Semantic Web Tutorial Using N3
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A Hierarchical SVG Image Abstraction Layer for Medical Imaging
A Hierarchical SVG Image Abstraction Layer for Medical Imaging Edward Kim1, Xiaolei Huang1, Gang Tan1, L. Rodney Long2, Sameer Antani2 1Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA; 2Communications Engineering Branch, National Library of Medicine, Bethesda, MD ABSTRACT As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the “semantic gap”, a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible “layer” that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. -
Towards Ontology Based BPMN Implementation. Sophea Chhun, Néjib Moalla, Yacine Ouzrout
Towards ontology based BPMN Implementation. Sophea Chhun, Néjib Moalla, Yacine Ouzrout To cite this version: Sophea Chhun, Néjib Moalla, Yacine Ouzrout. Towards ontology based BPMN Implementation.. SKIMA, 6th Conference on Software Knowledge Information Management and Applications., Jan 2012, Chengdu, China. 8 p. hal-01551452 HAL Id: hal-01551452 https://hal.archives-ouvertes.fr/hal-01551452 Submitted on 6 Nov 2018 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. 1 Towards ontology based BPMN implementation CHHUN Sophea, MOALLA Néjib and OUZROUT Yacine University of Lumiere Lyon2, laboratory DISP, France Natural language is understandable by human and not machine. None technical persons can only use natural language to specify their business requirements. However, the current version of Business process management and notation (BPMN) tools do not allow business analysts to implement their business processes without having technical skills. BPMN tool is a tool that allows users to design and implement the business processes by connecting different business tasks and rules together. The tools do not provide automatic implementation of business tasks from users’ specifications in natural language (NL). Therefore, this research aims to propose a framework to automatically implement the business processes that are expressed in NL requirements. -
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 -
A State-Of-The-Art Survey on Semantic Web Mining
Intelligent Information Management, 2013, 5, 10-17 http://dx.doi.org/10.4236/iim.2013.51002 Published Online January 2013 (http://www.scirp.org/journal/iim) A State-of-the-Art Survey on Semantic Web Mining Qudamah K. Quboa, Mohamad Saraee School of Computing, Science, and Engineering, University of Salford, Salford, UK Email: [email protected], [email protected] Received November 9, 2012; revised December 9, 2012; accepted December 16, 2012 ABSTRACT The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Se- mantic Web Mining. The huge increase in the amount of Semantic Web data became a perfect target for many re- searchers to apply Data Mining techniques on it. This paper gives a detailed state-of-the-art survey of on-going research in this new area. It shows the positive effects of Semantic Web Mining, the obstacles faced by researchers and propose number of approaches to deal with the very complex and heterogeneous information and knowledge which are pro- duced by the technologies of Semantic Web. Keywords: Web Mining; Semantic Web; Data Mining; Semantic Web Mining 1. Introduction precisely answer and satisfy the web users’ requests [1,5, 6]. Semantic Web is a part of the second generation web Semantic Web Mining is an integration of two important (Web2.0) and its original idea derived from the vision scientific areas: Semantic Web and Data Mining [1]. Se- W3C’s director and the WWW founder, Sir Tim Berners- mantic Web is used to give a meaning to data, creating complex and heterogeneous data structure, while Data Lee. -
Binary RDF for Scalable Publishing, Exchanging and Consumption in the Web of Data
WWW 2012 – PhD Symposium April 16–20, 2012, Lyon, France Binary RDF for Scalable Publishing, Exchanging and Consumption in the Web of Data Javier D. Fernández 1;2 Supervised by: Miguel A. Martínez Prieto 1 and Claudio Gutierrez 2 1 Department of Computer Science, University of Valladolid (Spain) 2 Department of Computer Science, University of Chile (Chile) [email protected] ABSTRACT era and hence they share a document-centric view, providing The Web of Data is increasingly producing large RDF data- human-focused syntaxes, disregarding large data. sets from diverse fields of knowledge, pushing the Web to In a typical scenario within the current state-of-the-art, a data-to-data cloud. However, traditional RDF represen- efficient interchange of RDF data is limited, at most, to com- tations were inspired by a document-centric view, which pressing the verbose plain data with universal compression results in verbose/redundant data, costly to exchange and algorithms. The resultant file has no logical structure and post-process. This article discusses an ongoing doctoral the- there is no agreed way to efficiently publish such data, i.e., sis addressing efficient formats for publication, exchange and to make them (publicly) available for diverse purposes and consumption of RDF on a large scale. First, a binary serial- users. In addition, the data are hardly usable at the time of ization format for RDF, called HDT, is proposed. Then, we consumption; the consumer has to decompress the file and, focus on compressed rich-functional structures which take then, to use an appropriate external tool (e.g. -
Semantic Gadgets: Pervasive Computing Meets the Semantic Web
Semantic Gadgets: Pervasive Computing Meets the Semantic Web Ora Lassila Research Fellow Agent Technology Group, Nokia Research Center October 2002 1 © NOKIA 2002-10-01 - Ora Lassila Mobility Makes Things Different • Device location is a new dimension • more information about the user and the usage context available • new applications & services are possible • Devices are different • reduced capabilities: smaller screens, slow input devices, lower bandwidth, higher latency, worse reliability, … • trusted device: always with you & has access to your private data • Usage contexts and needs are different • awkward usage situations (e.g., in the car while driving) • specific needs (“surfing” unlikely) • you are always “on” (= connected) • Dilemma: • the Internet - by design - represents a departure from physical reality but mobility grounds services & users to the physical world 2 © NOKIA 2002-10-01 - Ora Lassila 1 Some Enablers of Mobile Internet • Access to services from handheld terminals • Dynamic synthesis of content • Context-sensitivity • location is one dimension of a “context”, but there are others • New Technologies • Artificial Intelligence • machine learning: automatic customization and adaptation • automated planning: autonomous operation • “Semantic Web” • intelligent synthesis of content from multiple sources (ad hoc & on demand) • explicit representation of semantics of data & services • Ubiquitous (aka Pervasive) Computing • (a paradigm shift in personal computing) 3 © NOKIA 2002-10-01 - Ora Lassila Semantic Web: Motivation -
Semantic Description of Web Services
Semantic Description of Web Services Thabet Slimani CS Department, Taif University, P.O.Box 888, 21974, KSA Abstract syntaxes) and in terms of the paradigms proposed for The tasks of semantic web service (discovery, selection, employing these in practice. composition, and execution) are supposed to enable seamless interoperation between systems, whereby human intervention is This paper is dedicated to provide an overview of these kept at a minimum. In the field of Web service description approaches, expressing their classification in terms of research, the exploitation of descriptions of services through commonalities and differences. It provides an semantics is a better support for the life-cycle of Web services. understanding of the technical foundation on which they The large number of developed ontologies, languages of are built. These techniques are classified from a range of representations, and integrated frameworks supporting the research areas including Top-down, Bottom-up and Restful discovery, composition and invocation of services is a good Approaches. indicator that research in the field of Semantic Web Services (SWS) has been considerably active. We provide in this paper a This paper does also provide some grounding that could detailed classification of the approaches and solutions, indicating help the reader perform a more detailed analysis of the their core characteristics and objectives required and provide different approaches which relies on the required indicators for the interested reader to follow up further insights objectives. We provide a little detailed comparison and details about these solutions and related software. between some approaches because this would require Keywords: SWS, SWS description, top-down approaches, addressing them from the perspective of some tasks bottom-up approaches, RESTful services. -
RDF Query Languages Need Support for Graph Properties
RDF Query Languages Need Support for Graph Properties Renzo Angles1, Claudio Gutierrez1, and Jonathan Hayes1,2 1 Dept. of Computer Science, Universidad de Chile 2 Dept. of Computer Science, Technische Universit¨at Darmstadt, Germany {rangles,cgutierr,jhayes}@dcc.uchile.cl Abstract. This short paper discusses the need to include into RDF query languages the ability to directly query graph properties from RDF data. We study the support that current RDF query languages give to these features, to conclude that they are currently not supported. We propose a set of basic graph properties that should be added to RDF query languages and provide evidence for this view. 1 Introduction One of the main features of the Resource Description Framework (RDF) is its ability to interconnect information resources, resulting in a graph-like structure for which connectivity is a central notion [GLMB98]. As we will argue, basic concepts of graph theory such as degree, path, and diameter play an important role for applications that involve RDF querying. Considering the fact that the data model influences the set of operations that should be provided by a query language [HBEV04], it follows the need for graph operations support in RDF query languages. For example, the query “all relatives of degree 1 of Alice”, submitted to a genealogy database, amounts to retrieving the nodes adjacent to a resource. The query “are suspects A and B related?”, submitted to a police database, asks for any path connecting these resources in the (RDF) graph that is stored in this database. The query “what is the Erd˝osnumber of Alberto Mendelzon”, submitted to (a RDF version of) DBLP, asks simply for the length of the shortest path between the nodes representing Erd˝osand Mendelzon. -
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. -
Exercise Sheet 1
Semantic Web, SS 2017 1 Exercise Sheet 1 RDF, RDFS and Linked Data Submit your solutions until Friday, 12.5.2017, 23h00 by uploading them to ILIAS. Later submissions won't be considered. Every solution should contain the name(s), email adress(es) and registration number(s) of its (co-)editor(s). Read the slides for more submission guidelines. 1 Knowledge Representation (4pts) 1.1 Knowledge Representation Humans usually express their knowledge in natural language. Why aren't we using, e.g., English for knowledge representation and the semantic web? 1.2 Terminological vs Concrete Knowledge What is the role of terminological knowledge (e.g. Every company is an organization.), and what is the role of facts (e.g. Microsoft is an organization. Microsoft is headquartered in Redmond.) in a semantic web system? Hint: Imagine an RDF ontology that contains either only facts (describing factual knowledge: states of aairs) or constraints (describing terminological knowledge: relations between con- cepts). What could a semantic web system do with it? Semantic Web, SS 2017 2 2 RDF (10pts) RDF graphs consist of triples having a subject, a predicate and an object. Dierent syntactic notations can be used in order to serialize RDF graphs. By now you have seen the XML and the Turtle syntax of RDF. In this task we will use the Notation3 (N3) format described at http://www.w3.org/2000/10/swap/Primer. Look at the following N3 le: @prefix model: <http://example.com/model1/> . @prefix cdk: <http://example.com/chemistrydevelopmentkit/> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . -
Browsing the Semantic Web
Browsing the Semantic Web Ora Lassila Nokia Research Center Cambridge Cambridge, MA 02142, USA Abstract lution. Enabling browsing is further encouraged by the fact that, despite its simple data model, users and developers are Presenting RDF graphs to the end user as hypertext enables often put off by RDF’s cryptic XML-based syntax. interactive exploration and inquiry of “semantic” data. In Many tools have been constructed for searching, ex- this paper we present a tool that allows users to “see” their ploring and querying complex information spaces such as RDF data, without having to decipher syntactic represen- semistructured data representations or collections with rich tations that are typically not human-friendly. The tool is metadata. These tools often offer a mixture of features such part of a broader architecture designed for viewing many as faceted search, clustering of search results, etc. Exam- different types of data in Semantic Web formalisms, for ples of such systems include Lore [7, 8], Flamenco [26], experimenting with the caching of dynamically changing and others. More specifically, recent years have also seen a data, and for exploring the possible ad hoc integration of number of “Semantic Web browsers” emerge; examples of data from multiple sources. Additionally, this tool enables this type of software include mSpace [16], Haystack [18], browsing of semantic data on a mobile device, by adapting Magnet [23], DBin [25], semantExplorer [21], and others. the hypertext generation to better suit small screens. These systems allow the browsing of Semantic Web data and provide various ways of searching and visualizing this data. Many of them combine semantic data with “classi- 1. -
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.