DOCSLIB.ORG

[email protected] Peter

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Pdiviacco@Inogs.It Peter A new approach in cross-domain collaborative research Paolo Diviacco, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale , Trieste (ITALY), [email protected] Peter Fox, Rensselaer Polytechnic Institute, Troy , NY (USA), [email protected] Alessandro Busato, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale , Trieste (ITALY), [email protected] Develop new ideas <rdf:RDF Scientific collaborative work Is generally based xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://localhost/default#" Multiple domains / fields xmlns:owl="http://www.w3.org/2002/07/owl#" Gather researchers xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:daml="http://www.daml.org/2001/03/daml+oil#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"> Share data .... The single researchers does not filter measurement and vision <owl:Class rdf:about="http://localhost/default#TransformFault"> <rdfs:subClassOf> Traditional vision of Science Unbiased <owl:Class rdf:about="http://localhost/default#Fault"/> Share Knowledge </rdfs:subClassOf> </owl:Class> ... Objective Theory <owl:Class rdf:about="http://localhost/default#Fault"> Example 1 RDF converted to graph to link to a web resource Objectivity commonly is intended as something that does not depend on experience Reasoning <rdfs:subClassOf rdf:resource="http://localhost/default#Element"/> </owl:Class> .... Reproducible <Strike-Slip rdf:about="http://localhost/default#Gondola"> Quantitative <hasLocation>http://snap.ogs.trieste.it</hasLocation> </Strike-Slip> </rdf:RDF> Verification Induction-deduction loop Set experiment Cannot identify a posteriori factors Complex systems Difficulties in replicating experiments Factors intermingle Experiments Measurement Objectivity can be explained as "not just from someone's point of vieww Correlation does not mean causation Low number of cases Convergence of a community portrait observation Epistemic Objectivity Sets of claims or judgments which truth values are not determined privately Difficult to find analogies Subjectivity is private and objectivity is public Information and knowledge in different contexts will mean something different. How can we bridge the gaps between the different paradigms? Abduction: Where the resulting state of affairs and the law is known, while the controlling state of affairs is sought. It is good to explore a context which is Knowledge has to be explicit Knowledge is referenced trough labels FORMALIZATION REPRESENTATION uncertain in order to come up with new ideas, but Bridge conceptualization cannot produce definitive conclusions (Pierce 1932) Tool: RDF, Ontologies tools: Graphs, maps, models Abduction allow multiple points of view to co- exist Scientists live within paradigms: a theoretical or data DATA philosophical framework, a tradition or school that condition their way of thinking. (Kuhn, 1962) Metadata GRAPH Example 2: Using FOAF to link to web resources Scientific collaborative work Extension Group researchers Develop new ideas Share data Share Knowledge DATA <rdf:RDF Paradigms condition scientists life, xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" gathering them in groups that resemble xml:base="http://www.ogs.trieste.it/colla" xmlns:dc="http://purl.org/dc/elements/1.1/" tribes (Becher adn Trowler, 2001) xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" Intension xmlns:foaf="http://xmlns.com/foaf/0.1/"> <SeisLine> <dc:title>seismic line B-403</dc:title> <dc:subject rdf:resource="http://vocab.nerc.ac.uk/collection/GS8/current/SRFL/" /> <dc:identifier rdf:resource="http://snap.ogs.trieste.it/cache/octopus.jsp?q=B-403" /> <dc:creator> Representation is human-centered, software can <foaf:Person rdf:ID="diviak"> Carnap (1947) distinguished in a concept: <foaf:name>Paolo Diviacco</foaf:name> Intension: the list of its attributes (RDF only limitately be applied. <foaf:homepage rdf:resource="http://snap.ogs.trieste.it/" /> triplettes) <foaf:publications rdf:resource="https://www.researchgate.net/profile/P_Diviacco"/> <foaf:mbox rdf:resource="mailto:[email protected]"/> Extension: set of all individuals matching Graph nodes can store data and information such </foaf:Person> the intension (Sparql hits) as files and messaging, metadata is virtually not </dc:creator> <dc:publisher>OGS</dc:publisher> necessary. <dc:date>2016-03-12</dc:date> The model is based on the assumption that it </SeisLine> is the intension that determines the Nodes are Labels for concepts that can be </rdf:RDF> extension. understood differently, by the different communities. This is the idea behind the theory of "boundary objects" (Star and Griesemer, 1989). Only once we have defined the set of These are artifacts that are weakly structured in mailto:[email protected] Tribes tend to evolve separately (Speciation) common use while strongly structured in properties of a concept "A" can we realize if http://snap.ogs.trieste.it/cache/octopus.jsp?q=B-403 SeisLine developing different features, cognitive models and an entity is A individual use. rdf:type practices. Soon, these differences creates a form of https://www.researchgate.net/profile/P_Diviacco incommensurability that complicates understanding Putnam(1975) highlighted that a change in foaf:mbox foaf:publications between researchers. the intension (due for example to different Graphs can have certain flexibility but it is difficult dc11:identifier paradigm or context) will produce a different to change their structure later (merging is trivial They do not share the same meaning of the same extension while splitting nodes cannot handle automately dc11:creator Paolo Diviacco concept. For example there is a difference between the what is contained) seismic line B-403 http://snap.ogs.trieste.it/ meaning of the term layer in geology and geophysiscs, Tacit knowledge is knowledge that cannot be foaf:homepage while the two communities use it in a relaxed way. foaf:name made explicit because obscured or dc11:title embedded in practices or tools (Polanyi, rdf:type Conceptualizations then depend on the context dc11:subject 1966) "We know more than we can tell" Paolo Diviacco dc11:date seismic line B-403 We propose to use the two approaches at the same time dc11:publisher http://xmlns.com/foaf/0.1/Person http://vocab.nerc.ac.uk/collection/GS8/current/SRFL/ in order to take advantages of their positive aspects 2016-03-12 OGS Since scientific research is moved now from logic to the sociologic, becoming a "social contruct" (Latour and Woolgar, 1979) we need to consider communication Linked data 2011-01-28 12:26:26.0 Seismic reflection The elucidation of geological structure by quantifying the proportion of waves from a high energy source reflected by sub-surface layers. SeisRefl skos:prefLabel dc:date skos:altLabel skos:definition http://vocab.nerc.ac.uk/collection/GS8/current/SRFL/ rdf:type dc11:identifier skos:notation Graph nodes dc:identifier void:inDataset http://www.w3.org/2004/02/skos/core#Concept SDN:GS8::SRFL skos:note owl:deprecated http://vocab.nerc.ac.uk/.well-known/void owl:versionInfo Example 3: Using FOAF and dublin core to link to web resources; controlled vocabularies (NERC) are accepted false included and graphically rendered 1.
Load more

Recommended publications
  • AAAI/SSS 2008 Program - SSKI Detailed Program (Stanford University, History Building)
    AAAI/SSS 2008 Program - SSKI Detailed Program (Stanford University, History Building). Register at Cummings Hall. Wednesday, March 26 8:00 AM – 9:00 AM: Registration 8:45 AM – 9:00 AM: Semantic Scientific Knowledge Integration: Welcome Deborah L. McGuinness, Peter Fox, Boyan Brodaric 9:00 AM – 10:40 AM: Session I – Integration Foundations (moderator: Peter Fox) 9:00 AM – 9:30 AM: (Oral 17) Grounding the Foundations of Ontology Mapping on the Neglected Interoperability Ambition Hamid Haidarian Shahri, James Hendler, Donald Perlis 9:30 AM – 10:00 AM: (Oral 6) Ontological bridge building – using ontologies to merge spatial datasets Catherine Dolbear, Glen Hart 10:00 AM – 10:05 AM: (Poster 4 Intro) Integrating declarative knowledge: Issues, Algorithms and Future Work Doo Soon Kim, Bruce Porter 10:05 AM – 10:10 AM: (Poster 9 Intro) Improving Semantic Integration By Learning Semantic Interpretation Rules Michael Glass, Bruce Porter 10:10 AM – 10:15 AM: (Poster 16 Intro) Integrating Relational Databases with Semantic Web Ontologies: Reasoning and Query Answering using Views Linhua Zhou, Yuxin Mao. Huajun Chen 10:15 AM – 10:40 AM: Panel on Integration Foundations 10:40 AM – 11:00 AM: Break 11:00 AM – 12:30 PM: Session II - Upper Ontologies (Moderator: Deborah McGuinness) 11:00 AM – 11:30 AM: (Oral 27) DOLCE ROCKS: Integrating Geoscience Ontologies with DOLCE Boyan Brodaric, Florian Probst 11:30 AM – 12:00 PM: (Oral 23) Towards an Ontology for Data-driven Discovery of New Materials Kwok Cheung, John Drennan, Jane Hunter 12:00 PM – 12:30 PM: Panel on Use of Upper Ontologies for Integration 12:30 PM – 2:00 PM: Lunch 2:00 PM – 3:30 PM: Session III – Workflows and Service Composition (Moderator: Boyan Brodaric) 2:00 PM – 2:30 PM: Keynote – Scientific Workflow Design for Mere Mortals Bertram Ludaescher 2:30 PM – 2:35 PM: (Poster 11 Intro) Process-based Planning for Earth Science Data Acquisition Robert Morris, Jennifer Dungan, Petr Votava, Lina Khatib 2:35 PM – 2:40 PM: (Poster 12 Intro) Machine Learning for Earth Observation Flight Planning Optimization Elif Kurklu, Robert A.
    [Show full text]
  • 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.
    [Show full text]
  • The Role of Virtual Observatories and Data Frameworks in an Era of Big Data
    The Role of Virtual Observatories and Data Frameworks in an Era of Big Data NIST bIG dATA June 14, 2012, Gaithersburg, MD Peter Fox (RPI and WHOI) [email protected] , @taswegian, http://tw.rpi.edu/web/person/PeterFox Tetherless World Constellation http://tw.rpi.edu and AOP&E Virtual Observatories Working premise Scientists – actually ANYONE - should be able to access a global, distributed knowledge base of scientific data that: • appears to be integrated • appears to be locally available Data intensive – volume, complexity, mode, discipline, scale, heterogeneity2 .. Technical advances From: C. Borgman, 2008, NSF Cyberlearning Report Prior to 2005, we built systems • Rough definitions – Systems have very well-define entry and exit points. A user tends to know when they are using one. Options for extensions are limited and usually require engineering – Frameworks have many entry and use points. A user often does not know when they are using one. Extension points are part of the design – Modern platforms are built on frameworks Tetherless World Constellation 4 Early days of VxOs ? VO 2 VO3 VO1 DB DB2 DB3 n DB1 … … … … 5 The Astronomy approach; data-types as a service Limited interoperability VOTable VO App VO App2 3 VO App1 Simple Image Access Protocol Simple Spectrum VO layer Access Protocol Simple Lightweight semantics Time Limited meaning, DB Access DB2 DB3 hard coded n DB1 … … … … Protocol Limited extensibility 6 VO Standards • Creation – largely technical activity • Adoption – largely cultural activity Means of conduct of research* • Induction • Deduction Theory Theory Tentative hyp. Hypothesis Pattern Observation Confirmation Observation Fundamentally though We’ve built capabilities in VOs to support induction or deduction and sometimes both, but does this really enable the breadth of science discoveries we seek in the ERA of bIG dATA? Edges? In-betweens? Discipline mashups? Accidental? … For real discovery – we need abduction! - a method of logical inference introduced by C.
    [Show full text]
  • Data Life Cycle: Introduction, Definitions and Considerations
    Data Life Cycle: Introduction, Definitions and Considerations EUDAT, Sept. 25, 2014 Prof. Peter Fox ([email protected], @taswegian, #twcrpi) Tetherless World Constellation Chair, Earth and Environmental Science/ Computer Science/ Cognitive Science/ IT and Web Science) Rensselaer Polytechnic Institute, Troy, NY USA Life Cycle 2 Definitions • Data (management) life-cycle broad elements - – Acquisition: Process of recording or generating a concrete artefact from the concept (see transduction) – Curation: The activity of managing the use of data from its point of creation to ensure it is available for discovery and re-use in the future – Preservation: Process of retaining usability of data in some source form for intended and unintended use • Stewardship: Process of maintaining integrity for acquisition, curation, preservation • BUT… 3 Definitions ctd. • (Data) Management: Process of arranging for discovery, access and use of data, information and all related elements. Also oversees or effects control of processes for acquisition, curation, preservation and stewardship. Involves fiscal and intellectual responsibility. 4 5 Digital Curation Centre 6 MIT DDI Alliance Life Cycle 7 Data-Information-Knowledge Ecosystem Producers Consumers Experience Data Information Knowledge Creation Presentation Integration Gathering Organization Conversation Context 8 Data Life Cycle embedded in Research Life Cycle • Information Life Cycle • Knowledge Life Cycle Type of knowledge created • Tacit (created and stored informally): – Human memory – Localize, e.g.
    [Show full text]
  • The Earth System Grid: a Visualisation Solution
    The Earth System Grid: A Visualisation Solution Gary Strand Introduction Acknowledgments •PI’s •ESG Development Team –Veronika Nefedova (ANL) –Ian Foster (ANL) –Ann Chervenak (ISI/USC) –Don Middleton (NCAR) –Carl Kesselman (ISI/USC) –Dean Williams (LLNL) –David Bernholdt (ORNL) –Kasidit Chanchio (ORNL) –Line Pouchard (ORNL) –Alex Sim (LBNL) –Arie Shoshani (LBNL) –Bob Drach (LLNL) –Dave Brown (NCAR) –Gary Strand (NCAR) –Jose Garcia (NCAR) –Luca Cinquini (NCAR) –Peter Fox (NCAR) Current Practices oScientist (or others) wants a visualisation oVisualisation person gets appropriate data after verifying with data manager as to the name, location, total size, etc. oData moved to “local” machine that has visualisation tools oVisualization created on “local” machine oHopefully, someone remembers to archive the visualisation Simple Vis Example Problems in the process oWhat if the data cannot be found (e.g. we have 1.2 million files, 73 TB of data), or the data manager is unavailable? oWhat if there isn’t enough disk space or sufficient other resources? oWhat if a better visualisation tool is located elsewhere? oWhat if the visualisation should be shared? oWhat if the visualisation is lost? o ESG is part of the answers to these questions What is ESG? ANL: Computational grids, LBNL: Climate storage & grid-based applications facility LLNL: Model diagnostics & inter-comparison USC/ISI: Computational grids, & grid-based applications NCAR: Climate change LANL: Next generation ORNL: Climate storage & predication and scenarios coupled models & computing
    [Show full text]
  • Lecture Notes in Computer Science 6378 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan Van Leeuwen
    Lecture Notes in Computer Science 6378 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany Deborah L. McGuinness James R. Michaelis Luc Moreau (Eds.) Provenance and Annotation of Data and Processes Third International Provenance andAnnotation Workshop IPAW 2010, Troy, NY, USA, June 15-16, 2010 Revised Selected Papers 13 Volume Editors Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, USA E-mail: [email protected] James R. Michaelis Tetherless World Constellation Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, USA E-mail: [email protected] Luc Moreau University of Southampton School of Electronics and Computer Science Southampton SO17 1BJ, United Kingdom E-mail: [email protected] Library of Congress Control Number: 2010940987 CR Subject Classification (1998): H.3-4, D.4.6, I.2, H.5, K.6, K.4, C.2 LNCS Sublibrary: SL 3 – Information Systems and Application, incl.
    [Show full text]
  • A Neuroimaging Data Model to Share Brain Mapping Statistical Results
    bioRxiv preprint doi: https://doi.org/10.1101/041798; this version posted June 14, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. NIDM-Results: a Neuroimaging Data Model to share brain mapping statistical results Camille Maumet1, Tibor Auer2, Alexander Bowring1, Gang Chen15, Samir Das3, Guillaume Flandin4, Satrajit Ghosh5, Tristan Glatard3,6, Krzysztof J. Gorgolewski7, Karl G. Helmer8, Mark Jenkinson9, David Keator10, B. Nolan Nichols11, Jean-Baptiste Poline12, Richard Reynolds16, Vanessa Sochat7, Jessica Turner13 and Thomas E. Nichols1,14 1 Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom. 2 MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom. 3 McGill Centre for Integrative Neuroscience, Ludmer Centre, Montreal Neurological Institute, Montreal, Quebec, Canada. 4 Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, United Kingdom. 5 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA. 6 Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044; INSA-Lyon; Université Claude Bernard Lyon 1, France. 7 Department of Psychology, Stanford University, Stanford, CA, USA. 8 Martinos Center for Biomedical Imaging, Massachusetts General Hospital; Dept. of Radiology, Boston, MA, USA. 9 University of Oxford, UK. 10 Dept. of Psychiatry and Human Behavior, Dept. of Computer Science, Dept. of Neurology, University of California, Irvine, CA, USA. 11 Center for Health Sciences, SRI International, Menlo Park, CA, USA. 12 Helen Wills Neuroscience Institute, H. Wheeler Jr. Brain Imaging Center, University of California, Berkeley, CA, USA.
    [Show full text]
  • Web Approach for Ontology-Based Classification, Integration, and Interdisciplinary Usage of Geoscience Metadata
    Data Science Journal, Volume 11, 18 October 2012 WEB APPROACH FOR ONTOLOGY-BASED CLASSIFICATION, INTEGRATION, AND INTERDISCIPLINARY USAGE OF GEOSCIENCE METADATA B Ritschel1*, V Mende1*, L Gericke2*, R Kornmesser2*, and S Pfeiffer3 1*Data Center, GFZ German Research Centre for Geosciences, Potsdam, German 14473 Potsdam, Germany 2*Hasso-Plattner-Institut für Softwaresystemtechnik, Potsdam, Germany 2-3, 14482 Potsdam, Germany 3Hochschule Neubrandenburg, Neubrandenburg, Germany, Postfach 11 01 21, 17041 Neubrandenburg, Germany ABSTRACT The Semantic Web is a W3C approach that integrates the different sources of semantics within documents and services using ontology-based techniques. The main objective of this approach in the geoscience domain is the improvement of understanding, integration, and usage of Earth and space science related web content in terms of data, information, and knowledge for machines and people. The modeling and representation of semantic attributes and relations within and among documents can be realized by human readable concept maps and machine readable OWL documents. The objectives for the usage of the Semantic Web approach in the GFZ data center ISDC project are the design of an extended classification of metadata documents for product types related to instruments, platforms, and projects as well as the integration of different types of metadata related to data product providers, users, and data centers. Sources of content and semantics for the description of Earth and space science product types and related classes are standardized metadata documents (e.g., DIF documents), publications, grey literature, and Web pages. Other sources are information provided by users, such as tagging data and social navigation information. The integration of controlled vocabularies as well as folksonomies plays an important role in the design of well formed ontologies.
    [Show full text]
  • Metadata and Semantics Workshop Summary
    Metadata and Semantics Workshop Summary February 23-25, 2015 Indianapolis, Indiana Acknowledgements This report is a summary of the Metadata and Semantics Workshop that was held February 23-25, 2015 in Indianapolis, Indiana and funded by US-Research Data Alliance (NSF # 1349002) and DataONE (NSF #0830944). All the materials from this workshop are available on the RDA Metadata Interest Group web page. We would like to thank Indiana University and the staff for their support of this workshop. In particular we thank Jenny Olmes-Stevens and Jodi Stern for their support with logistics both prior to and during the workshop. Thanks to Bolanie Solomon and Scott McCaulay for taking notes and Inna Kouper for help with facilitation. We would also like to thank Jamie Petta for her help with logistics and reimbursement and RDA-US for making this workshop possible. The editors for this paper are Gary Berg-Cross (Spatial Ontology Community of Practice (SOCoP) and Rebecca Koskela (DataONE, University of New Mexico). We would like to acknowledge the contributions of the workshop participants: Laura Bartolo, Kent State University Dave Dubin, University of Illinois Urbana-Champaign Michel Dumontier, Stanford University Peter Fox, Rensselaer Polytechnic Institute Ted Haberman, HDF Group Robert Hanisch, National Institute of Standards and Technology (NIST) Pascal Hitzer, Wright State University Krzysztof Janowicz, University of California Santa Barbara Inna Kouper, Indiana University Adila Krisnadhi, Wright State University Marshall Ma, Rensselaer Polytechnic Institute Emilio Mayorga, University of Washington Scott Peckman, University of Colorado Beth Plale, Indiana University Ray Plante, National Data Service Charles Vardeman, Center for Research Computing Notre Dame Mary Vardigan, Interuniversity Consortium for Political and Social Research (ICPSR) John Westbrook, Rutgers University Ilya Zaslavsky, University of California San Diego 2 Contents Executive Summary ......................................................................................
    [Show full text]
  • Speaker and Moderator Biographical Information
    Data in Motion: New Approaches to Advancing Scientific, Engineering and Medical Progress October 14-15, 2020 Speaker and Moderator Biographical Information JAMES HENDLER (Planning Committee Chair) is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at Rensselaer Polytechnic Institute and an affiliate of the Department of Industrial and Systems Engineering. He also serves as a Director of the UK’s charitable Web Science Trust and a visiting faculty researcher at Bath Spa University. Dr. Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high performance processing. One of the originators of the “Semantic Web,” Dr. Hendler is a Fellow of the American Association for Artificial Intelligence, the British Computer Society, the Institute of Electrical and Electronics Engineers (IEEE) and the American Association for the Advancement of Science (AAAS). He is also the former Chief Scientist of the Information Systems Office at the U.S. Defense Advanced Research Projects Agency (DARPA) and was awarded a U.S. Air Force Exceptional Civilian Service Medal in 2002. He is also the first computer scientist to serve on the Board of Reviewing editors for Science. In 2010, Dr. Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2012, he was one of the inaugural recipients of the Strata Conference “Big Data” awards for his work on large-scale open government data, and he is a columnist and associate editor of the Big Data journal.
    [Show full text]
  • The Foundations for Provenance on the Web
    The Foundations for Provenance on the Web Luc Moreau November 11, 2010 Foundations and Trends in Web Science Vol. 2, Nos. 2-3 (2010) 99{241 @ 2010 L. Moreau DOI: 10.1561/1800000010 Abstract Provenance, i.e., the origin or source of something, is becoming an important concern, since it offers the means to verify data products, to infer their quality, to analyse the processes that led to them, and to decide whether they can be trusted. For instance, provenance enables the reproducibility of scientific results; provenance is necessary to track attribution and credit in curated databases; and, it is essential for reasoners to make trust judgements about the information they use over the Semantic Web. As the Web allows information sharing, discovery, aggregation, filtering and flow in an unprecedented manner, it also becomes very difficult to identify, reli- ably, the original source that produced an information item on the Web. Since the emerging use of provenance in niche applications is undoubtedly demonstrating the benefits of provenance, this survey contends that provenance can and should reliably be tracked and exploited on the Web, and investigates the necessary foundations to achieve such a vision. Multiple data sources have been used to compile the largest bibliographical database on provenance so far. This large corpus permits the analysis of emerg- ing trends in the research community. Specifically, the CiteSpace tool identifies clusters of papers that constitute research fronts, from which characteristics are extracted to structure a foundational framework for provenance on the Web. Such an endeavour requires a multi-disciplinary approach, since it requires contribu- tions from many computer science sub-disciplines, but also other non-technical fields given the human challenge that is anticipated.
    [Show full text]
  • Response to NSF 20-015 Request for Information on Data-Focused
    Response to NSF 20-015, Dear Colleague Letter: Request for Information on Data-Focused Cyberinfrastructure Needed to Support Future Data-Intensive Science and Engineering Research Reference ID: 11227080299_Fontaine Reference ID: 11227080299_Fontaine Submission Date and Time: 12/16/2019 4:58:30 PM This contribution was submitted to the National Science Foundation in response to a Request for Information, https://www.nsf.gov/pubs/2020/nsf20015/nsf20015.jsp. Consideration of this contribution in NSF's planning process and any NSF-provided public accessibility of this document does not constitute approval of the content by NSF or the US Government. The opinions and views expressed herein are those of the author(s) and do not necessarily reflect those of the NSF or the US Government. The content of this submission is protected by the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode). Consent Statement: “I hereby agree to give the National Science Foundation (NSF) the right to use this information for the purposes stated above and to display it on a publicly available website, consistent with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode)." Consent answer: I consent to NSF's use and display of the submitted information. Author Names & Affiliations Submitting author: Kathleen Fontaine - RPI Additional authors: Katherine Chastain, Peter Fox, Shweta Narkar,
    [Show full text]