Annotation of Existing Databases Using Semantic Web Technologies: Making Data More FAIR
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Ontotext Platform Documentation Release 3.4 Ontotext
Ontotext Platform Documentation Release 3.4 Ontotext Apr 16, 2021 CONTENTS 1 Overview 1 1.1 30,000ft ................................................ 2 1.2 Layered View ............................................ 2 1.2.1 Application Layer ...................................... 3 1.2.1.1 Ontotext Platform Workbench .......................... 3 1.2.1.2 GraphDB Workbench ............................... 4 1.2.2 Service Layer ........................................ 5 1.2.2.1 Semantic Objects (GraphQL) ........................... 5 1.2.2.2 GraphQL Federation (Apollo GraphQL Federation) ............... 5 1.2.2.3 Text Analytics Service ............................... 6 1.2.2.4 Annotation Service ................................ 7 1.2.3 Data Layer ......................................... 7 1.2.3.1 Graph Database (GraphDB) ........................... 7 1.2.3.2 Semantic Object Schema Storage (MongoDB) ................. 7 1.2.3.3 Semantic Objects for MongoDB ......................... 8 1.2.3.4 Semantic Object for Elasticsearch ........................ 8 1.2.4 Authentication and Authorization ............................. 8 1.2.4.1 FusionAuth ..................................... 8 1.2.4.2 Semantic Objects RBAC ............................. 9 1.2.5 Kubernetes ......................................... 9 1.2.5.1 Ingress and GW .................................. 9 1.2.6 Operation Layer ....................................... 10 1.2.6.1 Health Checking .................................. 10 1.2.6.2 Telegraf ....................................... 10 -
Ontologies and Semantic Web for the Internet of Things - a Survey
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/312113565 Ontologies and Semantic Web for the Internet of Things - a survey Conference Paper · October 2016 DOI: 10.1109/IECON.2016.7793744 CITATIONS READS 5 256 2 authors: Ioan Szilagyi Patrice Wira Université de Haute-Alsace Université de Haute-Alsace 10 PUBLICATIONS 17 CITATIONS 122 PUBLICATIONS 679 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Physics of Solar Cells and Systems View project Artificial intelligence for renewable power generation and management: Application to wind and photovoltaic systems View project All content following this page was uploaded by Patrice Wira on 08 January 2018. The user has requested enhancement of the downloaded file. Ontologies and Semantic Web for the Internet of Things – A Survey Ioan Szilagyi, Patrice Wira MIPS Laboratory, University of Haute-Alsace, Mulhouse, France {ioan.szilagyi; patrice.wira}@uha.fr Abstract—The reality of Internet of Things (IoT), with its one of the most important task in an IoT system [6]. Providing growing number of devices and their diversity is challenging interoperability among the things is “one of the most current approaches and technologies for a smarter integration of fundamental requirements to support object addressing, their data, applications and services. While the Web is seen as a tracking and discovery as well as information representation, convenient platform for integrating things, the Semantic Web can storage, and exchange” [4]. further improve its capacity to understand things’ data and facilitate their interoperability. In this paper we present an There is consensus that Semantic Technologies is the overview of some of the Semantic Web technologies used in IoT appropriate tool to address the diversity of Things [4], [7]–[9]. -
Data Platforms Map from 451 Research
1 2 3 4 5 6 Azure AgilData Cloudera Distribu2on HDInsight Metascale of Apache Kaa MapR Streams MapR Hortonworks Towards Teradata Listener Doopex Apache Spark Strao enterprise search Apache Solr Google Cloud Confluent/Apache Kaa Al2scale Qubole AWS IBM Azure DataTorrent/Apache Apex PipelineDB Dataproc BigInsights Apache Lucene Apache Samza EMR Data Lake IBM Analy2cs for Apache Spark Oracle Stream Explorer Teradata Cloud Databricks A Towards SRCH2 So\ware AG for Hadoop Oracle Big Data Cloud A E-discovery TIBCO StreamBase Cloudera Elas2csearch SQLStream Data Elas2c Found Apache S4 Apache Storm Rackspace Non-relaonal Oracle Big Data Appliance ObjectRocket for IBM InfoSphere Streams xPlenty Apache Hadoop HP IDOL Elas2csearch Google Azure Stream Analy2cs Data Ar2sans Apache Flink Azure Cloud EsgnDB/ zone Platforms Oracle Dataflow Endeca Server Search AWS Apache Apache IBM Ac2an Treasure Avio Kinesis LeanXcale Trafodion Splice Machine MammothDB Drill Presto Big SQL Vortex Data SciDB HPCC AsterixDB IBM InfoSphere Towards LucidWorks Starcounter SQLite Apache Teradata Map Data Explorer Firebird Apache Apache JethroData Pivotal HD/ Apache Cazena CitusDB SIEM Big Data Tajo Hive Impala Apache HAWQ Kudu Aster Loggly Ac2an Ingres Sumo Cloudera SAP Sybase ASE IBM PureData January 2016 Logic Search for Analy2cs/dashDB Logentries SAP Sybase SQL Anywhere Key: B TIBCO Splunk Maana Rela%onal zone B LogLogic EnterpriseDB SQream General purpose Postgres-XL Microso\ Ry\ X15 So\ware Oracle IBM SAP SQL Server Oracle Teradata Specialist analy2c PostgreSQL Exadata -
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. -
The Application of Semantic Web Technologies to Content Analysis in Sociology
THEAPPLICATIONOFSEMANTICWEBTECHNOLOGIESTO CONTENTANALYSISINSOCIOLOGY MASTER THESIS tabea tietz Matrikelnummer: 749153 Faculty of Economics and Social Science University of Potsdam Erstgutachter: Alexander Knoth, M.A. Zweitgutachter: Prof. Dr. rer. nat. Harald Sack Potsdam, August 2018 Tabea Tietz: The Application of Semantic Web Technologies to Content Analysis in Soci- ology, , © August 2018 ABSTRACT In sociology, texts are understood as social phenomena and provide means to an- alyze social reality. Throughout the years, a broad range of techniques evolved to perform such analysis, qualitative and quantitative approaches as well as com- pletely manual analyses and computer-assisted methods. The development of the World Wide Web and social media as well as technical developments like optical character recognition and automated speech recognition contributed to the enor- mous increase of text available for analysis. This also led sociologists to rely more on computer-assisted approaches for their text analysis and included statistical Natural Language Processing (NLP) techniques. A variety of techniques, tools and use cases developed, which lack an overall uniform way of standardizing these approaches. Furthermore, this problem is coupled with a lack of standards for reporting studies with regards to text analysis in sociology. Semantic Web and Linked Data provide a variety of standards to represent information and knowl- edge. Numerous applications make use of these standards, including possibilities to publish data and to perform Named Entity Linking, a specific branch of NLP. This thesis attempts to discuss the question to which extend the standards and tools provided by the Semantic Web and Linked Data community may support computer-assisted text analysis in sociology. First, these said tools and standards will be briefly introduced and then applied to the use case of constitutional texts of the Netherlands from 1884 to 2016. -
The Semantic Web: the Origins of Artificial Intelligence Redux
The Semantic Web: The Origins of Artificial Intelligence Redux Harry Halpin ICCS, School of Informatics University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW Scotland UK Fax:+44 (0) 131 650 458 E-mail:[email protected] Corresponding author is Harry Halpin. For further information please contact him. This is the tear-off page. To facilitate blind review. Title:The Semantic Web: The Origins of AI Redux working process managed to both halt the fragmentation of Submission for HPLMC-04 the Web and create accepted Web standards through its con- sensus process and its own research team. The W3C set three long-term goals for itself: universal access, Semantic Web, and a web of trust, and since its creation these three goals 1 Introduction have driven a large portion of development of the Web(W3C, 1999) The World Wide Web is considered by many to be the most significant computational phenomenon yet, although even by One comparable program is the Hilbert Program in mathe- the standards of computer science its development has been matics, which set out to prove all of mathematics follows chaotic. While the promise of artificial intelligence to give us from a finite system of axioms and that such an axiom system machines capable of genuine human-level intelligence seems is consistent(Hilbert, 1922). It was through both force of per- nearly as distant as it was during the heyday of the field, the sonality and merit as a mathematician that Hilbert was able ubiquity of the World Wide Web is unquestionable. If any- to set the research program and his challenge led many of the thing it is the Web, not artificial intelligence as traditionally greatest mathematical minds to work. -
Benchmarking RDF Query Engines: the LDBC Semantic Publishing Benchmark
Benchmarking RDF Query Engines: The LDBC Semantic Publishing Benchmark V. Kotsev1, N. Minadakis2, V. Papakonstantinou2, O. Erling3, I. Fundulaki2, and A. Kiryakov1 1 Ontotext, Bulgaria 2 Institute of Computer Science-FORTH, Greece 3 OpenLink Software, Netherlands Abstract. The Linked Data paradigm which is now the prominent en- abler for sharing huge volumes of data by means of Semantic Web tech- nologies, has created novel challenges for non-relational data manage- ment technologies such as RDF and graph database systems. Bench- marking, which is an important factor in the development of research on RDF and graph data management technologies, must address these challenges. In this paper we present the Semantic Publishing Benchmark (SPB) developed in the context of the Linked Data Benchmark Council (LDBC) EU project. It is based on the scenario of the BBC media or- ganisation which makes heavy use of Linked Data Technologies such as RDF and SPARQL. In SPB a large number of aggregation agents pro- vide the heavy query workload, while at the same time a steady stream of editorial agents execute a number of update operations. In this paper we describe the benchmark’s schema, data generator, workload and re- port the results of experiments conducted using SPB for the Virtuoso and GraphDB RDF engines. Keywords: RDF, Linked Data, Benchmarking, Graph Databases 1 Introduction Non-relational data management is emerging as a critical need in the era of a new data economy where heterogeneous, schema-less, and complexly structured data from a number of domains are published in RDF. In this new environment where the Linked Data paradigm is now the prominent enabler for sharing huge volumes of data, several data management challenges are present and which RDF and graph database technologies are called to tackle. -
Exploiting Semantic Web Knowledge Graphs in Data Mining
Exploiting Semantic Web Knowledge Graphs in Data Mining Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universit¨atMannheim presented by Petar Ristoski Mannheim, 2017 ii Dekan: Dr. Bernd Lübcke, Universität Mannheim Referent: Professor Dr. Heiko Paulheim, Universität Mannheim Korreferent: Professor Dr. Simone Paolo Ponzetto, Universität Mannheim Tag der mündlichen Prüfung: 15 Januar 2018 Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many in- formation systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations be- tween them, which can be utilized in various natural language processing, infor- mation retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. -
Semantic Web
Semantic Web Ing. Federico Chesani Corso di Fondamenti di Intelligenza Artificiale M Outline 1. Introduction a) The map of the Web (accordingly to Tim Berners-Lee) b) The current Web and its limits c) The Semantic Web idea 2. Semantic Information (a bird’s eye view) a) Semantic Models b) Ontologies c) Few examples 3. Semantic Web Tools a) Unique identifiers -URI b) XML c) RDF and SPARQL d) OWL 4. Semantic Web: where are we? a) Problems against the success of SW proposal b) Critics against SW c) Few considerations d) Few links to start with The Web Map (by Berners-Lee) ©Tim Berners-Lee, http://www.w3.org/2007/09/map/main.jpg About the content Knowledge Representation Semantic Web Web The Web 1.0 … • Information represented by means of: – Natural language – Images, multimedia, graphic rendering/aspect • Human Users easily exploit all this means for: – Deducting facts from partial information – Creating mental asociations (between the facts and, e.g., the images) – They use different communication channels at the same time (contemporary use of many primitive senses) The Web 1.0 … • The content is published on the web with the principal aim of being “human-readable” – Standard HTML is focused on how to represent the content – There is no notion of what is represented – Few tags (e.g. <title>) provide an implicit semantics but … • … their content is not structured • … their use is not really standardized The Web 1.0 … We can identify the title by means of its representation (<h1>, <b>) … … what if tomorrow the designer changes the format of the web pages? <h1> <!-- inizio TITOLO --> <B> Finanziaria, il voto slitta a domani<br> Al Senato va in scena l'assurdo </B> <!-- fine TITOLO --> </h1> The Web 1.0 … • Web pages contain also links to other pages, but .. -
Semantic Web: a Review of the Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA
Semantic Web: A Review Of The Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA ABSTRACT which would probably produce a rather different narrative of the We review two decades of Semantic Web research and applica- history and the current state of the art of the field. I therefore do tions, discuss relationships to some other disciplines, and current not strive to achieve the impossible task of presenting something challenges in the field. close to a consensus – such a thing seems still elusive. However I do point out here, and sometimes within the narrative, that there CCS CONCEPTS are a good number of alternative perspectives. The review is also necessarily very selective, because Semantic • Information systems → Graph-based database models; In- Web is a rich field of diverse research and applications, borrowing formation integration; Semantic web description languages; from many disciplines within or adjacent to computer science, Ontologies; • Computing methodologies → Description log- and a brief review like this one cannot possibly be exhaustive or ics; Ontology engineering. give due credit to all important individual contributions. I do hope KEYWORDS that I have captured what many would consider key areas of the Semantic Web field. For the reader interested in obtaining amore Semantic Web, ontology, knowledge graph, linked data detailed overview, I recommend perusing the major publication ACM Reference Format: outlets in the field: The Semantic Web journal,1 the Journal of Pascal Hitzler. 2020. Semantic Web: A Review Of The Field. In Proceedings Web Semantics,2 and the proceedings of the annual International of . ACM, New York, NY, USA, 7 pages. -
Semantic Web Technologies and Data Management
Semantic Web Technologies and Data Management Li Ma, Jing Mei, Yue Pan Krishna Kulkarni Achille Fokoue, Anand Ranganathan IBM China Research Laboratory IBM Software Group IBM Watson Research Center Bei Jing 100094, China San Jose, CA 95141-1003, USA New York 10598, USA Introduction The Semantic Web aims to build a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. It proposes to use RDF as a flexible data model and use ontology to represent data semantics. Currently, relational models and XML tree models are widely used to represent structured and semi-structured data. But they offer limited means to capture the semantics of data. An XML Schema defines a syntax-valid XML document and has no formal semantics, and an ER model can capture data semantics well but it is hard for end-users to use them when the ER model is transformed into a physical database model on which user queries are evaluated. RDFS and OWL ontologies can effectively capture data semantics and enable semantic query and matching, as well as efficient data integration. The following example illustrates the unique value of semantic web technologies for data management. Figure 1. An example of ontology based data management In Figure 1, we have two tables in a relational database. One stores some basic information of several companies, and another one describes shareholding relationship among these companies. Sometimes, users want to issue such a query “find Company EDOX’s all direct and indirect shareholders which are from Europe and are IT company”. Based on the data stored in the database, existing RDBMSes cannot represent and answer the above query. -
Interacting with Semantic Web Data Through an Automatic Information Architecture Josep Maria Brunetti Fernández
Nom/Logotip de la Universitat on s’ha llegit la tesi Interacting with Semantic Web Data through an Automatic Information Architecture Josep Maria Brunetti Fernández Dipòsit Legal: L.313-2014 http://hdl.handle.net/10803/131223 Interacting with semantic web data throught an automatic information architecture està subjecte a una llicència de Reconeixement-NoComercial-CompartirIgual 3.0 No adaptada de Creative Commons (c) 2013, Josep Maria Brunetti Fernández Universitat de Lleida Escola Polit`ecnicaSuperior Interacting with Semantic Web Data through an Automatic Information Architecture by Josep Maria Brunetti Fern´andez Thesis submitted to the University of Lleida in fulfillment of the requirements for the degree of Doctor in Computer Science Under supervision of PhD Roberto Garc´ıaGonz´alez Lleida, December 2013 Acknowledgments Voldria mostrar el meu agra¨ıment a totes aquelles persones que han col laborat d’una · manera o altra amb aquesta tesis. I despr´es d’escriure tantes p`agines en angl`es, voldria fer-ho en catal`a. Al cap i a la fi, si en aquesta mem`oria hi ha una petita part on puc expressar lliurement all`oque sento, ´es aqu´ı; i no tinc millor manera de fer-ho que en catal`a, perqu`e´es la meva llengua materna i ´es amb la que millor m’expresso. Tot i que alguns es capfiquin en canviar-li el nom o intentin reduir-ne el seu ´us, som moltes persones les que parlem en catal`a i seguirem fent-ho. Nom´es demanem que es respecti com qualsevol altra llengua. En primer lloc, el meu m´es sincer agra¨ıment al Roberto Garc´ıa, director d’aquesta tesis.