The Social Semantic Web
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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, -
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. -
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. -
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. -
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 -
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. -
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 . -
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. -
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. -
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]. -
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.