DOCSLIB.ORG

The Social Semantic Web

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Social Semantic Web The Social Semantic Web Bearbeitet von John Breslin, Alexandre Passant, Stefan Decker 1. Auflage 2009. Buch. IX, 300 S. Hardcover ISBN 978 3 642 01171 9 Format (B x L): 15,5 x 23,5 cm Gewicht: 1360 g Weitere Fachgebiete > EDV, Informatik > Informatik > Natürliche Sprachen & Maschinelle Übersetzung schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft. Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, eBooks, etc.) aller Verlage. Ergänzt wird das Programm durch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehr als 8 Millionen Produkte. 2 Motivation for applying Semantic Web technologies to the Social Web Many will have become familiar with popular Social Web applications such as blogging, social networks and wikis, and will be aware that we are heading towards an interconnected information space (through the blogosphere, in- ter-wiki links, mashups, etc.). At the same time, these applications are experi- encing boundaries in terms of information integration, dissemination, reuse, portability, searchability, automation and more demanding tasks like query- ing. The Semantic Web is increasingly aiming at these applications areas - quite a number of Semantic Web approaches have appeared in recent years to overcome the boundaries in these application areas, e.g. semantic wikis (Semantic MediaWiki), knowledge networking (Twine), embedded microcon- tent detection and reuse (Operator, Headup, Semantic Radar), social graph and data portability APIs (from Google and Facebook), etc. In an effort to consolidate and combine knowledge about existing efforts, we aim to educate readers about Social Web application areas and new avenues open to com- mercial exploitation in the Semantic Web. We shall give an overview of how the Social Web and Semantic Web can be meshed together. 2.1 Web 2.0 and the Social Web One of the most visible trends on the Web is the emergence of the Web 2.0 tech- nology platform. The term Web 2.0 refers to a perceived second-generation of Web-based communities and hosted services. Although the term suggests a new version of the Web, it does not refer to an update of the World Wide Web techni- cal specifications, but rather to new structures and abstractions that have emerged on top of the ordinary Web. While it is difficult to define the exact boundaries of what structures or abstractions belong to Web 2.0, there seems to be an agreement that services and technologies like blogs, wikis, folksonomies, podcasts, RSS feeds (and other forms of many-to-many publishing), social software and social networking sites, web APIs, web standards1 and online web services are part of Web 2.0. Web 2.0 has not only been a technological but also a business trend: ac- cording to Tim O’Reilly2: ‘Web 2.0 is the business revolution in the computer in- 1 http://www.webstandards.org/ (URL last accessed 2009-06-09) 2 http://radar.oreilly.com/archives/2006/12/web-20-compact.html (accessed 2009-06-09) J.G. Breslin et al., The Social Semantic Web, DOI 10.1007/978-3-642-01172-6_2, © Springer-Verlag Berlin Heidelberg 2009 12 The Social Semantic Web dustry caused by the move to the Internet as platform, and an attempt to under- stand the rules for success on that new platform’. Social networking sites such as Facebook (one of the world’s most popular SNSs), Friendster (an early SNS previously popular in the US, now widely used in Asia), orkut (Google’s SNS), LinkedIn (an SNS for professional relationships) and MySpace (a music and youth-oriented service) - where explicitly-stated networks of friendship form a core part of the website - have become part of the daily lives of millions of users, and have generated huge amounts of investment since they began to appear around 2002. Since then, the popularity of these sites has grown hugely and continues to do so. (Boyd and Ellison 2007) have described the history of social networking sites, and suggested that in the early days of SNSs, when only the SixDegrees service existed, there simply were not enough users: ‘While people were already flocking to the Internet, most did not have extended networks of friends who were online’. A graph from Internet World Stats3 shows the growth in the number of Internet users over time. Between 2000 (when SixDegrees shut down) and 2003 (when Friendster became the first successful SNS), the number of Internet users had doubled. Web 2.0 content-sharing sites with social networking functionality such as YouTube (a video-sharing site), Flickr (for sharing images) and Last.fm (a music community site) have enjoyed similar popularity. The basic features of a social networking site are profiles, friend listings and commenting, often along with other features such as private messaging, discussion forums, blogging, and media uploading and sharing. In addition to SNSs, other forms of social websites include wikis, forums and blogs. Some of these publish content in structured formats ena- bling them to be aggregated together. A common property of Web 2.0 technologies is that they facilitate collabora- tion and sharing between users with low technical barriers – although usually on single sites (e.g. Technorati) or with a limited range of information (e.g. RSS, which we will describe later). In this book we will refer to this collaborative and sharing aspect as the ‘Social Web’, a term that can be used to describe a subset of Web interactions that are highly social, conversational and participatory. The So- cial Web may also be used instead of Web 2.0 as it is clearer what feature of the Web is being referred to4. The Social Web has applications on intranets as well as on the Internet. On the Internet, the Social Web enables participation through the simplification of user contributions via blogs and tagging, and has unleashed the power of community- based knowledge acquisition with efforts like Wikipedia demonstrating the collec- tive ‘wisdom of the crowds’ in creating the largest encyclopaedia. One outcome of such websites, especially wikis, is that they can produce more valuable knowledge collectively rather than that created by separated individuals. In this sense, the So- cial Web can be seen as a way to create collective intelligence at a Web-scale 3 http://www.internetworldstats.com/emarketing.htm (URL last accessed 2009-06-09) 4 http://en.wikipedia.org/wiki/Social_web (URL last accessed 2009-06-09) 2 Motivation for applying Semantic Web technologies to the Social Web 13 level, following the ‘we are smarter than me’ principles5 (Libert and Spector 2008). Similar technologies are also being used in company intranets as effective knowledge management, collaboration and communication tools between employ- ees. Companies are also aiming to make social website users part of their IT ‘team’, e.g. by allowing users to have access to some of their data and by bringing the results into their business processes (Tapscott and Williams 2007). 2.2 Addressing limitations in the Social Web with semantics A limitation of current social websites is that they are isolated from one another like islands in the sea (Figure 2.1). For example, different online discussions may contain complementary knowledge and topics, segmented parts of an answer that a person may be looking for, but people participating in one discussion do not have ready access to information about related discussions elsewhere. As more and more social websites, communities and services come online, the lack of interop- erability between them becomes obvious. The Social Web creates a set of single data silos or ‘stovepipes’, i.e. there are many sites, communities and services that cannot interoperate with each other, where synergies are expensive to exploit, and where reuse and interlinking of data is difficult and cumbersome. The main reason for this lack of interoperation is that for most Social Web ap- plications, communities, and domains, there are still no common standards for knowledge and information exchange or interoperation available. RSS (Really Simple Syndication), a format for publishing recently-updated Web content such as blog entries, was the first step towards interoperability among social websites, but it has various limitations that make it difficult to be used efficiently in such an interoperability context, as we will see later. Another extension of the Web aims to provide the tools that are necessary to define extensible and flexible standards for information exchange and interopera- bility. The Scientific American article (Berners-Lee et al. 2001) from Berners-Lee, Hendler and Lassila defined the Semantic Web as ‘an extension of the current Web in which information is given well-defined meaning, better enabling com- puters and people to work in cooperation’. The last couple of years have seen large efforts going into the definition of the foundational standards supporting data interchange and interoperation, and currently a quite well-defined Semantic Web technology stack exists, enabling the creation of defining metadata and associated vocabularies. 5 http://www.wearesmarter.org/ (URL last accessed 2009-06-09) 14 The Social Semantic Web i ii iii iv Fig. 2.1. Creating bridges between isolated communities of users and their data6 A number of Semantic Web vocabularies have achieved wide deployment – successful examples include RSS 1.0 for the syndication of information, FOAF, for expressing personal profile and social networking information, and SIOC, for interlinking communities and distributed conversations. These vocabularies share a joint property: they are small, but at the same time vertical – i.e. they are a part of many different domains. Each horizontal domain (e.g. e-health) would typically reuse a number of these vertical vocabularies, and when deployed the vocabularies would be able to interact with each other.
Load more

Recommended publications
  • Semantic Integration and Knowledge Discovery for Environmental Research
    Journal of Database Management, 18(1), 43-67, January-March 2007 43 Semantic Integration and Knowledge Discovery for Environmental Research Zhiyuan Chen, University of Maryland, Baltimore County (UMBC), USA Aryya Gangopadhyay, University of Maryland, Baltimore County (UMBC), USA George Karabatis, University of Maryland, Baltimore County (UMBC), USA Michael McGuire, University of Maryland, Baltimore County (UMBC), USA Claire Welty, University of Maryland, Baltimore County (UMBC), USA ABSTRACT Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantics- based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution brows- ing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results. Keywords: environmental research, knowledge discovery and navigation, semantic integra- tion, semantic networks,
    [Show full text]
  • Semantic Integration Across Heterogeneous Databases Finding Data Correspondences Using Agglomerative Hierarchical Clustering and Artificial Neural Networks
    DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO Master in Computer Science Date: April 11, 2018 Supervisor: John Folkesson Examiner: Hedvig Kjellström Swedish title: Semantisk integrering mellan heterogena databaser: Hitta datakopplingar med hjälp av hierarkisk klustring och artificiella neuronnät School of Computer Science and Communication iii Abstract The process of data integration is an important part of the database field when it comes to database migrations and the merging of data. The research in the area has grown with the addition of machine learn- ing approaches in the last 20 years. Due to the complexity of the re- search field, no go-to solutions have appeared. Instead, a wide variety of ways of enhancing database migrations have emerged. This thesis examines how well a learning-based solution performs for the seman- tic integration problem in database migrations. Two algorithms are implemented. One that is based on informa- tion retrieval theory, with the goal of yielding a matching result that can be used as a benchmark for measuring the performance of the machine learning algorithm. The machine learning approach is based on grouping data with agglomerative hierarchical clustering and then training a neural network to recognize patterns in the data. This al- lows making predictions about potential data correspondences across two databases.
    [Show full text]
  • The Social Semantic Web John G
    The Social Semantic Web John G. Breslin · Alexandre Passant · Stefan Decker The Social Semantic Web 123 John G. Breslin Alexandre Passant Electrical and Electronic Engineering Digital Enterprise Research School of Engineering and Informatics Institute (DERI) National University of Ireland, Galway National University of Ireland, Galway Nuns Island IDA Business Park Galway Lower Dangan Ireland Galway [email protected] Ireland [email protected] Stefan Decker Digital Enterprise Research Institute (DERI) National University of Ireland, Galway IDA Business Park Lower Dangan Galway Ireland [email protected] ISBN 978-3-642-01171-9 e-ISBN 978-3-642-01172-6 DOI 10.1007/978-3-642-01172-6 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2009936149 ACM Computing Classification (1998): H.3.5, H.4.3, I.2, K.4 c Springer-Verlag Berlin Heidelberg 2009 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
    [Show full text]
  • The Complete Guide to Social Media from the Social Media Guys
    The Complete Guide to Social Media From The Social Media Guys PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Mon, 08 Nov 2010 19:01:07 UTC Contents Articles Social media 1 Social web 6 Social media measurement 8 Social media marketing 9 Social media optimization 11 Social network service 12 Digg 24 Facebook 33 LinkedIn 48 MySpace 52 Newsvine 70 Reddit 74 StumbleUpon 80 Twitter 84 YouTube 98 XING 112 References Article Sources and Contributors 115 Image Sources, Licenses and Contributors 123 Article Licenses License 125 Social media 1 Social media Social media are media for social interaction, using highly accessible and scalable publishing techniques. Social media uses web-based technologies to turn communication into interactive dialogues. Andreas Kaplan and Michael Haenlein define social media as "a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, which allows the creation and exchange of user-generated content."[1] Businesses also refer to social media as consumer-generated media (CGM). Social media utilization is believed to be a driving force in defining the current time period as the Attention Age. A common thread running through all definitions of social media is a blending of technology and social interaction for the co-creation of value. Distinction from industrial media People gain information, education, news, etc., by electronic media and print media. Social media are distinct from industrial or traditional media, such as newspapers, television, and film. They are relatively inexpensive and accessible to enable anyone (even private individuals) to publish or access information, compared to industrial media, which generally require significant resources to publish information.
    [Show full text]
  • L Dataspaces Make Data Ntegration Obsolete?
    DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles DBKDA 2011 Panel Discussion: Will Dataspaces Make Data Integration Obsolete? Moderator: Fritz Laux, Reutlingen Univ., Germany Panelists: Kazuko Takahashi, Kwansei Gakuin Univ., Japan Lena Strömbäck, Linköping Univ., Sweden Nipun Agarwal, Oracle Corp., USA Christopher Ireland, The Open Univ., UK Fritz Laux, Reutlingen Univ., Germany DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles The Dataspace Idea Space of Data Management Scalable Functionality and Costs far Web Search Functionality virtual Organization pay-as-you-go, Enterprise Dataspaces Admin. Portal Schema Proximity Federated first, DBMS DBMS scient. Desktop Repository Search DBMS schemaless, near unstructured high Semantic Integration low Time and Cost adopted from [Franklin, Halvey, Maier, 2005] DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Inclusive ● Deal with all the data of interest, in whatever form => but semantics matters ● We need access to the metadata! ● derive schema from instances? ● Discovering new data sources => The Münchhausen bootstrap problem? Theodor Hosemann (1807-1875) DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Co-existence
    [Show full text]
  • Learning to Match Ontologies on the Semantic Web
    The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologies on the Semantic Web AnHai Doan1, Jayant Madhavan2, Robin Dhamankar1, Pedro Domingos2, Alon Halevy2 1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA fanhai,[email protected] 2 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA fjayant,pedrod,[email protected] Received: date / Revised version: date Abstract On the Semantic Web, data will inevitably come and much of the potential of the Web has so far remained from many different ontologies, and information processing untapped. across ontologies is not possible without knowing the seman- In response, researchers have created the vision of the Se- tic mappings between them. Manually finding such mappings mantic Web [BLHL01], where data has structure and ontolo- is tedious, error-prone, and clearly not possible at the Web gies describe the semantics of the data. When data is marked scale. Hence, the development of tools to assist in the ontol- up using ontologies, softbots can better understand the se- ogy mapping process is crucial to the success of the Seman- mantics and therefore more intelligently locate and integrate tic Web. We describe GLUE, a system that employs machine data for a wide variety of tasks. The following example illus- learning techniques to find such mappings. Given two on- trates the vision of the Semantic Web. tologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well- founded probabilistic definitions to several practical similar- Example 1 Suppose you want to find out more about some- ity measures, and show that GLUE can work with all of them.
    [Show full text]
  • Semantic Integration in the IFF Gies , Etc
    ogies, composing ontologies via fusions, noting dependen- cies between ontologies, declaring the use of other ontolo- 3 Semantic Integration in the IFF gies , etc. The IFF takes a building blocks approach to- wards the development of object-level ontological struc- ture. This is a rather elaborate categorical approach, which Robert E. Kent uses insights and ideas from the theory of distributed logic Ontologos known as information flow (Barwise and Seligman, 1997) [email protected] and the theory of formal concept analysis (Ganter and Wille, 1999). The IFF represents metalogic, and as such operates at the structural level of ontologies. In the IFF, Abstract there is a precise boundary between the metalevel and the object level. The IEEE P1600.1 Standard Upper Ontology (SUO) The modular architecture of the IFF consists of metale- project aims to specify an upper ontology that will provide vels, namespaces and meta-ontologies. There are three me- a structure and a set of general concepts upon which do- talevels: top, upper and lower. This partition, which cor- main ontologies could be constructed. The Information responds to the set-theoretic distinction between small Flow Framework (IFF), which is being developed under (sets), large (classes) and generic collections, is permanent. the auspices of the SUO Working Group, represents the Each metalevel services the level below by providing a structural aspect of the SUO. The IFF is based on category language that is used to declare and axiomatize that level. theory. Semantic integration of object-level ontologies in * The top metalevel services the upper metalevel, the upper the IFF is represented with its fusion construction .
    [Show full text]
  • Rule-Based Intelligence on the Semantic Web Implications for Military Capabilities
    UNCLASSIFIED Rule-Based Intelligence on the Semantic Web Implications for Military Capabilities Dr Paul Smart Senior Research Fellow School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom 26th November 2007 UNCLASSIFIED Report Documentation Page Report Title: Rule-Based Intelligence on the Semantic Web Report Subtitle Implications for Military Capabilities Project Title: N/A Number of Pages: 57 Version: 1.2 Date of Issue: 26/11/2007 Due Date: 22/11/2007 Performance EZ~01~01~17 Number of 95 Indicator: References: Reference Number: DT/Report/RuleIntel Report Availability: APPROVED FOR PUBLIC RELEASE; LIMITED DISTRIBUTION Abstract Availability: APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED Authors: Paul Smart Keywords: semantic web, ontologies, reasoning, decision support, rule languages, military Primary Author Details: Client Details: Dr Paul Smart Senior Research Fellow School of Electronics and Computer Science University of Southampton Southampton, UK SO17 1BJ tel: +44 (0)23 8059 6669 fax: +44 (0)23 8059 2783 email: [email protected] Abstract: Rules are a key element of the Semantic Web vision, promising to provide a foundation for reasoning capabilities that underpin the intelligent manipulation and exploitation of information content. Although ontologies provide the basis for some forms of reasoning, it is unlikely that ontologies, by themselves, will support the range of knowledge-based services that are likely to be required on the Semantic Web. As such, it is important to consider the contribution that rule-based systems can make to the realization of advanced machine intelligence on the Semantic Web. This report aims to review the current state-of-the-art with respect to semantic rule-based technologies.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Semantic Integration and Analysis of Clinical Data-1024
    Semantic integration and analysis of clinical data Hong Sun, Kristof Depraetere, Jos De Roo, Boris De Vloed, Giovanni Mels, Dirk Colaert Advanced Clinical Applications Research Group, Agfa HealthCare, Gent, Belgium {hong.sun, kristof.depraetere, jos.deroo, boris.devloed, giovanni.mels, dirk.colaert }@agfa.com Abstract. There is a growing need to semantically process and integrate clinical data from different sources for Clinical Data Management and Clinical Deci- sion Support in the healthcare IT industry. In the clinical practice domain, the semantic gap between clinical information systems and domain ontologies is quite often difficult to bridge in one step. In this paper, we report our experi- ence in using a two-step formalization approach to formalize clinical data, i.e. from database schemas to local formalisms and from local formalisms to do- main (unifying) formalisms. We use N3 rules to explicitly and formally state the mapping from local ontologies to domain ontologies. The resulting data ex- pressed in domain formalisms can be integrated and analyzed, though originat- ing from very distinct sources. Practices of applying the two-step approach in the infectious disorders and cancer domains are introduced. Keywords: Semantic interoperability, N3 rules, SPARQL, formal clinical data, data integration, data analysis, clinical information system. 1 Introduction A decade ago formal semantics, the study of logic within a linguistic framework, found a new means of expression, i.e. the World Wide Web and with it the Semantic Web [1]. The Semantic Web provides additional capabilities that enable information sharing between different resources which are semantically represented. It consists of a set of standards and technologies that include a simple data model for representing information (RDF) [2], a query language for RDF (SPARQL) [3], a schema language describing RDF vocabularies (RDFS) [4], a few syntaxes to represent RDF (RDF/XML [20], N3 [19]) and a language for describing and sharing ontologies (OWL) [5].
    [Show full text]
  • E-Learning 3.0 = E-Learning 2.0 + Web 3.0?
    IADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2012) E-LEARNING 3.0 = E-LEARNING 2.0 + WEB 3.0? Fehmida Hussain Middlesex University, Dubai ABSTRACT Web 3.0, termed as the semantic web or the web of data is the transformed version of Web 2.0 with technologies and functionalities such as intelligent collaborative filtering, cloud computing, big data, linked data, openness, interoperability and smart mobility. If Web 2.0 is about social networking and mass collaboration between the creator and user, then the Web 3.0 is referring to intelligent applications using natural language processing, machine-based learning and reasoning. From the perspective of advancements in e-Learning, the Web 2.0 technologies have transformed the classroom and converted a passive learner into an active participant in the learning process. This paper posits that the way both previous generations of e-Learning (1.0 and 2.0) have emerged with the prevalent technologies in their kin Web versions (1.0 and 2.0, respectively), it can be argued that e-Learning 3.0 will provide all earlier generations’ capabilities enhanced with the Web 3.0 technologies. Furthermore, in this paper, reviewing all the theories of learning and examining closely the theory of connectivism (considered to be the theory of learning for the digital age), it is argued that since most of the technologies that are to be a part of e-Learning 3.0 are addressed by these principles, a call for a new learning theory for e-Learning 3.0 is not justified.
    [Show full text]