RDF Query Languages Need Support for Graph Properties
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Semantics Developer's Guide
MarkLogic Server Semantic Graph Developer’s Guide 2 MarkLogic 10 May, 2019 Last Revised: 10.0-8, October, 2021 Copyright © 2021 MarkLogic Corporation. All rights reserved. MarkLogic Server MarkLogic 10—May, 2019 Semantic Graph Developer’s Guide—Page 2 MarkLogic Server Table of Contents Table of Contents Semantic Graph Developer’s Guide 1.0 Introduction to Semantic Graphs in MarkLogic ..........................................11 1.1 Terminology ..........................................................................................................12 1.2 Linked Open Data .................................................................................................13 1.3 RDF Implementation in MarkLogic .....................................................................14 1.3.1 Using RDF in MarkLogic .........................................................................15 1.3.1.1 Storing RDF Triples in MarkLogic ...........................................17 1.3.1.2 Querying Triples .......................................................................18 1.3.2 RDF Data Model .......................................................................................20 1.3.3 Blank Node Identifiers ..............................................................................21 1.3.4 RDF Datatypes ..........................................................................................21 1.3.5 IRIs and Prefixes .......................................................................................22 1.3.5.1 IRIs ............................................................................................22 -
Web and Semantic Web Query Languages: a Survey
Web and Semantic Web Query Languages: A Survey James Bailey1, Fran¸coisBry2, Tim Furche2, and Sebastian Schaffert2 1 NICTA Victoria Laboratory Department of Computer Science and Software Engineering The University of Melbourne, Victoria 3010, Australia http://www.cs.mu.oz.au/~jbailey/ 2 Institute for Informatics,University of Munich, Oettingenstraße 67, 80538 M¨unchen, Germany http://pms.ifi.lmu.de/ Abstract. A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This ar- ticle introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced us- ing common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion. 1 Introduction The Semantic Web Vision A major endeavour in current Web research is the so-called Semantic Web, a term coined by W3C founder Tim Berners-Lee in a Scientific American article describing the future of the Web [37]. The Semantic Web aims at enriching Web data (that is usually represented in (X)HTML or other XML formats) by meta-data and (meta-)data processing specifying the “meaning” of such data and allowing Web based systems to take advantage of “intelligent” reasoning capabilities. To quote Berners-Lee et al. [37]: “The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users.” The Semantic Web meta-data added to today’s Web can be seen as advanced semantic indices, making the Web into something rather like an encyclopedia. -
XML-Based RDF Data Management for Efficient Query Processing
XML-Based RDF Data Management for Efficient Query Processing Mo Zhou Yuqing Wu Indiana University, USA Indiana University, USA [email protected] [email protected] Abstract SPARQL[17] is a W3C recommended RDF query language. A The Semantic Web, which represents a web of knowledge, offers SPARQL query contains a collection of triples with variables called new opportunities to search for knowledge and information. To simple access patterns which form graph patterns for describing harvest such search power requires robust and scalable data repos- query requirements. For SELECT ?t example, the SPARQL itories that can store RDF data and support efficient evaluation of WHERE {?p type Person. ?r ?x ?t. SPARQL queries. Most of the existing RDF storage techniques rely ?p name ?n. ?p write ?r} query on the left re- on relation model and relational database technologies for these trieves all properties of tasks. They either keep the RDF data as triples, or decompose it reviews written by a person whose name is known. into multiple relations. The mis-match between the graph model The needs to develop applications on the Semantic Web and sup- of the RDF data and the rigid 2D tables of relational model jeop- port search in RDF graphs call for RDF repositories to be reliable, ardizes the scalability of such repositories and frequently renders a robust and efficient in answering SPARQL queries. As in the con- repository inefficient for some types of data and queries. We pro- text of RDB and XML, the selection of storage models is critical to pose to decompose RDF graph into a forest of semantically cor- a data repository as it is the dominating factor to determine how to related XML trees, store them in an XML repository and rewrite evaluate queries and how the system behaves when it scales up. -
Common Sense Reasoning with the Semantic Web
Common Sense Reasoning with the Semantic Web Christopher C. Johnson and Push Singh MIT Summer Research Program Massachusetts Institute of Technology, Cambridge, MA 02139 [email protected], [email protected] http://groups.csail.mit.edu/dig/2005/08/Johnson-CommonSense.pdf Abstract Current HTML content on the World Wide Web has no real meaning to the computers that display the content. Rather, the content is just fodder for the human eye. This is unfortunate as in fact Web documents describe real objects and concepts, and give particular relationships between them. The goal of the World Wide Web Consortium’s (W3C) Semantic Web initiative is to formalize web content into Resource Description Framework (RDF) ontologies so that computers may reason and make decisions about content across the Web. Current W3C work has so far been concerned with creating languages in which to express formal Web ontologies and tools, but has overlooked the value and importance of implementing common sense reasoning within the Semantic Web. As Web blogging and news postings become more prominent across the Web, there will be a vast source of natural language text not represented as RDF metadata. Common sense reasoning will be needed to take full advantage of this content. In this paper we will first describe our work in converting the common sense knowledge base, ConceptNet, to RDF format and running N3 rules through the forward chaining reasoner, CWM, to further produce new concepts in ConceptNet. We will then describe an example in using ConceptNet to recommend gift ideas by analyzing the contents of a weblog. -
Answering SPARQL Queries Modulo RDF Schema with Paths Faisal Alkhateeb, Jérôme Euzenat
Answering SPARQL queries modulo RDF Schema with paths Faisal Alkhateeb, Jérôme Euzenat To cite this version: Faisal Alkhateeb, Jérôme Euzenat. Answering SPARQL queries modulo RDF Schema with paths. [Research Report] RR-8394, INRIA. 2013, pp.46. hal-00904961 HAL Id: hal-00904961 https://hal.inria.fr/hal-00904961 Submitted on 15 Nov 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Answering SPARQL queries modulo RDF Schema with paths Faisal Alkhateeb, Jérôme Euzenat RESEARCH REPORT N° 8394 November 2013 Project-Teams Exmo ISSN 0249-6399 ISRN INRIA/RR--8394--FR+ENG Answering SPARQL queries modulo RDF Schema with paths Faisal Alkhateeb∗, Jérôme Euzenat† Project-Teams Exmo Research Report n° 8394 — November 2013 — 46 pages Abstract: SPARQL is the standard query language for RDF graphs. In its strict instantiation, it only offers querying according to the RDF semantics and would thus ignore the semantics of data expressed with respect to (RDF) schemas or (OWL) ontologies. Several extensions to SPARQL have been proposed to query RDF data modulo RDFS, i.e., interpreting the query with RDFS semantics and/or considering external ontologies. We introduce a general framework which allows for expressing query answering modulo a particular semantics in an homogeneous way. -
Use Style: Paper Title
IJRECE VOL. 6 ISSUE 2 APR.-JUNE 2018 ISSN: 2393-9028 (PRINT) | ISSN: 2348-2281 (ONLINE) A Review on Reasoning System, Types and Tools and Need for Hybrid Reasoning Goutami M.K1, Mrs. Rashmi S R2, Bharath V3 1M.Tech, Computer Network Engineering, Dept. of Computer Science and Engineering, Dayananda Sagar college of Engineering, Bangalore. (Email: [email protected]) 2Asst. Professor, Dept. of Computer Science and Engineering, Dayananda Sagar college of Engineering , Bangalore. (Email: [email protected]) 3Dept. of Computer Science and Engineering, Dayananda Sagar college of Engineering, Bangalore Abstract— Expert system is a programming system which knowledge base then we require a programming system that utilizes the information of expert knowledge of the specific will facilitate us to access the knowledge for reasoning purpose domain to make decisions. These frameworks are intended to and helps in decision making and problem solving. Inference tackle complex issues by thinking through the learning process engine is an element of the system that applies logical rules to called as reasoning. The knowledge used for reasoning is the knowledge base to deduce new information. User interface represented mainly as if-then rules. Expert system forms a is one through which the user can communicate with the expert significant part in day-to-day life nowadays as it emulates the system. expert’s behavior in analyzing the information and concludes “A reasoning system is a software system that uses available knowledge, applies logical techniques to generate decision. This paper primarily centers on the reasoning system conclusions”. It uses logical techniques such as deduction which plays the fundamental part in field of artificial approach and induction approach. -
A Comparison of RDF Query Languages
A Comparison of RDF Query Languages Peter Haase1, Jeen Broekstra2, Andreas Eberhart1, Raphael Volz1 1Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe, Germany haase, eberhart, volz @aifb.uni-karlsruhe.de f 2 g Vrije Universiteit Amsterdam, The Netherlands [email protected] Abstract. The purpose of this paper is to provide a rigorous compari- son of six query languages for RDF. We outline and categorize features that any RDF query language should provide and compare the individ- ual languages along these features. We describe several practical usage examples for RDF queries and conclude with a comparison of the expres- siveness of the particular query languages. The usecases, sample data and queries for the respective languages are available on the web1. 1 Introduction The Resource Description Framework (RDF) is considered to be the most rele- vant standard for data representation and exchange on the Semantic Web. The recent recommendation of RDF has just completed a major clean up of the initial proposal [9] in terms of syntax [1], along with a clarification of the un- derlying data model [8], and its intended interpretation [5]. Several languages for querying RDF documents and have been proposed, some in the tradition of database query languages (i.e. SQL, OQL), others more closely inspired by rule languages. No standard for RDF query language has yet emerged, but the dis- cussion is ongoing within both academic institutions, Semantic Web enthusiasts and the World Wide Web Consortium (W3C). The W3C recently chartered a working group2 with a focus on accessing and querying RDF data. We present a comparison of six representative query languages for RDF, highlighting their common features and differences along general dimensions for query languages and particular requirements for RDF. -
Chapter 3 Querying RDF Stores with SPARQL
Why an RDF Query Language? Chapter 3 l Why not use an XML query language? Querying RDF l XML at a lower level of abstraction than stores with RDF l There are various ways of syntactically SPARQL representing an RDF statement in XML l Thus we would require several XPath queries, e.g. – //uni:lecturer/uni:title if uni:title element – //uni:lecturer/@uni:title if uni:title attribute – Both XML representations equivalent! Enter SPARQL SPARQL Example l SPARQL Protocol and RDF Query Language PREFIX foaf: <http://xmlns.com/foaf/0.1/> l W3C began developing a spec for a query SELECT ?name ?age language in 2004 WHERE { l There were/are other RDF query languages, and ?person a foaf:Person. extensions, e.g., RQL, Jena’s ARQ, ?person foaf:name ?name. l SPARQL a W3C recommendation in 2008 ?person foaf:age ?age – Query language + protocol + xml result format } l SPARQL 1.1 currently a last-call working draft ORDER BY ?age DESC – Includes updates, aggregation functions, federation, … LIMIT 10 l Most triple stores support SPARQL 1 SPARQL Protocol, Endpoints, APIs SPARQL Basic Queries l SPARQL query language l SPARQL is based on matching graph patterns l SPROT = SPARQL Protocol for RDF l The simplest graph pattern is the triple pattern - – Among other things specifies how results can be ?person foaf:name ?name encoded as RDF, XML or JSON - Like an RDF triple, but variables can be in any position l SPARQL endpoint - Variables begin with a question mark – A service that accepts queries and returns results via HTTP l Combining triple patterns gives a graph pattern; an exact match to a graph is needed – Either generic (fetching data as needed) or specific (querying an associated triple store) l Like SQL, a set of results is returned, not just one – May be a service for federated queries Turtle Like Syntax Optional Data As in N# and Turtle, we can omit a common subject l The query fails unless the entire patter matches in a graph pattern. -
On Querying Ontologies with Contextual Logic Programming
On Querying Ontologies with Contextual Logic Programming Cl´audio Fernandes, Nuno Lopes, and Salvador Abreu Universidade de Evora´ Abstract. We describe a system in which Contextual Logic Program- ming is used as a mediator for knowledge modeled by ontologies. Our system provides the components required to behave as a SPARQL query engine and, as a result of its Logic Programming heritage, it may also recursively interrogate other ontologies or data repositories, providing a semantic integration of multiple sources. 1 Introduction The Semantic Web topic currently represents one of the most active and ex- citing research areas in computer science. As the originator and mentor of this vision Tim Berners-Lee puts it [5], the Semantic Web is a natural evolution of the Internet and, hopefully, will provide the foundations for the emergence of intelligent systems and agent layers over the world wide web. The standard web page provides data oriented for human comprehension, which means a computer agent cannot intelligently reason about that information. The Semantic Web thrives the creation of information technology that will allow explicit machine- processable meta-data documents that describes the meaning and semantics of the data published in the Web. One important step towards the fulfilling of this vision is the emergence of systems that cannot only understand and reason over Semantic Web documents but also retrieve and process knowledge of multiple information sources. This represents the motivation and purpose of our work which is to use contextual con- straint logic programming [1] as a framework for Semantic Web agents, in which knowledge representation and reasoning for ontology documents can be carried out. -
Introduction to RDF & SPARQL
OPEN Presentation DATA metadata SUPPORT Open Data Support is Training Module 1.3 funded by the European Commission under SMART 2012/0107 ‘Lot 2: Provision of services for the Publication, Access and Reuse of Open Introduction to RDF & Public Data across the European Union, through existing open data SPARQL portals’(Contract No. 30-CE- 0530965/00-17). © 2014 European Commission OPEN DATASUPPORT Slide 1 Learning objectives By the end of this training module you should have an understanding of: • The Resource Description Framework (RDF). • How to write/read RDF. • How you can describe your data with RDF. • What SPARQL is. • The different types of SPARQL queries. • How to write a SPARQL query. OPEN DATASUPPORT Slide 2 Content This module contains ... • An introduction to the Resource Description Framework (RDF) for describing your data. - What is RDF? - How is it structured? - How to represent your data in RDF. • An introduction to SPARQL on how you can query and manipulate data in RDF. • Pointers to further reading, examples and exercises. OPEN DATASUPPORT Slide 3 Resource Description Framework An introduction on RDF. OPEN DATASUPPORT Slide 4 RDF in the stack of Semantic Web technologies • RDF stands for: - Resource: Everything that can have a unique identifier (URI), e.g. pages, places, people, dogs, products... - Description: attributes, features, and relations of the resources - Framework: model, languages and syntaxes for these descriptions • RDF was published as a W3C recommendation in 1999. • RDF was originally introduced as a data -
I-SEMANTICS '08: International Conference on Semantic Systems
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Auer, Sören (Ed.); Schaffert, Sebastian (Ed.); Pellegrini, Tassilo (Ed.) Proceedings — Published Version I-SEMANTICS '08: International conference on semantic systems Suggested Citation: Auer, Sören (Ed.); Schaffert, Sebastian (Ed.); Pellegrini, Tassilo (Ed.) (2008) : I-SEMANTICS '08: International conference on semantic systems, Verlag der Technischen Universität Graz, Graz This Version is available at: http://hdl.handle.net/10419/44448 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu I-SEMANTICS ’08 International Conference on Semantic Systems Preface 1 S. Auer, S. Schaffert, T. Pellegrini Wikis in Global Businesses 4 P. Kemper Managing Knowledge that Everybody Knows Already 5 H. Lieberman Humans and the Web of Data 6 T. -
Graph Databases for Beginners Bryce Merkl Sasaki, Joy Chao & Rachel Howard
The #1 Platform for Connected Data EBOOK Graph Databases for Beginners Bryce Merkl Sasaki, Joy Chao & Rachel Howard neo4j.com The #1 Platform for Connected Data Ebook TABLE OF CONTENTS Introduction 1 Graph Databases Graphs Are the Future 2 For Beginners Why Data Relationships Matter 4 Bryce Merkl Sasaki, Joy Chao & Rachel Howard Data Modeling Basics 6 Data Modeling Pitfalls to Avoid 9 Introduction: Welcome to the World of Graph Technology Why a Database Query So you’ve heard about graph databases and you want to know what all the buzz is about. Are Language Matters 13 they just a passing trend — here today and gone tomorrow – or are they a rising tide your business and your development team can’t afford to pass up? Imperative vs. Declarative Query Languages 19 Whether you’re a business executive or a seasoned developer, something — maybe a business challenge or a task your current database can’t handle — has led you on the quest to learn more about graphs and what they can do. Graph Theory & Predictive Modeling 21 In this Graph Databases For Beginners ebook, we’ll take you through the basics of graph technology assuming you have little (or no) background in the technology. We’ll also include some useful tips that will help you if you decide to make the switch to Neo4j. Graph Search Algorithm Basics 24 Before we dive in, what is it that makes graphs not only relevant, but necessary in today’s world? The first is its focus onrelationships . Collectively we are gathering more data than ever before, but more and more frequently it’s how that data is related that is truly important.