Cooking HTTP Content Negotiation with Vapour
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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 -
PDF Formats; the XHTML Version Has Active Links That You Can Follow
Semantic Web @ W3C: Activities, Recommendations and State of Adoption Athens, GA, USA, 2006-11-09 Ivan Herman, W3C Ivan Herman, W3C RDF(S), tools We have a solid specification since 2004: well defined (formal) semantics, clear RDF/XML syntax Lots of tools are available. Are listed on W3C’s wiki: RDF programming environment for 14+ languages, including C, C++, Python, Java, Javascript, Ruby, PHP,… (no Cobol or Ada yet ) 13+ Triple Stores, ie, database systems to store datasets 16+ general development tools (specialized editors, application builders, …) etc Ivan Herman, W3C RDF(S), tools (cont.) Note the large number of large corporations among the tool developers: Adobe, IBM, Software AG, Oracle, HP, Northrop Grumman, … …but the small companies and independent developers also play a major role! Some of the tools are Open Source, some are not; some are very mature, some are not : it is the usual picture of software tools, nothing special any more! Anybody can start developing RDF-based applications today Ivan Herman, W3C RDF(S), tools (cont.) There are lots of tutorials, overviews, or books around the wiki page on books lists 20+ (English) textbooks; 19+ proceedings for 2005 & 2006 alone… again, some of them good, some of them bad, just as with any other areas… Active developers’ communities Ivan Herman, W3C Large datasets are accumulating IngentaConnect bibliographic metadata storage: over 200 million triplets UniProt Protein Database: 262 million triplets RDF version of Wikipedia: more than 47 million triplets RDFS/OWL Representation -
Rdfa in XHTML: Syntax and Processing Rdfa in XHTML: Syntax and Processing
RDFa in XHTML: Syntax and Processing RDFa in XHTML: Syntax and Processing RDFa in XHTML: Syntax and Processing A collection of attributes and processing rules for extending XHTML to support RDF W3C Recommendation 14 October 2008 This version: http://www.w3.org/TR/2008/REC-rdfa-syntax-20081014 Latest version: http://www.w3.org/TR/rdfa-syntax Previous version: http://www.w3.org/TR/2008/PR-rdfa-syntax-20080904 Diff from previous version: rdfa-syntax-diff.html Editors: Ben Adida, Creative Commons [email protected] Mark Birbeck, webBackplane [email protected] Shane McCarron, Applied Testing and Technology, Inc. [email protected] Steven Pemberton, CWI Please refer to the errata for this document, which may include some normative corrections. This document is also available in these non-normative formats: PostScript version, PDF version, ZIP archive, and Gzip’d TAR archive. The English version of this specification is the only normative version. Non-normative translations may also be available. Copyright © 2007-2008 W3C® (MIT, ERCIM, Keio), All Rights Reserved. W3C liability, trademark and document use rules apply. Abstract The current Web is primarily made up of an enormous number of documents that have been created using HTML. These documents contain significant amounts of structured data, which is largely unavailable to tools and applications. When publishers can express this data more completely, and when tools can read it, a new world of user functionality becomes available, letting users transfer structured data between applications and web sites, and allowing browsing applications to improve the user experience: an event on a web page can be directly imported - 1 - How to Read this Document RDFa in XHTML: Syntax and Processing into a user’s desktop calendar; a license on a document can be detected so that users can be informed of their rights automatically; a photo’s creator, camera setting information, resolution, location and topic can be published as easily as the original photo itself, enabling structured search and sharing. -
Linked Data Overview and Usage in Social Networks
Linked Data Overview and Usage in Social Networks Gustavo G. Valdez Technische Universitat Berlin Email: project [email protected] Abstract—This paper intends to introduce the principles of Webpages, also possibly reference any Object or Concept (or Linked Data, show some possible uses in Social Networks, even relationships of concepts/objects). advantages and disadvantages of using it, aswell as presenting The second one combines the unique identification of the an example application of Linked Data to the field of social networks. first with a well known retrieval mechanism (HTTP), enabling this URIs to be looked up via the HTTP protocol. I. INTRODUCTION The third principle tackles the problem of using the same The web is a very good source of human-readable informa- document standards, in order to have scalability and interop- tion that has improved our daily life in several areas. By using erability. This is done by having a standard, the Resource De- rich text and hyperlinks, it made our access to information scription Framewok (RDF), which will be explained in section something more active rather than passive. But the web was III-A. This standards are very useful to provide resources that made for humans and not for machines, even though today are machine-readable and are identified by a URI. a lot of the acesses to the web are made by machines. This The fourth principle basically advocates that hyperlinks has lead to the search for adding structure, so that data is should be used not only to link webpages, but also to link more machine-readable. -
A Visual Notation for the Integrated Representation of OWL Ontologies
A Visual Notation for the Integrated Representation of OWL Ontologies Stefan Negru1 and Steffen Lohmann2 1Faculty of Computer Science, Alexandru Ioan Cuza University, General Berthelot 16, 700483 Iasi, Romania 2Institute for Visualization and Interactive Systems (VIS), Universit¨atsstraße 38, 70569 Stuttgart, Germany [email protected], [email protected] Keywords: Ontology Visualization, Visual Notation, OWL, Semantic Web, Knowledge Visualization, Ontologies. Abstract: The paper presents a visual notation for the Web Ontology Language (OWL) providing an integrated view on the classes and individuals of ontologies. The classes are displayed as circles, with the size of each circle representing its connectivity in the ontology. The individuals are represented as sections in the circles so that it is immediately clear from the visualization how many individuals the classes contain. The notation can be used to visualize the property relations of either the classes (conceptual layer) or of selected individuals (integrated layer), while certain elements are always shown for a better understanding. It requires only a small number of graphical elements and the resulting visualizations are comparatively compact. Yet, the notation is comprehensive, as it defines graphical representations for all OWL elements that can be reasonably visualized. The applicability of the notation is illustrated by the example of the Friend of a Friend (FOAF) ontology. 1 INTRODUCTION reasoning. Even though it provides great means to de- scribe and integrate information on the Web, its text- The Web of Data has attracted large interest for both based representation is difficult to understand for av- data publishers and consumers in the last years. -
Mapping Between Digital Identity Ontologies Through SISM
Mapping between Digital Identity Ontologies through SISM Matthew Rowe The OAK Group, Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, UK [email protected] Abstract. Various ontologies are available defining the semantics of dig- ital identity information. Due to the rise in use of lowercase semantics, such ontologies are now used to add metadata to digital identity informa- tion within web pages. However concepts exist in these ontologies which are related and must be mapped together in order to enhance machine- readability of identity information on the web. This paper presents the Social identity Schema Mapping (SISM) vocabulary which contains a set of mappings between related concepts in distinct digital identity ontolo- gies using OWL and SKOS mapping constructs. Key words: Semantic Web, Social Web, SKOS, OWL, FOAF, SIOC, PIMO, NCO, Microformats 1 Introduction The semantic web provides a web of machine-readable data. Ontologies form a vital component of the semantic web by providing conceptualisations of domains of knowledge which can then be used to provide a common understanding of some domain. A basic ontology contains a vocabulary of concepts and definitions of the relationships between those concepts. An agent reading a concept from an ontology can look up the concept and discover its properties and characteristics, therefore interpreting how it fits into that particular domain. Due to the great number of ontologies it is common for related concepts to be defined in separate ontologies, these concepts must be identified and mapped together. Web technologies such as Microformats, eRDF and RDFa have allowed web developers to encode lowercase semantics within XHTML pages. -
Enhancing JSON to RDF Data Conversion with Entity Type Recognition
Enhancing JSON to RDF Data Conversion with Entity Type Recognition Fellipe Freire, Crishane Freire and Damires Souza Academic Unit of Informatics, Federal Institute of Education, Science and Technology of Paraiba, João Pessoa, Brazil Keywords: RDF Data Conversion, Entity Type Recognition, Semi-Structured Data, JSON Documents, Semantics Usage. Abstract: Nowadays, many Web data sources and APIs make their data available on the Web in semi-structured formats such as JSON. However, JSON data cannot be directly used in the Web of data, where principles such as URIs and semantically named links are essential. Thus it is necessary to convert JSON data into RDF data. To this end, we have to consider semantics in order to provide data reference according to domain vocabularies. To help matters, we present an approach which identifies JSON metadata, aligns them with domain vocabulary terms and converts data into RDF. In addition, along with the data conversion process, we provide the identification of the semantically most appropriate entity types to the JSON objects. We present the definitions underlying our approach and results obtained with the evaluation. 1 INTRODUCTION principles of this work is using semantics provided by the knowledge domain of the data to enhance The Web has evolved into an interactive information their conversion to RDF data. To this end, we use network, allowing users and applications to share recommended open vocabularies which are data on a massive scale. To help matters, the Linked composed by a set of terms, i.e., classes and Data principles define a set of practices for properties useful to describe specific types of things publishing structured data on the Web aiming to (LOV Documentation, 2016). -
Linked Data Schemata: Fixing Unsound Foundations
Linked data schemata: fixing unsound foundations. Kevin Feeney, Gavin Mendel Gleason, Rob Brennan Knowledge and Data Engineering Group & ADAPT Centre, School of Computer Science & Statistics, Trinity College Dublin, Ireland Abstract. This paper describes an analysis, and the tools and methods used to produce it, of the practical and logical implications of unifying common linked data vocabularies into a single logical model. In order to support any type of reasoning or even just simple type-checking, the vocabularies that are referenced by linked data statements need to be unified into a complete model wherever they reference or reuse terms that have been defined in other linked data vocabularies. Strong interdependencies between vocabularies are common and a large number of logical and practical problems make this unification inconsistent and messy. However, the situation is far from hopeless. We identify a minimal set of necessary fixes that can be carried out to make a large number of widely-deployed vocabularies mutually compatible, and a set of wider-ranging recommendations for linked data ontology design best practice to help alleviate the problem in future. Finally we make some suggestions for improving OWL’s support for distributed authoring and ontology reuse in the wild. Keywords: Linked Data, Reasoning, Data Quality 1. Introduction One of the central tenets of the Linked Data movement is the reuse of terms from existing well- known vocabularies [Bizer09] when developing new schemata or datasets. The semantic web infrastructure, and the RDF, RDFS and OWL languages, support this with their inherently distributed and modular nature. In practice, through vocabulary reuse, linked data schemata adopt knowledge models that are based on multiple, independently devised ontologies that often exhibit varying definitional semantics [Hogan12]. -
Building Linked Data for Both Humans and Machines∗
Building Linked Data For Both Humans and Machines∗ y z x Wolfgang Halb Yves Raimond Michael Hausenblas Institute of Information Centre for Digital Music Institute of Information Systems & Information London, UK Systems & Information Management Management Graz, Austria Graz, Austria ABSTRACT Existing linked datasets such as [3] are slanted towards ma- In this paper we describe our experience with building the chines as the consumer. Although there are exceptions to riese dataset, an interlinked, RDF-based version of the Eu- this machine-first approach (cf. [13]), we strongly believe rostat data, containing statistical data about the European that satisfying both humans and machines from a single Union. The riese dataset (http://riese.joanneum.at), aims source is a necessary path to follow. at serving roughly 3 billion RDF triples, along with mil- lions of high-quality interlinks. Our contribution is twofold: We subscribe to the view that every LOD dataset can be Firstly, we suggest using RDFa as the main deployment understood as a Semantic Web application. Every Semantic mechanism, hence serving both humans and machines to Web application in turn is a Web application in the sense effectively and efficiently explore and use the dataset. Sec- that it should support a certain task for a human user. With- ondly, we introduce a new way of enriching the dataset out offering a state-of-the-art Web user interface, potential with high-quality links: the User Contributed Interlinking, end-users are scared away. Hence a Semantic Web applica- a Wiki-style way of adding semantic links to data pages. tion needs to have a nice outfit, as well. -
Microformats the Next (Small) Thing on the Semantic Web?
Standards Editor: Jim Whitehead • [email protected] Microformats The Next (Small) Thing on the Semantic Web? Rohit Khare • CommerceNet “Designed for humans first and machines second, microformats are a set of simple, open data formats built upon existing and widely adopted standards.” — Microformats.org hen we speak of the “evolution of the is precisely encoding the great variety of person- Web,” it might actually be more appropri- al, professional, and genealogical relationships W ate to speak of “intelligent design” — we between people and organizations. By contrast, can actually point to a living, breathing, and an accidental challenge is that any blogger with actively involved Creator of the Web. We can even some knowledge of HTML can add microformat consult Tim Berners-Lee’s stated goals for the markup to a text-input form, but uploading an “promised land,” dubbed the Semantic Web. Few external file dedicated to machine-readable use presume we could reach those objectives by ran- remains forbiddingly complex with most blog- domly hacking existing Web standards and hop- ging tools. ing that “natural selection” by authors, software So, although any intelligent designer ought to developers, and readers would ensure powerful be able to rely on the long-established facility of enough abstractions for it. file transfer to publish the “right” model of a social Indeed, the elegant and painstakingly inter- network, the path of least resistance might favor locked edifice of technologies, including RDF, adding one of a handful of fixed tags to an exist- XML, and query languages is now growing pow- ing indirect form — the “blogroll” of hyperlinks to erful enough to attack massive information chal- other people’s sites. -
Linked Data Schemata: Fixing Unsound Foundations
Linked data schemata: fixing unsound foundations Editor(s): Amrapali Zaveri, Stanford University, USA; Dimitris Kontokostas, Universität Leipzig, Germany; Sebastian Hellmann, Universität Leipzig, Germany; Jürgen Umbrich, Wirtschaftsuniversität Wien, Austria Solicited review(s): Mathieu d’Aquin, The Open University, UK; Peter F. Patel-Schneider, Nuance Communications, USA; John McCrae, Insight Centre for Data Analytics, Ireland; One anonymous reviewer Kevin Chekov Feeney, Gavin Mendel Gleason, and Rob Brennan Knowledge and Data Engineering Group & ADAPT Centre, School of Computer Science & Statistics, Trinity College Dublin, Ireland Corresponding author’s e-mail: [email protected] Abstract. This paper describes our tools and method for an evaluation of the practical and logical implications of combining common linked data vocabularies into a single local logical model for the purpose of reasoning or performing quality evalua- tions. These vocabularies need to be unified to form a combined model because they reference or reuse terms from other linked data vocabularies and thus the definitions of those terms must be imported. We found that strong interdependencies between vocabularies are common and that a significant number of logical and practical problems make this model unification inconsistent. In addition to identifying problems, this paper suggests a set of recommendations for linked data ontology design best practice. Finally we make some suggestions for improving OWL’s support for distributed authoring and ontology reuse. Keywords: Linked Data, Reasoning, Data Quality 1. Introduction In order to validate any dataset which uses the adms:Asset term we must combine the adms ontolo- One of the central tenets of the linked data move- gy and the dcat ontology in order to ensure that ment is the reuse of terms from existing well-known dcat:Dataset is a valid class. -
From FOAF to English: Linguistic Contribution to Web Semantics
From FOAF to English: Linguistic Contribution to Web Semantics Max Silberztein Université de Franche-Comté [email protected] Abstract which sentence to produce. This article presents the linguistic component of such a system. This paper presents a linguistic module capable of generating a set of English sentences that 2 The linguistic framework correspond to a Resource Description Framework (RDF) statement; I discuss how a Based on the principles of linguistic approaches to generator can control the linguistic module, as generation laid out by Danlos (1987), I have used well as the various limitations of a pure the NooJ1 platform to construct a set of linguistic linguistic framework. resources that parses a sequence of RDF statements and produces a corresponding set of English 1 Introduction sentences. NooJ allows linguists to construct structured sets of linguistic resources (“modules”) Automatic Natural Language Generation Software in the form of dictionaries and grammars2 to aims to express information stored in a knowledge formalize a large gamut of linguistic phenomena: database as a Natural Language text. The orthography and spelling, inflectional, derivational information at the source typically consists of a and agglutinative morphology, local and structural series of simple atomic statements, such as “John syntax, transformational syntax, lexical and owns a house”, “Mary lives in Manchester”, “Peter predicative semantics. All linguistic analyses are is Ann’s cousin”. These statements are usually performed sequentially by adding and/or removing stored in a knowledge database or ontology in a linguistic annotations to/from a Text Annotation formal notation such as Prolog (e.g. Structure (TAS); at each level of the analysis, each “Own(John,House)”) or XML.