The World of Knowledge Graphs in Oracle Databases

Total Page:16

File Type:pdf, Size:1020Kb

The World of Knowledge Graphs in Oracle Databases The World of Knowledge Graphs in Oracle Databases Collaboration between Semantic Web Company and Oracle Emma Thomas Sebastian Gabler Principal Solutions Architect Director of Sales Oracle A-team Semantic Web Company November 19th 2019 2 Confidential – © 2019 Oracle Restricted Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation. 3 The World of Knowledge Graphs in Oracle Databases Introduction to Knowledge Graphs Oracle Database Spatial and Graph - RDF Focus Oracle RDF4J adapter Using PoolParty for Search, Similarity, Recommendation and Visualisation 4 Confidential – © 2019 Oracle Restricted Graphs are Everywhere Heiko Paulheim. Journal of Web Semantics: Special Issue on Knowledge Graph Refinement. “Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods.” [September, 2016] 5 Confidential – © 2019 Oracle Restricted An Opte Project Visualisation of routing paths through a portion of the Internet By The Opte Project - Originally from the English Wikipedia; description page is/was here., CC BY 2.5, https://commons.wikimedia.org/w/index.php?cu rid=1538544 6 What is a Knowledge Graph? Knowledge graphs are large networks of entities, their semantic types, properties, and relationships between entities1. Key Features: Knowledge “Things, not strings” Database Base Knowledge global unique identifiers Graph Formal structure/semantics machine processable, unambiguous Linked descriptions Graph resources are described by their connections 1. M. Kroetsch and G. Weikum. Journal of Web Semantics: Special Issue on Knowledge Graphs. http://www.websemanticsjournal.org/index.php/ps/an 7 Confidential – © 2019 Oracle Restricted nouncement/view/19 [August, 2016] Data Storage Name Desc Product in-a has-a Category SKU Image Relational Hierarchical Graph 8 The Semantic Web and Graph Databases “I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A "Semantic Web", which makes this possible, has yet to emerge, but when it does, the day- to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The "intelligent agents" people have touted for ages will finally materialize” Tim Berners-Lee, 1999 9 Confidential – © 2019 Oracle Restricted W3C Standards for Knowledge Graphs The W3C (World Wide Web Consortium) has defined a suite of standards to support Linked Data and Knowledge Graphs. Fundamental Concepts are: • Globally Unique IDs: URI • Links to other resources • Standard Data Model: RDF • Standard Ontology Language: OWL • Standard Query Language: SPARQL 10 Resource Description Framework (RDF) An RDF graph is a directed, labeled graph with some syntactic restrictions ● Edge labels are URIs ● Source vertex for an edge must be a URI ● Destination vertex is a URI or a scalar value An edge is the atomic unit of an RDF graph – called an RDF triple RDF Triple 11 Confidential – © 2019 Oracle Restricted Modeling Vocabularies With the Semantic Web, vocabularies define the concepts and relationships (also referred to as “terms”) used to describe and represent an area of concern: RDFS Resource Description Framework Schema allows you to express the relationships between things by standardizing on a flexible, triple-based format and then providing a vocabulary. OWL Web Ontology Language is an ontology language. The semantic web standard that is used to defined ontologies (metadata sets) so that they can be used and understood in that environment. Owls purpose is to develop ontologies that are compatible with the world wide web, where an ontology is definition and classification of concepts and entities and the relationship between them 12 Confidential – © 2019 Oracle Restricted Modeling Vocabularies SKOS stands for Simple Knowledge Organisation System. The name SKOS was chosen to emphasise the goal of providing a simple yet powerful framework for expressing knowledge organisation systems in a machine- understandable way. SKOS provides a standard way to represent knowledge organization systems using the Resource Description Framework (RDF). Encoding this information in RDF allows it to be passed between computer applications in an interoperable way. 13 Confidential – © 2019 Oracle Restricted SPARQL SELECT ?name WHERE { ?s a onto:Employee . ?s dcterms:title ?name } 14 Confidential – © 2019 Oracle Restricted Recap: Description of a Knowledge Graph Knowledge graphs provide holistic knowledge, including ▹ instance data (ground truth), either open, private, or closed ▹ schema data (vocabularies, ontologies) ▹ metadata (e.g. provenance, versioning, licensing) ▹ comprehensive taxonomies to categorize entities ▹ links between internal and external data ▹ mappings to data and documents stored in other systems and databases Source: Sören Auer, Fraunhofer IAI 15 Confidential – © 2019 Oracle Restricted The World of Knowledge Graphs in Oracle Databases Introduction to Knowledge Graphs Oracle Database Spatial and Graph - RDF Focus Oracle RDF4J adapter Using PoolParty for Search, Similarity, Recommendation and Visualisation 16 Confidential – © 2019 Oracle Restricted Database Version and Options for RDF Knowledge Graph ▪ On-premise ▪ Oracle Database Enterprise Edition with Spatial & Graph and Partitioning options ▪ Oracle Database Cloud Services ▪ DBCS (Bare Metal & VM) ▪ Exadata Cloud Service ▪ Exadata Cloud at Customer ▪ ATP-Dedicated (planned) 17 Copyright © 2019, Oracle and/or its affiliates. All rights reserved. Oracle Spatial and Graph 19c – RDF Knowledge Graph Features • Fast bulk-load and indexing Load / • RDF view of Relational data RDF Knowledge Graph Storage • Manages over a trillion quads Leverages Oracle capabilities: • SPARQL-Jena/Fuseki RAC & Exadata scalability Query • SPARQL-in-SQL query & update • Federated query Compression & partitioning • GeoSPARQL In-Memory Column Store SQL*Loader direct path load • RDFS, OWL2 RL, EL, SKOS • Ladder-based inference Parallel DDL, DML, and query Reasoning • Incremental, parallel reasoning RMAN Backup and Recovery • User-defined rules High Availability - Data Guard + DR • Plug-in architecture Oracle Label security Analytics • OBIEE Manageability - Enterprise Manager • Oracle Advanced Analytics Logical Standby • PGX 18 Copyright © 2019, Oracle and/or its affiliates. All rights reserved. Big Data Graph Benchmark 1 Trillion Triple RDF Benchmark with Oracle Spatial and Graph Oracle Database can load, query and inference millions of RDF graph edges per second World’s fastest data loading performance World’s fastest query performance Worlds fastest inference performance Massive scalability: 1.08 trillion edges Platform: Oracle Exadata X4-2 Database Machine Source: w3.org/wiki/LargeTripleStores 19 Confidential – © 2019 Oracle Restricted Oracle Spatial and Graph 19c – RDF Knowledge Graph Architecture SQL Developer Enterprise Manager Protégé Plugin Fuseki Endpoint Cytoscape Plugin RDF Support and Other DB Tools Support for Apache Jena (Java API) SQL and PL/SQL API RDF Bulk Loader Forward-chaining SPARQL-to-SQL SPARQL Update OWL Reasoner Query Translator Processor Generic Relational Schema for RDF Views of Relational Data Storing RDF Data 20 Confidential – © 2019 Oracle Restricted What makes a store RDF ready? It implements at least one of the RDF ready libraries/frameworks: • Eclipse RDF4J • Apache Jena It offers a SPARQL endpoint to query the database using SPARQL 21 Confidential – © 2019 Oracle Restricted Oracle Spatial and Graph - Apache Jena Oracle Database Release 18c offers support for: • Apache Jena 3.1 • Apache Jena Fuseki 2.4 • Protege 5.2 Available adapters: Source: Oracle Spatial and Graph downloads 22 Confidential – © 2019 Oracle Restricted The World of Knowledge Graphs in Oracle Databases Introduction to Knowledge Graphs Oracle Database Spatial and Graph - RDF Focus Oracle RDF4J adapter Using PoolParty for Search, Similarity, Recommendation and Visualisation 23 Confidential – © 2019 Oracle Restricted Oracle RDF4J Adapter Collaboration work between Semantic Web Company and the Oracle A-Team to create a new adapter for the Java based RDF4J library. RDF4J capabilities brings: • Community opportunity - RDF4J GitHub repo • Easy access to the Java community • Easy access and tested with PoolParty Semantic Suite 24 Confidential – © 2019 Oracle Restricted Oracle RDF4J Adapter 25 Confidential – © 2019 Oracle Restricted The World of Knowledge Graphs in Oracle Databases Introduction to Oracle Spatial and Graph Core Graph Database Features Oracle RDF4J Using PoolParty for Search, Similarity, Recommendation and Visualisation 26 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted Introducing Semantic Web Company Founded in 2004 Developer & Vendor of SWC named to KMWorld’s Based in Vienna PoolParty Semantic Suite ‘100 Companies That Matter in Privately held Participating in projects with Knowledge €2.5 million Management’ in 2016, until 2019 50+ FTE funding for R&D Software Engineers & ISO 27001:2013 Consultants for NLP, ~35% certified Semantics and Machine revenue growth/year learning 9,000 followers 27 PoolParty
Recommended publications
  • Mapping Spatiotemporal Data to RDF: a SPARQL Endpoint for Brussels
    International Journal of Geo-Information Article Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels Alejandro Vaisman 1, * and Kevin Chentout 2 1 Instituto Tecnológico de Buenos Aires, Buenos Aires 1424, Argentina 2 Sopra Banking Software, Avenue de Tevuren 226, B-1150 Brussels, Belgium * Correspondence: [email protected]; Tel.: +54-11-3457-4864 Received: 20 June 2019; Accepted: 7 August 2019; Published: 10 August 2019 Abstract: This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis.
    [Show full text]
  • Open Web Ontobud: an Open Source RDF4J Frontend
    Open Web Ontobud: An Open Source RDF4J Frontend Francisco José Moreira Oliveira University of Minho, Braga, Portugal [email protected] José Carlos Ramalho Department of Informatics, University of Minho, Braga, Portugal [email protected] Abstract Nowadays, we deal with increasing volumes of data. A few years ago, data was isolated, which did not allow communication or sharing between datasets. We live in a world where everything is connected, and our data mimics this. Data model focus changed from a square structure like the relational model to a model centered on the relations. Knowledge graphs are the new paradigm to represent and manage this new kind of information structure. Along with this new paradigm, a new kind of database emerged to support the new needs, graph databases! Although there is an increasing interest in this field, only a few native solutions are available. Most of these are commercial, and the ones that are open source have poor interfaces, and for that, they are a little distant from end-users. In this article, we introduce Ontobud, and discuss its design and development. A Web application that intends to improve the interface for one of the most interesting frameworks in this area: RDF4J. RDF4J is a Java framework to deal with RDF triples storage and management. Open Web Ontobud is an open source RDF4J web frontend, created to reduce the gap between end users and the RDF4J backend. We have created a web interface that enables users with a basic knowledge of OWL and SPARQL to explore ontologies and extract information from them.
    [Show full text]
  • A Performance Study of RDF Stores for Linked Sensor Data
    Semantic Web 1 (0) 1–5 1 IOS Press 1 1 2 2 3 3 4 A Performance Study of RDF Stores for 4 5 5 6 Linked Sensor Data 6 7 7 8 Hoan Nguyen Mau Quoc a,*, Martin Serrano b, Han Nguyen Mau c, John G. Breslin d , Danh Le Phuoc e 8 9 a Insight Centre for Data Analytics, National University of Ireland Galway, Ireland 9 10 E-mail: [email protected] 10 11 b Insight Centre for Data Analytics, National University of Ireland Galway, Ireland 11 12 E-mail: [email protected] 12 13 c Information Technology Department, Hue University, Viet Nam 13 14 E-mail: [email protected] 14 15 d Confirm Centre for Smart Manufacturing and Insight Centre for Data Analytics, National University of Ireland 15 16 Galway, Ireland 16 17 E-mail: [email protected] 17 18 e Open Distributed Systems, Technical University of Berlin, Germany 18 19 E-mail: [email protected] 19 20 20 21 21 Editors: First Editor, University or Company name, Country; Second Editor, University or Company name, Country 22 Solicited reviews: First Solicited Reviewer, University or Company name, Country; Second Solicited Reviewer, University or Company name, 22 23 Country 23 24 Open reviews: First Open Reviewer, University or Company name, Country; Second Open Reviewer, University or Company name, Country 24 25 25 26 26 27 27 28 28 29 Abstract. The ever-increasing amount of Internet of Things (IoT) data emanating from sensor and mobile devices is creating 29 30 new capabilities and unprecedented economic opportunity for individuals, organisations and states.
    [Show full text]
  • Storage, Indexing, Query Processing, And
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 May 2020 doi:10.20944/preprints202005.0360.v1 STORAGE,INDEXING,QUERY PROCESSING, AND BENCHMARKING IN CENTRALIZED AND DISTRIBUTED RDF ENGINES:ASURVEY Waqas Ali Department of Computer Science and Engineering, School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, Shanghai, China [email protected] Muhammad Saleem Agile Knowledge and Semantic Web (AKWS), University of Leipzig, Leipzig, Germany [email protected] Bin Yao Department of Computer Science and Engineering, School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, Shanghai, China [email protected] Axel-Cyrille Ngonga Ngomo University of Paderborn, Paderborn, Germany [email protected] ABSTRACT The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is a huge adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ different mechanisms to implement key components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is key for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a key optimization component of the RDF storage solutions.
    [Show full text]
  • Performance of RDF Library of Java, C# and Python on Large RDF Models
    et International Journal on Emerging Technologies 12(1): 25-30(2021) ISSN No. (Print): 0975-8364 ISSN No. (Online): 2249-3255 Performance of RDF Library of Java, C# and Python on Large RDF Models Mustafa Ali Bamboat 1, Abdul Hafeez Khan 2 and Asif Wagan 3 1Department of Computer Science, Sindh Madressatul Islam University (SMIU), Karachi, (Sindh), Pakistan. 2Department of Software Engineering, Sindh Madressatul Islam University (SMIU) Karachi, (Sindh), Pakistan. 3Department of Computer Science, Sindh Madressatul Islam University (SMIU), Karachi (Sindh), Pakistan. (Corresponding author: Mustafa Ali Bamboat) (Received 03 November 2020, Revised 22 December 2020, Accepted 28 January 2021) (Published by Research Trend, Website: www.researchtrend.net) ABSTRACT: The semantic web is an extension of the traditional web, in which contents are understandable to the machine and human. RDF is a Semantic Web technology used to create data stores, build vocabularies, and write rules for approachable LinkedData. RDF Framework expresses the Web Data using Uniform Resource Identifiers, which elaborate the resource in triples consisting of subject, predicate, and object. This study examines RDF libraries' performance on three platforms like Java, DotNet, and Python. We analyzed the performance of Apache Jena, DotNetRDF, and RDFlib libraries on the RDF model of LinkedMovie and Medical Subject Headings (MeSH) in aspects measuring matrices such as loading time, file traversal time, query response time, and memory utilization of each dataset. SPARQL is the RDF model's query language; we used six queries, three for each dataset, to analyze each query's response time on the selected RDF libraries. Keywords: dotNetRDF, Apache Jena, RDFlib, LinkedMovie, MeSH.
    [Show full text]
  • Graphdb-Free.Pdf
    GraphDB Free Documentation Release 8.5 Ontotext Jun 17, 2019 CONTENTS 1 General 1 1.1 About GraphDB...........................................2 1.2 Architecture & components.....................................2 1.2.1 Architecture.........................................2 1.2.1.1 RDF4J.......................................3 1.2.1.2 The Sail API....................................4 1.2.2 Components.........................................4 1.2.2.1 Engine.......................................4 1.2.2.2 Connectors.....................................5 1.2.2.3 Workbench.....................................5 1.3 GraphDB Free............................................5 1.3.1 Comparison of GraphDB Free and GraphDB SE......................6 1.4 Connectors..............................................6 1.5 Workbench..............................................6 2 Quick start guide 9 2.1 Run GraphDB as a desktop installation...............................9 2.1.1 On Windows........................................ 10 2.1.2 On Mac OS......................................... 10 2.1.3 On Linux.......................................... 10 2.1.4 Configuring GraphDB................................... 10 2.1.5 Stopping GraphDB..................................... 11 2.2 Run GraphDB as a stand-alone server................................ 11 2.2.1 Running GraphDB..................................... 11 2.2.1.1 Options...................................... 11 2.2.2 Configuring GraphDB................................... 12 2.2.2.1 Paths and network settings...........................
    [Show full text]
  • Emergent Relational Schemas for RDF
    Emergent Relational Schemas for RDF Minh Duc Pham Committee prof.dr. Frank van Harmelen prof.dr. Martin Kersten prof.dr. Josep Lluis Larriba Pey prof.dr. Thomas Neumann dr. Jacopo Urbani The research reported in this thesis has been partially carried out at CWI, the Dutch National Research Laboratory for Mathematics and Computer Science, within the theme Database Architectures. The research reported in this thesis has been partially carried out as part of the continuous research and development of the MonetDB open-source database man- agement system. SIKS Dissertation Series No. 2018-19 The research reported in this thesis has been carried out under the auspices of SIKS, the Dutch Research School for Information and Knowledge Systems. The cover was designed by the author. Photo by Leo Rivas on Unsplash. The printing and binding of this dissertation was carried out by Ipskamp Printing. ISBN 978-94-028-1110-0 VRIJE UNIVERSITEIT Emergent Relational Schemas for RDF ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Bètawetenschappen op donderdag 6 september 2018 om 15.45 uur in de aula van de universiteit, De Boelelaan 1105 door Minh Duc Pham geboren te Bac Ninh, Vietnam promotor: prof.dr. P.A. Boncz copromotor: prof.dr. S. Manegold Tặng bố mẹ của con vì tình yêu thương vô bờ bến, Tặng em yêu vì bao gian khổ, ngọt ngào và con trai - nguồn vui vô tận ..
    [Show full text]
  • Graphdb Free Documentation Release 8.11
    GraphDB Free Documentation Release 8.11 Ontotext Sep 26, 2019 CONTENTS 1 General 1 1.1 About GraphDB...........................................2 1.2 Architecture & Components.....................................2 1.2.1 Architecture.........................................2 1.2.1.1 RDF4J.......................................3 1.2.1.2 The Sail API....................................4 1.2.2 Components.........................................4 1.2.2.1 Engine.......................................4 1.2.2.2 Connectors.....................................5 1.2.2.3 Workbench.....................................5 1.3 GraphDB Free............................................5 1.3.1 Comparison of GraphDB Free and GraphDB SE......................6 1.4 Connectors..............................................6 1.5 Workbench..............................................6 2 Quick start guide 9 2.1 Run GraphDB as a Desktop Installation...............................9 2.1.1 On Windows........................................ 10 2.1.2 On MacOS......................................... 10 2.1.3 On Linux.......................................... 10 2.1.4 Configuring GraphDB................................... 10 2.1.5 Stopping GraphDB..................................... 11 2.2 Run GraphDB as a Standalone Server................................ 11 2.2.1 Running GraphDB..................................... 11 2.2.1.1 Options...................................... 11 2.2.2 Configuring GraphDB................................... 12 2.2.2.1 Paths and network settings...........................
    [Show full text]
  • Isa2 Action 2017.01 Standard-Based Archival
    Ref. Ares(2018)3256671 - 20/06/2018 ISA2 ACTION 2017.01 STANDARD-BASED ARCHIVAL DATA MANAGEMENT, EXCHANGE AND PUBLICATION STUDY FINAL REPORT Study on Standard-Based Archival Data Management, Exchange and Publication Final Report DOCUMENT METADATA Property Value Release date 15/06/2018 Status: Final version Version: V1.00 Susana Segura, Luis Gallego, Emmanuel Jamin, Miguel Angel Gomez, Seth Authors: van Hooland, Cédric Genin Lieven Baert, Julie Urbain, Annemie Vanlaer, Belá Harsanyi, Razvan Reviewed by: Ionescu, Reflection Committee Approved by: DOCUMENT HISTORY Version Description Action 0.10 First draft 0.90 Version for Review 0.98 Second version for Review 0.99 Third version for Acceptance 1.00 Final version 2 Study on Standard-Based Archival Data Management, Exchange and Publication Final Report TABLE OF CONTENTS Table of Contents ........................................................................................................................ 3 List of Figures ............................................................................................................................. 8 List of Tables ............................................................................................................................. 10 1 Executive Summary ........................................................................................................... 14 2 Introduction ........................................................................................................................ 16 2.1 Context ..........................................................................................................................
    [Show full text]
  • RDF Triplestores and SPARQL Endpoints
    RDF triplestores and SPARQL endpoints Lecturer: Mathias Bonduel [email protected] LDAC summer school 2019 – Lisbon, Portugal Lecture outline • Storing RDF data: RDF triplestores o Available methods to store RDF data o RDF triplestores o Triplestore applications – databases – default graph – named graphs o List of triplestore applications o Comparing triplestores o Relevant triplestore settings o Communication with triplestores • Distributing RDF data: SPARQL endpoints o Available methods to distribute RDF data o SPARQL endpoints o Reuse of SPARQL queries o SPARQL communication protocol: requests and responses June 18, 2019 RDF triplestores and SPARQL endpoints | Mathias Bonduel 2 Storing RDF data June 18, 2019 RDF triplestores and SPARQL endpoints | Mathias Bonduel 3 Available methods to store RDF data • In-memory storage (local RAM) o Working memory of application (e.g. client side web app, desktop app) o Frameworks/libraries: RDFLib (Python), rdflib.js (JavaScript), N3 (JavaScript), rdfstore-js (JavaScript), Jena (Java), RDF4J (Java), dotNetRDF (.NET), etc. o Varied support for SPARQL querying • Persistent storage (storage drive) o RDF file/dump (diff. RDF serializations): TTL, RDF/XML, N-Quads, JSON-LD, N-triples, TriG, N3, TriX, RDFa (RDF embedded in HTML), etc. o RDF triplestore o (ontology editing applications: Protégé, Topbraid Composer, etc.) June 18, 2019 RDF triplestores and SPARQL endpoints | Mathias Bonduel 4 RDF triplestores “a database to store and query RDF triples” • Member of the family of graph/NoSQL databases • Data structure: RDF • Main query language: SPARQL standards • Oftentimes support for RDFS/OWL/rules reasoning • Data storage is typically persistent June 18, 2019 RDF triplestores and SPARQL endpoints | Mathias Bonduel 5 Triplestore applications – databases - default graph - named graphs • An RDF triplestore instance (application) can have one or multiple databases (repositories) • Each database has one default graph and zero or more named graphs o a good practice is to place TBox in a separate named graph.
    [Show full text]
  • A Geosparql Compliance Benchmark Which Aims to Measure the Extent to Which an RDF Triplestore Complies with the Requirements Specified in the Geosparql Standard
    AGEOSPARQL COMPLIANCE BENCHMARK APREPRINT Milos Jovanovik Timo Homburg Ss. Cyril and Methodius Univesity in Skopje, N. Macedonia Mainz University Of Applied Sciences, Germany OpenLink Software, London, UK [email protected] [email protected] Mirko Spasic´ University of Belgrade, Serbia OpenLink Software, London, UK [email protected] February 12, 2021 ABSTRACT We propose a series of tests that check for the compliance of RDF triplestores with the GeoSPARQL standard. The purpose of the benchmark is to test how many of the requirements outlined in the standard a tested system supports and to push triplestores forward in achieving a full GeoSPARQL compliance. This topic is of concern because the support of GeoSPARQL varies greatly between different triplestore implementations, and such support is of great importance for the domain of geospatial RDF data. Additionally, we present a comprehensive comparison of triplestores, providing an insight into their current GeoSPARQL support. Keywords GeoSPARQL · Benchmarking · RDF · SPARQL 1 Introduction The geospatial Semantic Web [1] as part of the Semantic Web [2] represents an ever-growing semantically interpreted wealth of geospatial information. The initial research [3] and the subsequent introduction of the OGC GeoSPARQL standard [4] formalized geospatial vector data representations (WKT [5] and GML [6]) in ontologies, and extended the SPARQL query language [7] with support for spatial relation operators. arXiv:2102.06139v1 [cs.DB] 11 Feb 2021 Several RDF storage solutions have since adopted GeoSPARQL to various extents as features of their triplestore implementations [8, 9]. These varying levels of implementation may lead to some false assumptions of users when choosing an appropriate triplestore implementation for their project.
    [Show full text]
  • Tendências Atuais E Perspetivas Futuras Em Organização Do Conhecimento
    TENDÊNCIAS ATUAIS E PERSPETIVAS FUTURAS EM ORGANIZAÇÃO DO CONHECIMENTO ATAS DO III CONGRESSO ISKO ESPANHA-PORTUGAL XIII CONGRESSO ISKO ESPANHA Universidade de Coimbra, 23 e 24 de novembro de 2017 Com a coordenação de Maria da Graça Simões, Maria Manuel Borges TÍTULO Tendências Atuais e Perspetivas Futuras em Organização do Conhecimento: atas do III Congresso ISKO Espanha e Portugal - XIII Congresso ISKO Espanha COORDENADORES Maria da Graça Simões Maria Manuel Borges EDIÇÃO Universidade de Coimbra. Centro de Estudos Interdisciplinares do Século XX - CEIS20 ISBN 978-972-8627-75-1 ACESSO https://purl.org/sci/atas/isko2017 COPYRIGHT Este trabalho está licenciado com uma Licença Creative Commons - Atribuição 4.0 Internacional (https://creativecommons.org/licenses/by/4.0/deed.pt) OBRA PUBLICADA COM O APOIO DE PROJETO UID/HIS/00460/2013 DESAFIOS À ORGANIZAÇÃO E ACESSO AO PATRIMÓNIO CULTURAL COLECCIONES DE DATOS ABIERTOS ENLAZADOS: DE LA BÚSQUEDA AL DESCUBRIMIENTO DE INFORMACIÓN María Luisa Alvite Díez Área de Biblioteconomía y Documentación. Universidad de León, 0000-0003-1490-8936, [email protected] RESUMEN En el ámbito bibliotecario se abordan desde hace años proyectos dirigidos a la publicación de datos, metadatos y vocabularios aplicando el modelo de “Datos abiertos enlazados”. El acceso a estos datos se ha orientado, mayoritariamente, a usuarios expertos -desarrolladores- que utilizan SPARQL para interrogar estos silos de información. En este contexto, el estudio dirige su mirada a las soluciones concretas enfocadas a usuarios finales que han sido implementadas por instituciones inmersas en proyectos de datos abiertos enlazados. Se pretende realizar una aproximación sobre las características básicas de las interfaces de usuario en una muestra de portales de datos abiertos y extraer conclusiones sobre la potencia de búsqueda, la usabilidad y las tendencias observables en la visualización de la información en este tipo de proyectos.
    [Show full text]