Semantic Web Services for Interdisciplinary Scientific Data Query and Retrieval
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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. -
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. -
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. -
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 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. -
Semantic Web: a Review of the Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA
Semantic Web: A Review Of The Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA ABSTRACT which would probably produce a rather different narrative of the We review two decades of Semantic Web research and applica- history and the current state of the art of the field. I therefore do tions, discuss relationships to some other disciplines, and current not strive to achieve the impossible task of presenting something challenges in the field. close to a consensus – such a thing seems still elusive. However I do point out here, and sometimes within the narrative, that there CCS CONCEPTS are a good number of alternative perspectives. The review is also necessarily very selective, because Semantic • Information systems → Graph-based database models; In- Web is a rich field of diverse research and applications, borrowing formation integration; Semantic web description languages; from many disciplines within or adjacent to computer science, Ontologies; • Computing methodologies → Description log- and a brief review like this one cannot possibly be exhaustive or ics; Ontology engineering. give due credit to all important individual contributions. I do hope KEYWORDS that I have captured what many would consider key areas of the Semantic Web field. For the reader interested in obtaining amore Semantic Web, ontology, knowledge graph, linked data detailed overview, I recommend perusing the major publication ACM Reference Format: outlets in the field: The Semantic Web journal,1 the Journal of Pascal Hitzler. 2020. Semantic Web: A Review Of The Field. In Proceedings Web Semantics,2 and the proceedings of the annual International of . ACM, New York, NY, USA, 7 pages. -
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. -
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 -
Infrastructure for Web Explanations
Infrastructure for Web Explanations Deborah L. McGuinness and Paulo Pinheiro da Silva Knowledge Systems Laboratory, Stanford University, Stanford CA 94305 {dlm,pp}@ksl.stanford.edu Abstract. The Semantic Web lacks support for explaining knowledge provenance. When web applications return answers, many users do not know what information sources were used, when they were updated, how reliable the source was, or what information was looked up ver- sus derived. The Semantic Web also lacks support for explaining rea- soning paths used to derive answers. The Inference Web (IW) aims to take opaque query answers and make the answers more transparent by providing explanations. The explanations include information concern- ing where answers came from and how they were derived (or retrieved). In this paper we describe an infrastructure for IW explanations. The infrastructure includes: an extensible web-based registry containing de- tails on information sources, reasoners, languages, and rewrite rules; a portable proof specification; and a proof and explanation browser. Source information in the IW registry is used to convey knowledge provenance. Representation and reasoning language axioms and rewrite rules in the IW registry are used to support proofs, proof combination, and semantic web agent interoperability. The IW browser is used to support navigation and presentations of proofs and their explanations. The Inference Web is in use by two Semantic Web agents using an embedded reasoning engine fully registered in the IW. Additional reasoning engine registration is underway in order to help provide input for evaluation of the adequacy, breadth, and scalability of our approach. 1 Introduction Inference Web (IW) aims to enable applications to generate portable and dis- tributed explanations for any of their answers. -
Ecole Doctorale Environnements-Santé
Ecole doctorale LECLA ANNEE 2021 Titre du projet de thèse : SYSTEMES INTELLIGENTS POUR LA TRANSMISSION DES HUMANITES NUMERIQUES ET POUR LA RECHERCHE EN SANTE Présentation du projet 1. Contexte scientifique et état de l’art Le déploiement du web bouleverse notre rapport au savoir et notre métier d’enseignant et de chercheur. Accompagnées d’agents intelligents capables d’effectuer des raisonnements à partir d’ontologies (représentations structurées d’un domaine), les plateformes sémantiques contribuent à construire cet espace commun de la connaissance au XXIe siècle : les « humanités numériques » (HN). Le thème des HN rassemble les travaux de chercheurs qui interrogent notre besoin de transmettre ce socle commun et des compétences de haut niveau cognitif. Ce champ a aussi besoin d’être mieux structuré, clarifié, stimulé et partagé. Sa formalisation peut permettre d’étudier les concepts et les liens entre compétences (Ageeva et al., 2019). Or, si nous savons produire des ontologies de compétences (Desmoulins, 2010), ou utiliser des ontologies dans le domaine des HN (Toyoshima, 2019), nous ne disposons ni d’ontologies, ni de plateforme de référence dans le champ « humanités numériques et éducation » (HNE). Enfin, les données structurées issues des SHS sont rarement valorisées comme objets pédagogiques. La recherche en intelligence artificielle (IA) s’est fortement développé ces dernières années (plan d’investissement français 1,5 md € en France en 2018). Le cadre d’applications ou framework Whyis1 est une solution pour construire une plateforme intelligente dédiées aux HNE. Il permet d’extraire d’une ressource une unité minimale d’information, la nanopublication, pour effectuer des inférences. Il sait traiter des sources de données hétérogènes ainsi que le langage naturel, manipuler des graphes de connaissances communes pour répondre aux questions des enseignants et des chercheurs. -
Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies
Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies Vandana Kabilan October 2007. A Dissertation submitted to The Royal Institute of Technology in partial fullfillment of the requirements for the degree of Doctor of Technology . The Royal Institute of Technology School of Information and Communication Technology Department of Computer and Systems Sciences IV DSV Report Series No. 07–013 ISBN 978–91–7178–752–1 ISSN 1101–8526 ISRN SU–KTH/DSV/R– –07/13– –SE V All knowledge that the world has ever received comes from the mind; the infinite library of the universe is in our own mind. – Swami Vivekananda. (1863 – 1902) Indian spiritual philosopher. The whole of science is nothing more than a refinement of everyday thinking. – Albert Einstein (1879 – 1955) German-Swiss-U.S. scientist. Science is a mechanism, a way of trying to improve your knowledge of na- ture. It’s a system for testing your thoughts against the universe, and seeing whether they match. – Isaac Asimov. (1920 – 1992) Russian-U.S. science-fiction author. VII Dedicated to the three KAs of my life: Kabilan, Rithika and Kavin. IX Abstract. Globalization has opened new frontiers for business enterprises and human com- munication. There is an information explosion that necessitates huge amounts of informa- tion to be speedily processed and acted upon. Information Systems aim to facilitate human decision-making by retrieving context-sensitive information, making implicit knowledge ex- plicit and to reuse the knowledge that has already been discovered. A possible answer to meet these goals is the use of Ontology. -
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.