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A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage
heritage Article A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage Ikrom Nishanbaev 1,* , Erik Champion 1,2,3 and David A. McMeekin 4,5 1 School of Media, Creative Arts, and Social Inquiry, Curtin University, Perth, WA 6845, Australia; [email protected] 2 Honorary Research Professor, CDHR, Sir Roland Wilson Building, 120 McCoy Circuit, Acton 2601, Australia 3 Honorary Research Fellow, School of Social Sciences, FABLE, University of Western Australia, 35 Stirling Highway, Perth, WA 6907, Australia 4 School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia; [email protected] 5 School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia * Correspondence: [email protected] Received: 14 July 2020; Accepted: 4 August 2020; Published: 12 August 2020 Abstract: Recently, many Resource Description Framework (RDF) data generation tools have been developed to convert geospatial and non-geospatial data into RDF data. Furthermore, there are several interlinking frameworks that find semantically equivalent geospatial resources in related RDF data sources. However, many existing Linked Open Data sources are currently sparsely interlinked. Also, many RDF generation and interlinking frameworks require a solid knowledge of Semantic Web and Geospatial Semantic Web concepts to successfully deploy them. This article comparatively evaluates features and functionality of the current state-of-the-art geospatial RDF generation tools and interlinking frameworks. This evaluation is specifically performed for cultural heritage researchers and professionals who have limited expertise in computer programming. Hence, a set of criteria has been defined to facilitate the selection of tools and frameworks. -
From the Semantic Web to Social Machines
ARTICLE IN PRESS ARTINT:2455 JID:ARTINT AID:2455 /REV [m3G; v 1.23; Prn:25/11/2009; 12:36] P.1 (1-6) Artificial Intelligence ••• (••••) •••–••• Contents lists available at ScienceDirect Artificial Intelligence www.elsevier.com/locate/artint From the Semantic Web to social machines: A research challenge for AI on the World Wide Web ∗ Jim Hendler a, , Tim Berners-Lee b a Tetherless World Constellation, RPI, United States b Computer Science and AI Laboratory, MIT, United States article info abstract Article history: The advent of social computing on the Web has led to a new generation of Web Received 24 September 2009 applications that are powerful and world-changing. However, we argue that we are just Received in revised form 1 October 2009 at the beginning of this age of “social machines” and that their continued evolution and Accepted 1 October 2009 growth requires the cooperation of Web and AI researchers. In this paper, we show how Available online xxxx the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research. © 2009 Elsevier B.V. All rights reserved. Much has been written about the profound impact that the World Wide Web has had on society. Yet it is primarily in the past few years, as more interactive “read/write” technologies (e.g. Wikis, blogs and photo/video sharing) and social network- ing sites have proliferated, that the truly profound nature of this change is being felt. From the very beginning, however, the Web was designed to create a network of humans changing society empowered using this shared infrastructure. -
Description Logics Emerge from Ivory Towers Deborah L
Description Logics Emerge from Ivory Towers Deborah L. McGuinness Stanford University, Stanford, CA, 94305 [email protected] Abstract: Description logic (DL) has existed as a field for a few decades yet somewhat recently have appeared to transform from an area of academic interest to an area of broad interest. This paper provides a brief historical perspective of description logic developments that have impacted their usability beyond just in universities and research labs and provides one perspective on the topic. Description logics (previously called terminological logics and KL-ONE-like systems) started with a motivation of providing a formal foundation for semantic networks. The first implemented DL system – KL-ONE – grew out of Brachman’s thesis [Brachman, 1977]. This work was influenced by the work on frame systems but was focused on providing a foundation for building term meanings in a semantically meaningful and unambiguous manner. It rejected the notion of maintaining an ever growing (seemingly adhoc) vocabulary of link and node names seen in semantic networks and instead embraced the notion of a fixed set of domain-independent “epistemological primitives” that could be used to construct complex, structured object descriptions. It included constructs such as “defines-an-attribute-of” as a built-in construct and expected terms like “has-employee” to be higher-level terms built up from the epistemological primitives. Higher level terms such as “has-employee” and “has-part-time-employee” could be related automatically based on term definitions instead of requiring a user to place links between them. In its original incarnation, this led to maintaining the motivation of semantic networks of providing broad expressive capabilities (since people wanted to be able to represent natural language applications) coupled with the motivation of providing a foundation of building blocks that could be used in a principled and well-defined manner. -
Deciding SHACL Shape Containment Through Description Logics Reasoning
Deciding SHACL Shape Containment through Description Logics Reasoning Martin Leinberger1, Philipp Seifer2, Tjitze Rienstra1, Ralf Lämmel2, and Steffen Staab3;4 1 Inst. for Web Science and Technologies, University of Koblenz-Landau, Germany 2 The Software Languages Team, University of Koblenz-Landau, Germany 3 Institute for Parallel and Distributed Systems, University of Stuttgart, Germany 4 Web and Internet Science Research Group, University of Southampton, England Abstract. The Shapes Constraint Language (SHACL) allows for for- malizing constraints over RDF data graphs. A shape groups a set of constraints that may be fulfilled by nodes in the RDF graph. We investi- gate the problem of containment between SHACL shapes. One shape is contained in a second shape if every graph node meeting the constraints of the first shape also meets the constraints of the second. Todecide shape containment, we map SHACL shape graphs into description logic axioms such that shape containment can be answered by description logic reasoning. We identify several, increasingly tight syntactic restrictions of SHACL for which this approach becomes sound and complete. 1 Introduction RDF has been designed as a flexible, semi-structured data format. To ensure data quality and to allow for restricting its large flexibility in specific domains, the W3C has standardized the Shapes Constraint Language (SHACL)5. A set of SHACL shapes are represented in a shape graph. A shape graph represents constraints that only a subset of all possible RDF data graphs conform to. A SHACL processor may validate whether a given RDF data graph conforms to a given SHACL shape graph. A shape graph and a data graph that act as a running example are pre- sented in Fig. -
Trust on the World Wide Web: a Survey Full Text Available At
Full text available at: http://dx.doi.org/10.1561/0400000006 Trust on the World Wide Web: A Survey Full text available at: http://dx.doi.org/10.1561/0400000006 Trust on the World Wide Web: A Survey Jennifer Golbeck University of Maryland College Park MA 20742 USA [email protected] Boston – Delft Full text available at: http://dx.doi.org/10.1561/0400000006 Foundations and Trends R in Web Science Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 USA Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is J. Golbeck, Trust on the World Wide Web: A Survey, Foundation and Trends R in Web Science, vol 1, no 2, pp 131–197, 2006 ISBN: 978-1-60198-116-5 c 2008 J. Golbeck All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Cen- ter, Inc., 222 Rosewood Drive, Danvers, MA 01923. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by now Publishers Inc. for users registered with the Copyright Clearance Center (CCC). The ‘services’ for users can be found on the internet at: www.copyright.com For those organizations that have been granted a photocopy license, a separate system of payment has been arranged. -
Collective Intelligence in Citizen Science – a Study of Performers and Talkers
1 Collective Intelligence in Citizen Science – A Study of Performers and Talkers Ramine Tinati, Elena Simperl, Markus Luczak-Roesch, Max Van Kleek, Nigel Shadbolt, University of Southampton 1. INTRODUCTION Online citizen science can be seen as a form of collective intelligence Levy´ [1997] and Woolley et al. [2010] in which the wisdom of the crowd is applied to the Web to advance scientific knowledge [Prestop- nik and Crowston 2012] Thus far, online citizen science projects [Bonney et al. 2009] have applied millions of volunteers to solving problems in a wide array of scientific domains, ranging from the clas- sification of galaxies [Fortson et al. 2011] to the completion of protein folding networks [Khatib et al. 2011]. Central to many of these projects are online messaging or discussion facilities designed to allow volunteers to ask one another questions and advice. Such facilities have in many cases yielded sub- stantial, dedicated self-sustaining online communities. In this paper, we examine participation in such communities; specifically, whether participation in online discussion influences task completion within and across 10 distinct projects of a shared citizen science platform, the Zooniverse1. Our study was conducted on a dataset from December 2009 to July 2013, in which 250; 000 users contributed over 50 million tasks and 650; 000 discussion posts. 2. RELATED WORK The hybrid nature of online citizen science, as part Web-based community and part crowdsourcing exercise, places at the intersection of several important research disciplines, including data analysis, social computing, and collective intelligence. In this section we will give a brief overview of key works in these areas, which have informed our analysis. -
Knowledge Representation Or Misrepresentation Kieron O'hara
Ontologies and Technologies: Knowledge Representation or Misrepresentation Kieron O’Hara Intelligence, Agents, Multimedia Group School of Electronics and Computer Science University of Southampton Highfield Southampton SO17 1BJ United Kingdom [email protected] The development of the Semantic Web (SW) raises a number of difficult and interesting technical issues. Less often remarked, however, are the social and political debates that it will engender, if and when the technologies become widely accepted. As the SW is a technology for transferring information and knowledge efficiently and effectively, then many of these questions have an epistemological base. In this paper I want to focus especially on the epistemological underpinnings of these social issues, to think about the interaction between the epistemological and the political. How does technology affect the social networks in which it is embedded? How can technology be successfully transplanted into a new context? And, perhaps most importantly for us, how is technology affected by its context? In particular, I want to look at how our decisions about how we treat knowledge can impact quite dramatically on the technologies we produce. Let us begin with a familiar diagram, the layered view of the SW developed in the very early stages by Tim Berners-Lee and colleagues (Figure 1). Knowledge of different types is separated out (ontologies appearning in the middle). And by making this separation, we can see how the semanticity of the Semantic Web comes about. The technical machinery of Unicode and URIs appears at the bottom; XML can be used to tell the computer what objects this code is concerned with. -
The 8Th ACM Web Science Conference 2016
This is a repository copy of The 8th ACM Web Science Conference 2016. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/103102/ Version: Accepted Version Article: Jäschke, R. (2016) The 8th ACM Web Science Conference 2016. ACM SIGWEB Newsletter (Summer). pp. 1-7. ISSN 1931-1745 https://doi.org/10.1145/2956573.2956574 Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ pdfauthor The 8th ACM Web Science Conference 2016 Robert Jaschke¨ L3S Research Center Hannover, Germany This article provides an overview of this year’s ACM Web Science Conference (WebSci’16). It was located in Hannover, Germany, and organized by L3S Research Center and the Web Science Trust. WebSci’16 attracted more than 160 researchers from very different disciplines – ranging from computer science to anthropology. -
Awareness Watch™ Newsletter V16N2 February 2018
Awareness Watch™ Newsletter By Marcus P. Zillman, M.S., A.M.H.A. http://www.AwarenessWatch.com/ V16N2 February 2018 Welcome to the V16N2 February 2018 issue of the Awareness Watch™ Newsletter. This newsletter is available as a complimentary subscription and will be issued monthly. Each newsletter will feature the following: Awareness Watch™ Featured Report Awareness Watch™ Spotters Awareness Watch™ Book/Paper/Article Review Subject Tracer™ Information Blogs I am always open to feedback from readers so please feel free to email with all suggestions, reviews and new resources that you feel would be appropriate for inclusion in an upcoming issue of Awareness Watch™. This is an ongoing work of creativity and you will be observing constant changes, constant updates knowing that “change” is the only thing that will remain constant!! Awareness Watch™ Featured Report This month’s featured report covers my Deep Web Research and Discovery Resources 2018 and is a comprehensive listing of deep web resources including search engines, directories, subject guides and index resources and sites on the Internet. The below list of sources is taken from my Subject Tracer™ white paper titled Deep Web Research and Discovery Resources 2018 and is constantly updated with Subject Tracer™ bots at the following URLs: http://www.DeepWeb.us/ These resources and sources will help you to discover the many pathways available through the Internet to find the latest deep web resources and sites. As this site is constantly updated it would be to your benefit to bookmark and return to the above URL frequently. The true way to search the Internet and social media is to include the deep web and these resources will be your pathfinder to all the important and ever changing resources including the New Economy! 1 Awareness Watch V16N2 February 2018 Newsletter http://www.AwarenessWatch.com/ [email protected] eVoice: 800-858-1462 © 2018 Marcus P. -
Description Logics
Description Logics Franz Baader1, Ian Horrocks2, and Ulrike Sattler2 1 Institut f¨urTheoretische Informatik, TU Dresden, Germany [email protected] 2 Department of Computer Science, University of Manchester, UK {horrocks,sattler}@cs.man.ac.uk Summary. In this chapter, we explain what description logics are and why they make good ontology languages. In particular, we introduce the description logic SHIQ, which has formed the basis of several well-known ontology languages, in- cluding OWL. We argue that, without the last decade of basic research in description logics, this family of knowledge representation languages could not have played such an important rˆolein this context. Description logic reasoning can be used both during the design phase, in order to improve the quality of ontologies, and in the deployment phase, in order to exploit the rich structure of ontologies and ontology based information. We discuss the extensions to SHIQ that are required for languages such as OWL and, finally, we sketch how novel reasoning services can support building DL knowledge bases. 1 Introduction The aim of this section is to give a brief introduction to description logics, and to argue why they are well-suited as ontology languages. In the remainder of the chapter we will put some flesh on this skeleton by providing more technical details with respect to the theory of description logics, and their relationship to state of the art ontology languages. More detail on these and other matters related to description logics can be found in [6]. Ontologies There have been many attempts to define what constitutes an ontology, per- haps the best known (at least amongst computer scientists) being due to Gruber: “an ontology is an explicit specification of a conceptualisation” [47].3 In this context, a conceptualisation means an abstract model of some aspect of the world, taking the form of a definition of the properties of important 3 This was later elaborated to “a formal specification of a shared conceptualisation” [21]. -
The Web Observatory Extension: Facilitating Web Science Collaboration Through Semantic Markup
The Web Observatory Extension: Facilitating Web Science Collaboration through Semantic Markup Dominic DiFranzo John S. Erickson Marie Joan Kristine T. Gloria Tetherless World Constellation, Tetherless World Constellation, Tetherless World Constellation, Rensselaer Polytechnic Institute, Rensselaer Polytechnic Rensselaer Polytechnic Institute, Troy, NY USA Institute, Troy, NY USA Troy, NY USA [email protected] [email protected] [email protected] Joanne S. Luciano Deborah L. McGuinness James Hendler Tetherless World Constellation, Tetherless World Constellation, Tetherles World Constellation, Rensselaer Polytechnic Institute, Rensselaer Polytechnic Rensselaer Polytechnic Institute, Troy, NY USA Institute, Troy, NY USA Troy, NY USA [email protected] [email protected] [email protected] ABSTRACT Still in its infancy, the field of Web Science now faces several The multi-disciplinary nature of Web Science and the large size unique challenges from conflicting methodological philosophies and diversity of data collected and studied by its practitioners has to developing a suitable infrastructure for its scientists to inspired a new type of Web resource known as the Web collaborate and share resources. More importantly, the scale and Observatory. Web observatories are platforms that enable scope of the data produced by users of the Web and studied by researchers to collect, analyze and share data about the Web and Web Scientists is unprecedented for any modern field of study. to share tools for Web research. At the Boston Web Observatory Web Scientists are thus looking to mixed methodology practices, Workshop 2013 [3], a semantic model for describing Web new computational infrastructure and large scale analytics in order Observatories was drafted and an extension to the schema.org to better make sense of this complex phenomena. -
The Semantic Web Revisited
The Semantic Web Revisited Nigel Shadbolt Tim Berners-Lee Wendy Hall Today´sweb • It is designed for human consumption • Information retrieval is mainly supported by keyword-based search engines • Some problems with information retrieval: – High recall, low precision – Low or no recall – Results are highly sensitive to vocabulary – no integration of multiple sites – results restricted to single pages ⇒ Web content is not machine-proccessable and computers cannot understand and interpret the contents Semantic Web • An evolving extension of World Wide Web in which web content can be expressed not only in natural language, but also in a format that can be read and used by software agents, thus permitting them to find, share and integrate information more easily. • Proposed by W3C(World Wide Web Consortium) Chairman T.Berners Lee For example : Plan a trip to Boston Current Web Use search engine to list airlines. Check each airline for suitable flights with cheapest price, and decide the airline company. Make a reservation. Use search engine to list hotels at Boston. Check each hotel, decide the hotel and make a reservation. Print out the information about flight, and hotel. Semantic Web Ask to list available flights to Boston for any airline with low price. Pick one and it automatically reserve the flight. Then, it automatically search hotels at Boston which is convenient for the business. Pick one and it automatically reserve the hotel. All the information is put into your handheld device. It may also put restaurant phone numbers and other