Lecture Notes in Computer Science 7032 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan Van Leeuwen

Total Page:16

File Type:pdf, Size:1020Kb

Lecture Notes in Computer Science 7032 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan Van Leeuwen Lecture Notes in Computer Science 7032 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 Lora Aroyo Chris Welty Harith Alani Jamie Taylor Abraham Bernstein Lalana Kagal Natasha Noy Eva Blomqvist (Eds.) The Semantic Web – ISWC 2011 10th International Semantic Web Conference Bonn, Germany, October 23-27, 2011 Proceedings, Part II 13 Volume Editors Lora Aroyo VU University Amsterdam, The Netherlands; [email protected] Chris Welty IBM Research, Yorktown Heights, NY, USA; [email protected] Harith Alani The Open University, Milton Keynes, UK; [email protected] Jamie Taylor Google, Mountain View, CA, USA; [email protected] Abraham Bernstein University of Zurich, Switzerland; Bernstein@ifi.uzh.ch Lalana Kagal Massachusetts Institute of Technology, Cambridge, MA, USA; [email protected] Natasha Noy Stanford University, CA, USA; [email protected] Eva Blomqvist Linköping University, Sweden; [email protected] ISSN 0302-9743 e-ISSN 1611-3349 ISBN 978-3-642-25092-7 e-ISBN 978-3-642-25093-4 DOI 10.1007/978-3-642-25093-4 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011939851 CR Subject Classification (1998): C.2, H.4, H.3, H.5, J.1, K.4 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. Internet/Web and HCI © Springer-Verlag Berlin Heidelberg 2011 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, re-use of illustrations, recitation, broadcasting, reproduction on microfilms 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. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface Ten years ago, several researchers decided to organize a workshop to bring together an emerging community of scientists who were working on adding machine-readable semantics to the Web, the Semantic Web. The organizers were originally planning for a few dozen researchers to show up. When 200 of them came to Stanford in August 2001, the Semantic Web Workshop became the Semantic Web Working Symposium, and the International Semantic Web Con- ference (ISWC) was born. Much has changed in the ten years since that meeting. The Semantic Web has become a well-recognized research field in its own right, and ISWC is a premier international research conference today. It brings to- gether researchers, practitioners, and users in artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, human–computer interaction, natural-language processing, and others. Compa- nies from Facebook to Google to the New York Times rely on Semantic Web technologies to link and organize their data; governments in the United States, United Kingdom, and other countries open up their data by making it accessible to Semantic Web tools; scientists in many domains, from biology, to medicine, to oceanography and environmental sciences, view machine-processable semantics as key to sharing their knowledge in today’s data-intensive scientific enterprise; semantic technology trade shows attract more than a thousand attendees. The focus of Semantic Web research has moved from issues of representing data on the Web and the growing pains of figuring out a common format to share it, to such challenges as handling billions of statements in a scalable way to making all this data accessible and usable to regular citizens. This volume contains the main proceedings of the 10th International Seman- tic Web Conference (ISWC 2011), which was held in Bonn, Germany, in October 2011. We received tremendous response to our calls for papers from a truly inter- national community of researchers and practitioners. Indeed, every track of the conference received a record number of submissions this year. The careful nature of the review process, and the breadth and scope of the papers finally selected for inclusion in this volume, speak to the quality of the conference and to the contributions made by researchers whose work is presented in these proceedings. The Research Track of the conference attracted 264 submissions. Each paper received at least three, and sometimes as many as five, reviews from members of the Program Committee. After the first round of reviews, authors had the oppor- tunity to submit a rebuttal, leading to further discussions among the reviewers, a meta-review and a recommendation from a member of the Senior Program Committee. Every paper that had at least one recommendation for acceptance was discussed in a virtual meeting of the Senior Program Committee. As the Semantic Web develops, we find a changing variety of subjects that emerge. This year the keywords of accepted papers were distributed as follows VI Preface (frequency in parentheses): ontologies and semantics (15), database, IR, and AI technologies for the Semantic Web (14), management of Semantic Web data (11), reasoning over Semantic Web data (11), search, query, integration, and analysis on the Semantic Web (10), robust and scalable knowledge management and reasoning on the Web (10), interacting with Semantic Web data (9), on- tology modularity, mapping, merging, and alignment (8), languages, tools, and methodologies for representing and managing Semantic Web data (8), ontol- ogy methodology, evaluation, reuse, extraction, and evolution (7), evaluation of Semantic Web technologies or data (7), specific ontologies and ontology patterns for the Semantic Web (6), new formalisms for the Semantic Web (4), user inter- faces to the Semantic Web (3), cleaning, assurance, and provenance of Semantic Web data, services, and processes (3), social Semantic Web (3), evaluation of Se- mantic Web technology (3), Semantic Web population from the human Web (3). Overall, the ISWC Program Committee members adopted strict standards for what constitutes high-quality Semantic Web research and what papers must deliver in terms of theory, practice, and evaluation in order to be accepted to the Research Track. Correspondingly, the Program Committee accepted only 50 papers, 19% of the submissions. The Semantic Web In-Use Track received 75 submissions. At least three mem- bers of the In-Use Program Committee provided reviews for each paper. Sev- enteen papers were accepted – a 23% acceptance rate. The large number of submissions this year demonstrated the increasingly diverse breadth of applica- tions of Semantic Web technologies in practice. Papers demonstrated how seman- tic technologies could be used to drive a variety of simulation and test systems, manage distributed content and operate within embedded devices. Several pa- pers tapped the growing amount of semantically enriched environmental data available on the Web allowing communities to visualize, organize, and monitor collections for specific purposes. The Doctoral Consortium has become a key event at the conference over the years. PhD students get an opportunity to present their thesis proposals and to get detailed feedback on their research topics and plans from the leading academic and industrial scientists in the field. Out of 31 submissions to the Doctoral Consortium, 6 were accepted as long papers for presentation at the conference, and 9 were accepted for presentation at the special Consortium- only poster session. Each student was assigned a mentor who led the discussion following the presentation of their proposal, and provided extensive feedback and comments. A unique aspect of the ISWC conference is the Semantic Web Challenge. In this competition, the ninth to be held at the conference, practitioners and scientists showcase useful and leading-edge applications of Semantic Web tech- nology. Diana Maynard and Chris Bizer organized the Semantic Web Challenge this year. The keynote talks given by leading scientists in the field further enriched the ISWC program. Alex (Sandy) Pentland, the director of the Human Dynamics Laboratory and the Media Lab Entrepreneurship Program at the Massachusetts Preface VII Institute of
Recommended publications
  • An Overview of Ontoclean
    8 An Overview of OntoClean Nieola Guarino1 and Christopher A. Welty lLaboratory for Applied Ontology (ISTC-CNR) Polo Tecnologico, Via Solteri 38, 38100 Trento, ITAL Y [email protected] 2IBM Watson Research Center 19 Skyline Dr., Hawthome, NY 10532, USA [email protected] Summary. OntoClean is a methodology for validating the ontologie al adequaey of taxonomie relationships. It is based on highly general ontologieal notions drawn from philosophy, like essence, identity, and unity, whieh are used to ehar­ aeterize relevant aspeets of the intended meaning of the properties, classes, and relations that make up an ontology. These aspeets are represented by formal metaproperties, whieh impose several eonstraints on the taxonomie strueture of an ontology. The analysis of these eonstraints helps in evaluating and validating the ehoiees made. In this ehapter we present an informal overview ofthe philoso­ phieal notions involved and their role in OntoClean, review some eommon onto­ logieal pitfalls, and walk through the example that has appeared in pieees in pre­ vious papers and has been the basis of numerous tutorials and talks. 8.1 Introduction The OntoClean methodology was first introdueed in aseries of eonferenee-Iength papers in 2000 [Guarino and Welty, 2000a-e; Welty and Guarino, 2001], and re­ eeived mueh attention and use in subsequent years. The main eontribution of On­ toClean was the beginning of a formal foundation for ontologieal analysis. Alan Reetor, a seasoned veteran at ontologieal analysis in the medieal domain, said of OntoClean, " ... what you have done is reduee the amount of time I spend arguing with doetors that the way I want to model the world is right..." [Reetor, 2002].
    [Show full text]
  • Cristobalthesis
    Open Personalization: Involving Third Parties in Improving the User Experience of Websites Dissertation presented to the Department of Computer Languages and Systems of the University of the Basque Country in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (“international” mention) Cristóbal Arellano Bartolomé Supervisors: Prof. Dr. Oscar Díaz García Dr. Jon Iturrioz Sánchez San Sebastián, Spain, 2013 This work was hosted by the University of the Basque Country (Faculty of Computer Sciences). The author enjoyed a doctoral grant under de FPI (Formacion de Personal Investigador) from the Spanish Ministry of Science & Education during the years 2007 to 2011. The work was was co- supported by the Spanish Ministry of Education, and the European Social Fund under contracts (TIN2005-05610), MODELINE (TIN2008-06507- C02-01) and Scriptongue (TIN2011-23839). Summary Traditional software development captures the user needs during the requirement analysis. The Web makes this endeavour even harder due to the difficulty to determine who these users are. In an attempt to tackle the heterogeneity of the user base, Web Personalization techniques are proposed to guide the users’ experience. In addition, Open Innovation allows organisations to look beyond their internal resources to develop new products or improve existing processes. This thesis sits in between by introducing Open Personalization as a means to incorporate actors other than webmasters in the personalization of web applications. The aim is to provide the technological basis that builds up a trusty environment for webmasters and companion actors to collaborate, i.e. "an architecture of participation". Such architecture very much depends on these actors’ profile.
    [Show full text]
  • 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.
    [Show full text]
  • Introduction to Semantic Web Technologies & Linked Data
    IntroductionIntroduction toto SemanticSemantic WebWeb TechnologiesTechnologies && LinkedLinked DataData OktieOktie HassanzadehHassanzadeh UniversityUniversity ofof TorontoToronto March 2011 CS 443: Database Management Systems - Winter 2011 Outline 2 Introduction Semantic Web Technologies Resource Description Framework (RDF) Querying RDF data (SPARQL) Linked Data Linked Data Principles Linking Open Data Community Project Example Data Sources Example Applications 3 Introduction Web of Documents vs. Web of Data Web of Documents 4 Untyped Untyped Untyped Links Links Links API/ HTML HTML HTML XML A B C D Primary objects: documents Links between documents (or parts of them) Degree of structure in data: fairly low Implicit semantics of contents Designed for: human consumption Based on presentations by Chris Bizer, Richard Cyganiak, Tom Heath, available at http://linkeddata.org/guides-and-tutorials Web of Documents: Problem 5 ? thing thing Are two documents talking about the same ? ? “thing”? ? ? ? Untyped Untyped Untyped Links Links Links API/ HTML HTML HTML XML A B C D Based on presentations by Chris Bizer, Richard Cyganiak, Tom Heath, available at http://linkeddata.org/guides-and-tutorials Example Query 6 Elvis Presley 1935 - 1977 Will there ever be someone like him again? Based on presentation by Lauw, Schenkel, Suchanek, Theobald and Weikum, available at http://www.mpi-inf.mpg.de/yago-naga/CIKM10-tutorial/ Example Query 7 Another Elvis Elvis Presley: The Early Years Elvis spent more weeks at the top of the charts than any other artist. www.fiftiesweb.com/elvis.htm Based on presentation by Lauw, Schenkel, Suchanek, Theobald and Weikum, available at http://www.mpi-inf.mpg.de/yago-naga/CIKM10-tutorial/ Example Query 8 Another singer called Elvis, young Personal relationships of Elvis Presley – Wikipedia ...when Elvis was a young teen...
    [Show full text]
  • Semantics Driven Human-Machine Computation Framework for Linked Islamic Knowledge Engineering
    Semantics Driven Human-Machine Computation Framework for Linked Islamic Knowledge Engineering by Amna Basharat (Under the Direction of Khaled Rasheed and I. Budak Arpinar) Abstract Formalized knowledge engineering activities including semantic annotation and linked data management tasks in specialized domains suffer from considerable knowledge acquisition bottleneck - owing to the lack of availability of experts and in-efficacy of automated approaches. Human Computation & Crowdsourcing (HC&C) methods successfully advocate leveraging the human intelligence and pro- cessing power to solve problems that are still difficult to be solved computationally. Contextualized to the domain of Islamic Knowledge, this research investigates the synergistic interplay of these HC&C methods and the semantic web and proposes a semantics driven human-machine computation framework for knowledge engineer- ing in specialized and knowledge intensive domains. The overall objective is to augment the process of automated knowledge extraction and text mining methods using a hybrid approach for combining collective intelligence of the crowds with that of experts to facilitate activities in formalized knowledge engineering - thus overcoming the so-called knowledge acquisition bottleneck. As part of this framework, we design and implement formal and scalable knowl- edge acquisition workflows through the application of semantics driven crowdsourc- ing methodology and its specialized derivative, called learnersourcing. We evaluate these methods and workflows for a range of knowledge engineering tasks including thematic classification, thematic disambiguation, thematic annotation and contex- tual interlinking for two primary Islamic texts, namely the Qur’an and the books of Prophetic narrations called the Hadith. This is done at various levels of granu- larity, including atomic and composite task workflows, that existing research fails to address.
    [Show full text]
  • An Overview of Ontoclean
    8 An Overview of OntoClean Nicola Guarino1 and Christopher A. Welty2 1Laboratory for Applied Ontology (ISTC-CNR) Polo Tecnologico, Via Solteri 38, 38100 Trento, ITALY [email protected] 2IBM Watson Research Center 19 Skyline Dr., Hawthorne, NY 10532, USA [email protected] Summary. OntoClean is a methodology for validating the ontological adequacy of taxonomic relationships. It is based on highly general ontological notions drawn from philosophy, like essence, identity, and unity, which are used to characterize relevant aspects of the intended meaning of the properties, classes, and relations that make up an ontology. These aspects are represented by formal metaproperties, which impose several constraints on the taxonomic structure of an ontology. The analysis of these constraints helps in evaluating and validating the choices made. In this chapter we present an informal overview of the philosophical notions in- volved and their role in OntoClean, review some common ontological pitfalls, and walk through the example that has appeared in pieces in previous papers and has been the basis of numerous tutorials and talks. 8.1 Introduction The OntoClean methodology was first introduced in a series of conference-length papers in 2000 [Guarino and Welty, 2000a-c; Welty and Guarino, 2001], and re- ceived much attention and use in subsequent years. The main contribution of On- toClean was the beginning of a formal foundation for ontological analysis. Alan Rector, a seasoned veteran at ontological analysis in the medical domain, said of OntoClean, “…what you have done is reduce the amount of time I spend arguing with doctors that the way I want to model the world is right…” [Rector, 2002].
    [Show full text]
  • Ontology-Driven Conceptual Modeling
    Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center CAISE-02 Acknowledgements People Organizations Vassar College, USA Nicola Guarino LADSEB-CNR, Padova Cladio Masolo CNR Cognitive Science Aldo Gangemi Institute, Trento Alessandro Oltramari Bill Andersen OntologyWorks, Inc. CAISE-02 2 1 Outline • Setting the record straight • Motivation • Formal foundation • “Upper Level” distinctions • Common pitfalls CAISE-02 3 What is Ontology? • A discipline of Philosophy – Meta-physics dates back to Artistotle • Meta (after) + physica (physical, real) – Ontology dates back to 17th century • Ontos (that which exists) + logos (knowledge of) • As in TorONTO, ONTario, ON TOp – The science of what is (in the universe) – “One universe, One ontology” • Quine, 1969: “To exist is to be the value of a quantified variable” CAISE-02 4 2 What is Ontology? • Borrowed by AI community – McCarthy (1980) calls for “a list of things that exist” – Specify all the kinds of things that can be the values of variables • Evolution of meaning in CS – Now refers to domain modeling, conceptual modeling, knowledge engineering, etc. • Note: not a “new name for an old thing” CAISE-02 5 What is an Ontology? • Poor definition: “Specification of a conceptualization” [Gruber, 1993] • Better: “Description of the kinds of entities there are and how they are related.” • Good ontologies should provide: – Meaning –Agreement – Organization – Common Understanding – Taxonomy – Vocabulary – Connection to the “real world” CAISE-02 6 3 What is an Ontology? a set of a collection
    [Show full text]
  • IBM Research Report Harnessing Disagreement in Crowdsourcing A
    RC25371 (WAT1304-058) April 23, 2013 Computer Science IBM Research Report Harnessing Disagreement in Crowdsourcing a Relation Extraction Gold Standard Lora Aroyo VU University Amsterdam, The Netherlands Chris Welty IBM Research Division Thomas J. Watson Research Center P.O. Box 208 Yorktown Heights, NY 10598 USA Research Division Almaden - Austin - Beijing - Cambridge - Haifa - India - T. J. Watson - Tokyo - Zurich LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g. , payment of royalties). Copies may be requested from IBM T. J. Watson Research Center , P. O. Box 218, Yorktown Heights, NY 10598 USA (email: [email protected]). Some reports are available on the internet at http://domino.watson.ibm.com/library/CyberDig.nsf/home . Harnessing disagreement in crowdsourcing a relation extraction gold standard Lora Aroyo Chris Welty VU University Amsterdam IBM Watson Research Center The Netherlands USA [email protected] [email protected] ABSTRACT are described in natural language. The importance of rela- One of the first steps in any kind of web data analytics tions and their interpretation is widely recognized in NLP, is creating a human annotated gold standard. These gold but whereas NLP technology for detecting entities (such as standards are created based on the assumption that for each people, places, organizations, etc.) in text can be expected annotated instance there is a single right answer.
    [Show full text]
  • Crowdsourcing,The,Semantic,Web
    ! ! ! ! ! ! Proceedings! of!the! First!International!Workshop! on! ! Crowdsourcing,the,Semantic,Web, held%in%conjunction%with! the!12th!International!Semantic!Web!Conference! ! Sydney,!Australia! ! Editors:! Maribel!Acosta! Lora!Aroyo! Abraham!Bernstein! Jens!Lehmann! Natasha!Noy! Elena!Simperl! ! Copyright!©!2013!for!the!individual!papers!by!the!papers’!authors.!Copying! permitted!only!for!private!and!academic!purposes.!This!volume!is!published!and! copyrighted!by!its!editors.! Preface This volume contains the papers presented at the 1st International Workshop on ”Crowdsourcing the Semantic Web” that was held in conjunction with the 12th International Semantic Web Conference (ISWC 2013), 21-25 October 2013, in Sydney, Australia. This interactive workshop takes stock of the emergent work and chart the research agenda with interactive sessions to brainstorm ideas and potential applications of collective intelligence to solving AI hard semantic web problems. There were 12 submissions. Each submission was reviewed by at least 2, and on the average 3, program committee members. The committee decided to accept 9 papers. Our special thanks goes to the reviewers who diligently reviewed the papers within this volume. September 3, 2013 Maribel Acosta Lora Aroyo Abraham Bernstein Jens Lehmann Natasha Noy Elena Simperl v Table of Contents Full Papers Crowdsourced Semantics with Semantic Tagging: “Don’t just tag it, LexiTag it!” ...................................................... 1 Csaba Veres ”Dr. Detective”: combining gamification techniques and crowdsourcing to create a gold standard in medical text ............................. 16 Anca Dumitrache, Lora Aroyo, Chris Welty, Robert-Jan Sips and An- thony Levas SLUA: Towards Semantic Linking of Users with Actions in Crowdsourcing 32 Umair Ul Hassan, Sean O’Riain and Edward Curry Content and Behaviour Based Metrics for Crowd Truth ...............
    [Show full text]
  • The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics in Line with Reality
    The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality Mitchell L. Gordon Kaitlyn Zhou Kayur Patel Stanford University Stanford University Apple Inc. [email protected] [email protected] [email protected] Tatsunori Hashimoto Michael S. Bernstein Stanford University Stanford University [email protected] [email protected] ABSTRACT 1 INTRODUCTION Machine learning classifiers for human-facing tasks such as com- The machine learning classifiers that underpin modern social com- ment toxicity and misinformation often score highly on metrics puting systems such as Facebook, Wikipedia, and Twitter are a such as ROC AUC but are received poorly in practice. Why this gap? study in contrasts. On one hand, current performance metrics for Today, metrics such as ROC AUC, precision, and recall are used to popular tasks such as comment toxicity classification and disin- measure technical performance; however, human-computer inter- formation detection are extremely high, featuring up to .95 ROC action observes that evaluation of human-facing systems should AUC [11, 72], making the problem appear nearly solved. On the account for people’s reactions to the system. In this paper, we intro- other hand, audits suggest that these algorithms’ performance is duce a transformation that more closely aligns machine learning in reality much poorer than advertised [56]. Even well-intentioned classification metrics with the values and methods of user-facing platforms will continue to make highly-publicized mistakes [16] performance measures. The disagreement deconvolution takes in until they can better align metrics with reality. any multi-annotator (e.g., crowdsourced) dataset, disentangles sta- This disconnect between classifier performance and user-facing ble opinions from noise by estimating intra-annotator consistency, performance is indicative of a larger disconnect between how ma- and compares each test set prediction to the individual stable opin- chine learning (ML) and human-computer interaction (HCI) re- ions from each annotator.
    [Show full text]
  • Evaluating Ontology Cleaning
    Evaluating Ontology Cleaning Christopher Welty, Ruchi Mahindru, and Jennifer Chu-Carroll IBM T.J. Watson Research Center 19 Skyline Dr. Hawthorne, NY 10532 {welty,jencc}@us.ibm.com, [email protected] Abstract very little evidence that it can have impact on Ontology as a discipline of Computer Science has made knowledge-based systems. In fact, there appears many claims about its usefulness, however to date there has to be a significant obstacle in understanding the been very little evaluation of those claims. We present the methodology, and even without this “learning results of an experiment using a hybrid search system with a curve”, significant manual effort must be significant knowledge-based component to measure, using expended to employ the methodology to develop precision and recall, the impact of improving the quality of actual “clean” ontologies. Furthermore, there an ontology on overall performance. We demonstrate that has been no clear argument that such an improving the ontology using OntoClean (Guarino and expenditure will pay for itself in the long run. Welty, 2002), does positively impact performance, and that Indeed, “Why does it matter?” has been the most having knowledge of the search domain is more effective than domain-knowledge-free search techniques such as link frequent criticism of the OntoClean approach. analysis. We report here on a series of experiments using a Content Areas: Ontologies hybrid search system with a significant knowledge-based component to test the impact of improving the quality of ontologies on system Introduction performance. The use of search as a test system provides a well understood framework for Ontologies have been proposed as a solution to empirical evaluation, and gives an excellent numerous problems in areas such as search, opportunity to address the “Why does it matter?” semantic integration, agents, human-computer question.
    [Show full text]
  • Crowd Truth and the Seven Myths of Human Annotation Aroyo, Lora; Welty, Chris
    VU Research Portal Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation Aroyo, Lora; Welty, Chris published in The AI Magazine 2015 DOI (link to publisher) 10.1609/aimag.v36i1.2564 document version Publisher's PDF, also known as Version of record Link to publication in VU Research Portal citation for published version (APA) Aroyo, L., & Welty, C. (2015). Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation. The AI Magazine, 36(1), 15-24. https://doi.org/10.1609/aimag.v36i1.2564 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. E-mail address: [email protected] Download date: 30. Sep. 2021 Articles Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation Lora Aroyo, Chris Welty n Big data is having a disruptive impact amar prestar aen … “The world is changed.” In the past across the sciences.
    [Show full text]