

    SPARQL2NL

    tipo de documento Página Web

    SPARQL2NL

    SPARQL2NL es un framework que permite convertir queries SPARQL y tripletas RDF en lenguaje natural.

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    Publicado el 12.11.2012 por Equipo GNOSS

    SPARQL 1.1: Proposed Recommendation

    El grupo W3C SPARQL Working Group ha publicado SPARQL 1.1 como Proposed Recommendation. Tras el exito de SPARQL 1.0, SPARQL 1.1 es un estándar complete para trabajar con datos RDF que incluye lenguaje de consulta y actualización, dos protocolos HTTP, resultados en tres formatos, y otras características explotandos los puntos SPARQL. La mayoría de las características de SPARQL 1.1 han sido ya implementadas por varios proveedores como muestra la siguiente tabla.

    The Proposed Recommendations are:

    1. SPARQL 1.1 Overview - Overview of SPARQL 1.1 and the SPARQL 1.1 documents
    2. SPARQL 1.1 Query Language - A query language for RDF data.
    3. SPARQL 1.1 Update - Specifies additions to the query language to allow clients to update stored data
    4. SPARQL 1.1 Query Results JSON Format - How to use JSON for SPARQL query results
    5. SPARQL 1.1 Query Results CSV and TSV Formats - How to use comma-separated values (CVS) and tab-separated values (TSV) for SPARQL query results
    6. SPARQL Query Results XML Format - How to use XML for SPARQL query results. (This contains only minor, editorial updates from SPARQL 1.0, and is actually a Proposed Edited Recommendation.)
    7. SPARQL 1.1 Federated Query - an extension of the SPARQL 1.1 Query Language for executing queries distributed over different SPARQL endpoints.
    8. SPARQL 1.1 Service Description - a method for discovering and a vocabulary for describing SPARQL services.

    The following are Candidate Recommendations, as the group still seeks more feedback from implementors:

    1. SPARQL 1.1 Entailment Regimes - defines the semantics of SPARQL queries under entailment regimes such as RDF Schema, OWL, or RIF.
    2. SPARQL 1.1 Protocol for RDF - A protocol defining means for conveying arbitrary SPARQL queries and update requests to a SPARQL service.
    3. SPARQL 1.1 Graph Store HTTP Protocol - As opposed to the full SPARQL protocol, this specification defines minimal means for managing RDF graph content directly via common HTTP operations.

    The group has also produced a test suite and a page on using SPARQL 1.1 with RDF 1.1.

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    Publicado el 7.11.2012 por Equipo GNOSS

    SPARQL and Big Data (and NoSQL)

    En este post Bob DuCh­arme, arquitecto de soluciones en TopQuad­rant nos hablar de la situación actual de los datos en la Web. En él nos habla de distintos niveles de estructuración y de términos como "minimally structured".

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    Publicado el 6.8.2012 por Equipo GNOSS

    GeoSPARQL - A Geographic Query Language for RDF Data

    Este estándar define un conjunto de funciones de extensión SPARQL [SPARQL W3C], un conjunto de reglas RIF [W3C RIF Core], y un vocabulario  RDF/OWL para la descripción de información geográfica basado en el modelo ISO 19125-1 .

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    Publicado el 15.5.2012 por Equipo GNOSS

    Playing with SPARQL Graph Store HTTP Protocol

    En este post, Bob DuCharme, Solutions Architect en TopQuadrant  explica algunas conclusiones obtenidas tras usar implementaciones basadas en el protocolo SPARQL 1.1 Graph Store HTTP Protocol. Bob explica que añadiendo o borrando tripletas a nivel de nombre del grafo (al contrario que a nivel de tripleta) tiene más sentido en la publicación de workflows donde los conjuntos de datos pueden borrarse o insertarse como una unidad.

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