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    Publicado el 7.8.2018 por Ricardo Alonso Maturana

    Why Knowledge Graphs Are Foundational to Artificial Intelligence (by Jim Webber)

    AI is poised to drive the next wave of technological disruption across industries. Like previous technology revolutions in Web and mobile, however, there will be huge dividends for those organizations who can harness this technology for competitive advantage.

    I spend a lot of time working with customers, many of whom are investing significant time and effort  in building AI applications for this very reason. From the outside, these applications couldn’t be more diverse – fraud detection, retail recommendation engines, knowledge sharing – but I see a sweeping opportunity across the board: context.

    Without context (who the user is, what they are searching for, what similar users have searched for in the past, and how all these connections play together) these AI applications may never reach their full potential. Context is data, and as a data geek, that is profoundly exciting.

    We’re now looking at things, not strings

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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 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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    Presentación

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    Publicado el 21.7.2013 por Pablo Hermoso de Mendoza González

    NoSQL databases

    tipo de documento Presentación

    En esta presentación Marin Dimitrov (Ontotext) realiza un repaso de las principales categorías asociadas a Modelos de Base de Datos NOSQL: Key Value Store, Column Store, Document Store y Graph Database.  Mar 2010

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    Publicado el 28.7.2010 por Equipo GNOSS

    BLOG de Kingsley Idehen's, CEO y Fundador de Openlink Software, fabricantes de Virtuoso

    Kingsley Idehen es el Fundador, Presidente y CEO de OpenLink Software, y probablemente uno de los más profundos expertos en problemas de integración de datos en lo que se conoce como "Linked Data".

    Uno de los productos más relevantes de OpenLink es Virtuoso, un servidor virtual de base de datos que incluye un potente sistema de almacenamiento y recuperación de grafos RDF.

    Además de en su blog, se le puede seguir en Twitter.

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    Publicado el 21.5.2010 por Equipo GNOSS

    HyperGraphDB - A Graph Database

    HyperGraphDB es una mecanismo de almacenamiento de propósito general, extensible, portable, distribuido, encapsulable y open- source. Es una base de datos de grafos diseñada específicamente para inteligencia artificial y proyectos de la web semántica, aunque también puede usarse como una base de datos encapsulada orientada a objetos, para proyectos de cualquier tamaño.

     

    El sistema se encuentra en producción en varios proyectos, incluyendo motores de búsqueda.

    HyperGraphDB es, fundamentalmente, lo que su nombre implica: una base de datos para almacenar hiper-grafos. Aunque se incluye en la familia de las bases de datos de grafos, no es simplemente otro sistema, sino que desde su diseño se proporcionan los medios para gestionar información rica y estructurada con capas arbitrarias de complejidad. Se podría llegar a emular el comportamiento de una BD relacional, u orientada a objetos.

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    Publicado el 20.12.2009 por Equipo GNOSS

    Franz Inc. - Semantic Web Technologies

    Franz Inc. es uno de los líderes en el sector de las bases de datos para almacenamiento de RDF y grafos RDF. Según su web: "AllegroGraph RDFStore provides the solid storage layer for powerful reasoning and ontology modeling capabilities. Franz's semantic technology solutions are uniquely positioned to help bring your Web 3.0 ideas to reality."

    Sus productos para la Web Semántica son:

    AllegroGraph RDFStore is a modern, high-performance, persistent RDF graph database. AllegroGraph uses disk-based storage, enabling it to scale to billions of triples while maintaining superior performance. AllegroGraph supports SPARQL, RDFS++, and Prolog reasoning from Java applications.

    AGWebView, Web Browser Server for AllegroGraph Triple Stores, gives you access to your triple store data via an ordinary Web browser.

    Gruff, a Grapher-Based Triple-Store Browser for AllegroGraph. Gruff is a triple-store browser that displays visual graphs of subsets of a store's resources and their links. By selecting particular resources and predicates, you can build a visual graph that displays a variety of the relationships in a triple-store. Gruff can also display tables of all properties of selected resources or generate tables with SPARQL queries, and resources in the tables can be added to the visual graph.

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