Allegrograph Python Client Documentation Release 100.2.1.Dev0
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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. -
Franz's Allegrograph 7.1 Accelerates Complex Reasoning Across
Franz’s AllegroGraph 7.1 Accelerates Complex Reasoning Across Massive, Distributed Knowledge Graphs and Data Fabrics Distributed FedShard Queries Improved 10X. New SPARQL*, RDF* and SHACL Features Added. Lafayette, Calif., February 8, 2021 — Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for AI Knowledge Graph Solutions, today announced AllegroGraph 7.1, which provides optimizations for deeply complex queries across FedShard™ deployments – making complex reasoning up to 10X faster. AllegroGraph with FedShard offers a breakthrough solution that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution for Enterprise scale analytics. AllegroGraph’s unique capabilities drive 360-degree insights and enable complex reasoning across enterprise-wide Knowledge Fabrics in Healthcare, Media, Smart Call Centers, Pharmaceuticals, Financial and much more. “The proliferation of Artificial Intelligence has resulted in the need for increasingly complex queries over more data,” said Dr. Jans Aasman, CEO of Franz Inc. “AllegroGraph 7.1 addresses two of the most daunting challenges in AI – continuous data integration and the ability to query across all the data. With this new version of AllegroGraph, organizations can create Data Fabrics underpinned by AI Knowledge Graphs that take advantage of the infinite data integration capabilities possible with our FedShard technology and the ability to query across -
Using Microsoft Power BI with Allegrograph,Knowledge Graphs Rise
Using Microsoft Power BI with AllegroGraph There are multiple methods to integrate AllegroGraph SPARQL results into Microsoft Power BI. In this document we describe two best practices to automate queries and refresh results if you have a production AllegroGraph database with new streaming data: The first method uses Python scripts to feed Power BI. The second method issues SPARQL queries directly from Power BI using POST requests. Method 1: Python Script: Assuming you know Python and have it installed locally, this is definitely the easiest way to incorporate SPARQL results into Power BI. The basic idea of the method is as follows: First, the Python script enables a connection to your desired AllegroGraph repository. Then we utilize AllegroGraph’s Python API within our script to run a SPARQL query and return it as a Pandas dataframe. When running this script within Power BI Desktop, the Python scripting service recognizes all unique dataframes created, and allows you to import the dataframe into Power BI as a table, which can then be used to create visualizations. Requirements: 1. You must have the AllegroGraph Python API installed. If you do not, installation instructions are here: https://franz.com/agraph/support/documentation/current/p ython/install.html 2. Python scripting must be enabled in Power BI Desktop. Instructions to do so are here: https://docs.microsoft.com/en-us/power-bi/connect-data/d esktop-python-scripts a) As mentioned in the article, pandas and matplotlib must be installed. This can be done with ‘pip install pandas’ and ‘pip install matplotlib’ in your terminal. -
Practical Semantic Web and Linked Data Applications
Practical Semantic Web and Linked Data Applications Common Lisp Edition Uses the Free Editions of Franz Common Lisp and AllegroGraph Mark Watson Copyright 2010 Mark Watson. All rights reserved. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works Version 3.0 United States License. November 3, 2010 Contents Preface xi 1. Getting started . xi 2. Portable Common Lisp Code Book Examples . xii 3. Using the Common Lisp ASDF Package Manager . xii 4. Information on the Companion Edition to this Book that Covers Java and JVM Languages . xiii 5. AllegroGraph . xiii 6. Software License for Example Code in this Book . xiv 1. Introduction 1 1.1. Who is this Book Written For? . 1 1.2. Why a PDF Copy of this Book is Available Free on the Web . 3 1.3. Book Software . 3 1.4. Why Graph Data Representations are Better than the Relational Database Model for Dealing with Rapidly Changing Data Requirements . 4 1.5. What if You Use Other Programming Languages Other Than Lisp? . 4 2. AllegroGraph Embedded Lisp Quick Start 7 2.1. Starting AllegroGraph . 7 2.2. Working with RDF Data Stores . 8 2.2.1. Creating Repositories . 9 2.2.2. AllegroGraph Lisp Reader Support for RDF . 10 2.2.3. Adding Triples . 10 2.2.4. Fetching Triples by ID . 11 2.2.5. Printing Triples . 11 2.2.6. Using Cursors to Iterate Through Query Results . 13 2.2.7. Saving Triple Stores to Disk as XML, N-Triples, and N3 . 14 2.3. AllegroGraph’s Extensions to RDF . -
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. -
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. -
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. -
Gábor Melis to Keynote European Lisp Symposium
Gábor Melis to Keynote European Lisp Symposium OAKLAND, Calif. — April 14, 2014 —Franz Inc.’s Senior Engineer, Gábor Melis, will be a keynote speaker at the 7th annual European Lisp Symposium (ELS’14) this May in Paris, France. The European Lisp Symposium provides a forum for the discussion and dissemination of all aspects of design, implementationand application of any of the Lisp and Lisp- inspired dialects, including Common Lisp, Scheme, Emacs Lisp, AutoLisp, ISLISP, Dylan, Clojure, ACL2, ECMAScript, Racket, SKILL, Hop etc. Sending Beams into the Parallel Cube A pop-scientific look through the Lisp lens at machine learning, parallelism, software, and prize fighting We send probes into the topic hypercube bounded by machine learning, parallelism, software and contests, demonstrate existing and sketch future Lisp infrastructure, pin the future and foreign arrays down. We take a seemingly random walk along the different paths, watch the scenery of pairwise interactions unfold and piece a puzzle together. In the purely speculative thread, we compare models of parallel computation, keeping an eye on their applicability and lisp support. In the the Python and R envy thread, we detail why lisp could be a better vehicle for scientific programming and how high performance computing is eroding lisp’s largely unrealized competitive advantages. Switching to constructive mode, a basic data structure is proposed as a first step. In the machine learning thread, lisp’s unparalleled interactive capabilities meet contests, neural networks cross threads and all get in the way of the presentation. Video Presentation About Gábor Melis Gábor Melis is a consultant at Franz Inc. -
Evaluation of Current RDF Database Solutions
Evaluation of Current RDF Database Solutions Florian Stegmaier1, Udo Gr¨obner1, Mario D¨oller1, Harald Kosch1 and Gero Baese2 1 Chair of Distributed Information Systems University of Passau Passau, Germany [email protected] 2 Corporate Technology Siemens AG Munich, Germany [email protected] Abstract. Unstructured data (e.g., digital still images) is generated, distributed and stored worldwide at an ever increasing rate. In order to provide efficient annotation, storage and search capabilities among this data and XML based description formats, data stores and query languages have been introduced. As XML lacks on expressing semantic meanings and coherences, it has been enhanced by the Resource Descrip- tion Format (RDF) and the associated query language SPARQL. In this context, the paper evaluates currently existing RDF databases that support the SPARQL query language by the following means: gen- eral features such as details about software producer and license infor- mation, architectural comparison and efficiency comparison of the inter- pretation of SPARQL queries on a scalable test data set. 1 Introduction The production of unstructured data especially in the multimedia domain is overwhelming. For instance, recent studies3 report that 60% of today's mobile multimedia devices equipped with an image sensor, audio support and video playback have basic multimedia functionalities, almost nine out of ten in the year 2011. In this context, the annotation of unstructured data has become a necessity in order to increase retrieval efficiency during search. In the last couple of years, the Extensible Markup Language (XML) [16], due to its interoperability features, has become a de-facto standard as a basis for the use of description formats in various domains. -
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. -
Enhance Entity Extraction Using an RDF Store
Enhance Entity Extraction Using an RDF Store Jans Aasman, Franz Inc., 2201 Broadway, Suite 715 Oakland, CA 94612, USA www.franz.com [email protected] Rita Joseph Expert System, 1464 Waggaman Circle McLean, VA 22101, USA www.expertsystem.net [email protected] Abstract. Using robust entity extraction technology allows for the automatic comprehension of words, sentences, paragraphs and whole documents. Categorization, extraction, domain establishment, taxonomy and ontology creation can then be built upon a semantic technology infra-structure to exploit the capabilities provided in OWL and RDF. This technology brings value to the intelligence community by allowing users to find, discover and create structured knowledge connections from what were previously unstructured information sets. The presentation “Enhance Entity Extraction Using an RDF Store” will demonstrate how to represent output from Expert System’s Cogito entity extractor in a Franz AllegroGraph RDF database, and perform reasoning along with visually generated SPARQL queries. We will demonstrate a new product called TexTriples that combines professional entity extraction, a scalable triple-store, and intelligent web spidering. We will show how TexTriples collects thousands of articles from newspapers and blogs, and processes them through Cogito to create RDF triples for use in AllegroGraph. The presentation will discuss tips and techniques in dealing with these representations, demonstrate how to relate entities to Linked Data such as DBpedia, Scalability, and finally we will perform a number of queries on the resulting triple store data using some straight forward inferencing. Keywords: entity extraction, RDF, OWL, taxonomy, ontology, SPARQL, Semantic Web 1 Entity Extraction: Cogito’s Approach Cogito collects all the structural and lexical text aspects in order to comprehend natural language and understand the meanings of words and sentences. -
Report on Allegrograph Rdfstore Submitted by Ruku Roychowdhury
Report On AllegroGraph RDFStore Submitted By Ruku Roychowdhury Shagun Kariwala CSC 8711 Database and the Web Spring 2011 Table of content • Introduction • Allegrograph Features o Installation instructions o Allegrograph Architecture o Allegrograph Database • Allegrograph Triple Store o Creating a Triple store o Adding triples into Triple Store o Retrieving triples o Query on Triple Store o Deleting Triples • Gruff • Recent Projects in AllegroGraph • Conclusion • References Introduction :- Semantic web technology aim to build websites with sufficient self describing data so that computers can browse through them as easily humans can do.Allegrograph is a framework to develop semantic web applications.It is a high performance Graph Database to store this data and metadata in triple format. Allegrograph provides various query APIs to perform query on these triples. It also has its own built in RDFS++ reasoner but it is not complete.Allegrograph can be integrated to other powerful GUI based reasoner if more elaborated reasoning is required. Installation :- Allegrograph runs as a service. We have to install the server first and start the service. The latest version of Allegrograph Server, Allegrograph Server 4 runs natively on Linux x86-64 bit machine. It is advisable to to set up a Linux Virtual machine if we want to run it on other Operating Systems (Windows or MAC). Allegrograph 3.3 can run on 64-bit Mac, Windows, and Solaris, and all 32-bit systems. Allegrograph server can work with a large variety of programming interfaces like Java, Common Lisp and Prolog to name a few. Future editions of Allegrograph will also have interfaces for C# and Ruby.