Communications Wikidata: from “An” Identifier to “The” Identifier Theo Van Veen
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Amber Billey Senylrc 4/1/2016
AMBER BILLEY SENYLRC 4/1/2016 Photo by twm1340 - Creative Commons Attribution-ShareAlike License https://www.flickr.com/photos/89093669@N00 Created with Haiku Deck Today’s slides: http://bit.ly/SENYLRC_BF About me... ● Metadata Librarian ● 10 years of cataloging and digitization experience for cultural heritage institutions ● MLIS from Pratt in 2009 ● Zepheira BIBFRAME alumna ● Curious by nature [email protected] @justbilley Photo by GBokas - Creative Commons Attribution-NonCommercial-ShareAlike License https://www.flickr.com/photos/36724091@N07 Created with Haiku Deck http://digitalgallery.nypl.org/nypldigital/id?1153322 02743cam 22004094a 45000010008000000050017000080080041000250100017000660190014000830200031000970200028001280200 03800156020003500194024001600229035002400245035003900269035001700308040005300325050002200378 08200120040010000300041224500790044225000120052126000510053330000350058449000480061950400640 06675050675007315200735014066500030021416500014021717000023021858300049022089000014022579600 03302271948002902304700839020090428114549.0080822s2009 ctua b 001 0 eng a 2008037446 a236328594 a9781591585862 (alk. paper) a1591585864 (alk. paper) a9781591587002 (pbk. : alk. paper) a159158700X (pbk. : alk. paper) a99932583184 a(OCoLC) ocn236328585 a(OCoLC)236328585z(OCoLC)236328594 a(NNC)7008390 aDLCcDLCdBTCTAdBAKERdYDXCPdUKMdC#PdOrLoB-B00aZ666.5b.T39 200900a0252221 aTaylor, Arlene G., d1941-14aThe organization of information /cArlene G. Taylor and Daniel N. Joudrey. a3rd ed. aWestport, Conn. :bLibraries Unlimited,c2009. axxvi, -
What Do Wikidata and Wikipedia Have in Common? an Analysis of Their Use of External References
What do Wikidata and Wikipedia Have in Common? An Analysis of their Use of External References Alessandro Piscopo Pavlos Vougiouklis Lucie-Aimée Kaffee University of Southampton University of Southampton University of Southampton United Kingdom United Kingdom United Kingdom [email protected] [email protected] [email protected] Christopher Phethean Jonathon Hare Elena Simperl University of Southampton University of Southampton University of Southampton United Kingdom United Kingdom United Kingdom [email protected] [email protected] [email protected] ABSTRACT one hundred thousand. These users have gathered facts about Wikidata is a community-driven knowledge graph, strongly around 24 million entities and are able, at least theoretically, linked to Wikipedia. However, the connection between the to further expand the coverage of the knowledge graph and two projects has been sporadically explored. We investigated continuously keep it updated and correct. This is an advantage the relationship between the two projects in terms of the in- compared to a project like DBpedia, where data is periodically formation they contain by looking at their external references. extracted from Wikipedia and must first be modified on the Our findings show that while only a small number of sources is online encyclopedia in order to be corrected. directly reused across Wikidata and Wikipedia, references of- Another strength is that all the data in Wikidata can be openly ten point to the same domain. Furthermore, Wikidata appears reused and shared without requiring any attribution, as it is to use less Anglo-American-centred sources. These results released under a CC0 licence1. -
Wikipedia Knowledge Graph with Deepdive
The Workshops of the Tenth International AAAI Conference on Web and Social Media Wiki: Technical Report WS-16-17 Wikipedia Knowledge Graph with DeepDive Thomas Palomares Youssef Ahres [email protected] [email protected] Juhana Kangaspunta Christopher Re´ [email protected] [email protected] Abstract This paper is organized as follows: first, we review the related work and give a general overview of DeepDive. Sec- Despite the tremendous amount of information on Wikipedia, ond, starting from the data preprocessing, we detail the gen- only a very small amount is structured. Most of the informa- eral methodology used. Then, we detail two applications tion is embedded in unstructured text and extracting it is a non trivial challenge. In this paper, we propose a full pipeline that follow this pipeline along with their specific challenges built on top of DeepDive to successfully extract meaningful and solutions. Finally, we report the results of these applica- relations from the Wikipedia text corpus. We evaluated the tions and discuss the next steps to continue populating Wiki- system by extracting company-founders and family relations data and improve the current system to extract more relations from the text. As a result, we extracted more than 140,000 with a high precision. distinct relations with an average precision above 90%. Background & Related Work Introduction Until recently, populating the large knowledge bases relied on direct contributions from human volunteers as well With the perpetual growth of web usage, the amount as integration of existing repositories such as Wikipedia of unstructured data grows exponentially. Extract- info boxes. These methods are limited by the available ing facts and assertions to store them in a struc- structured data and by human power. -
Knowledge Graphs on the Web – an Overview Arxiv:2003.00719V3 [Cs
January 2020 Knowledge Graphs on the Web – an Overview Nicolas HEIST, Sven HERTLING, Daniel RINGLER, and Heiko PAULHEIM Data and Web Science Group, University of Mannheim, Germany Abstract. Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowl- edge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chap- ter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap. Keywords. Knowledge Graph, Linked Data, Semantic Web, Profiling 1. Introduction Knowledge Graphs are increasingly used as means to represent knowledge. Due to their versatile means of representation, they can be used to integrate different heterogeneous data sources, both within as well as across organizations. [8,9] Besides such domain-specific knowledge graphs which are typically developed for specific domains and/or use cases, there are also public, cross-domain knowledge graphs encoding common knowledge, such as DBpedia, Wikidata, or YAGO. [33] Such knowl- edge graphs may be used, e.g., for automatically enriching data with background knowl- arXiv:2003.00719v3 [cs.AI] 12 Mar 2020 edge to be used in knowledge-intensive downstream applications. -
Download; (2) the Appropriate Log-In and Password to Access the Server; and (3) Where on the Server (I.E., in What Folder) the File Was Kept
AN ALCTS MONOGRAPH LINKED DATA FOR THE PERPLEXED LIBRARIAN SCOTT CARLSON CORY LAMPERT DARNELLE MELVIN AND ANNE WASHINGTON chicago | 2020 alastore.ala.org © 2020 by the American Library Association Extensive effort has gone into ensuring the reliability of the information in this book; however, the publisher makes no warranty, express or implied, with respect to the material contained herein. ISBNs 978-0-8389-4746-3 (paper) 978-0-8389-4712-8 (PDF) 978-0-8389-4710-4 (ePub) 978-0-8389-4711-1 (Kindle) Library of Congress Control Number: 2019053975 Cover design by Alejandra Diaz. Text composition by Dianne M. Rooney in the Adobe Caslon Pro and Archer typefaces. This paper meets the requirements of ANSI/NISO Z39.48–1992 (Permanence of Paper). Printed in the United States of America 23 24 22 21 20 5 4 3 2 1 alastore.ala.org CONTENTS Acknowledgments vii Introduction ix One Enquire Within upon Everything 1 The Origins of Linked Data Two Unfunky and Obsolete 17 From MARC to RDF Three Mothership Connections 39 URIs and Serializations Four What Is a Thing? 61 Ontologies and Linked Data Five Once upon a Time Called Now 77 Real-World Examples of Linked Data Six Tear the Roof off the Sucker 105 Linked Library Data Seven Freaky and Habit-Forming 121 Linked Data Projects That Even Librarians Can Mess Around With EPILOGUE The Unprovable Pudding: Where Is Linked Data in Everyday Library Life? 139 Bibliography 143 Glossary 149 Figure Credits 153 About the Authors 155 Index 157 alastore.ala.orgv INTRODUCTION ince the mid-2000s, the greater GLAM (galleries, libraries, archives, and museums) community has proved itself to be a natural facilitator S of the idea of linked data—that is, a large collection of datasets on the Internet that is structured so that both humans and computers can understand it. -
Preparing for a Linked Data Approach to Name Authority Control in an Institutional Repository Context
Title Assessing author identifiers: preparing for a linked data approach to name authority control in an institutional repository context Author details Corresponding author: Moira Downey, Digital Repository Content Analyst, Duke University ([email protected] ; 919 660 2409; https://orcid.org/0000-0002-6238-4690) 1 Abstract Linked data solutions for name authority control in digital libraries are an area of growing interest, particularly among institutional repositories (IRs). This article first considers the shift from traditional authority files to author identifiers, highlighting some of the challenges and possibilities. An analysis of author name strings in Duke University's open access repository, DukeSpace, is conducted in order to identify a suitable source of author URIs for Duke's newly launched repository for research data. Does one of three prominent international authority sources—LCNAF, VIAF, and ORCID—demonstrate the most comprehensive uptake? Finally, recommendations surrounding a technical approach to leveraging author URIs at Duke are briefly considered. Keywords Name authority control, Authority files, Author identifiers, Linked data, Institutional repositories 2 Introduction Linked data has increasingly been looked upon as an encouraging model for storing metadata about digital objects in the libraries, archives and museums that constitute the cultural heritage sector. Writing in 2010, Coyle draws a connection between the affordances of linked data and the evolution of what she refers to as "bibliographic control," that is, "the organization of library materials to facilitate discovery, management, identification, and access" (2010, p. 7). Coyle notes the encouragement of the Working Group on the Future of Bibliographic Control to think beyond the library catalog when considering avenues by which users seek and encounter information, as well as the group's observation that the future of bibliographic control will be "collaborative, decentralized, international in scope, and Web- based" (2010, p. -
Scripts, Languages, and Authority Control Joan M
49(4) LRTS 243 Scripts, Languages, and Authority Control Joan M. Aliprand Library vendors’ use of Unicode is leading to library systems with multiscript capability, which offers the prospect of multiscript authority records. Although librarians tend to focus on Unicode in relation to non-Roman scripts, language is a more important feature of authority records than script. The concept of a catalog “locale” (of which language is one aspect) is introduced. Restrictions on the structure and content of a MARC 21 authority record are outlined, and the alternative structures for authority records containing languages written in non- Roman scripts are described. he Unicode Standard is the universal encoding standard for all the charac- Tters used in writing the world’s languages.1 The availability of library systems based on Unicode offers the prospect of library records not only in all languages but also in all the scripts that a particular system supports. While such a system will be used primarily to create and provide access to bibliographic records in their actual scripts, it can also be used to create authority records for the library, perhaps for contribution to communal authority files. A number of general design issues apply to authority records in multiple languages and scripts, design issues that affect not just the key hubs of communal authority files, but any institution or organization involved with authority control. Multiple scripts in library systems became available in the 1980s in the Research Libraries Information Network (RLIN) with the addition of Chinese, Japanese, and Korean (CJK) capability, and in ALEPH (Israel’s research library network), which initially provided Latin and Hebrew scripts and later Arabic, Cyrillic, and Greek.2 The Library of Congress continued to produce catalog cards for material in the JACKPHY (Japanese, Arabic, Chinese, Korean, Persian, Hebrew, and Yiddish) languages until all of the scripts used to write these languages were supported by an automated system. -
There Are No Limits to Learning! Academic and High School
Brick and Click Libraries An Academic Library Symposium Northwest Missouri State University Friday, November 5, 2010 Managing Editors: Frank Baudino Connie Jo Ury Sarah G. Park Co-Editor: Carolyn Johnson Vicki Wainscott Pat Wyatt Technical Editor: Kathy Ferguson Cover Design: Sean Callahan Northwest Missouri State University Maryville, Missouri Brick & Click Libraries Team Director of Libraries: Leslie Galbreath Co-Coordinators: Carolyn Johnson and Kathy Ferguson Executive Secretary & Check-in Assistant: Beverly Ruckman Proposal Reviewers: Frank Baudino, Sara Duff, Kathy Ferguson, Hong Gyu Han, Lisa Jennings, Carolyn Johnson, Sarah G. Park, Connie Jo Ury, Vicki Wainscott and Pat Wyatt Technology Coordinators: Sarah G. Park and Hong Gyu Han Union & Food Coordinator: Pat Wyatt Web Page Editors: Lori Mardis, Sarah G. Park and Vicki Wainscott Graphic Designer: Sean Callahan Table of Contents Quick & Dirty Library Promotions That Really Work! 1 Eric Jennings, Reference & Instruction Librarian Kathryn Tvaruzka, Education Reference Librarian University of Wisconsin Leveraging Technology, Improving Service: Streamlining Student Billing Procedures 2 Colleen S. Harris, Head of Access Services University of Tennessee – Chattanooga Powerful Partnerships & Great Opportunities: Promoting Archival Resources and Optimizing Outreach to Public and K12 Community 8 Lea Worcester, Public Services Librarian Evelyn Barker, Instruction & Information Literacy Librarian University of Texas at Arlington Mobile Patrons: Better Services on the Go 12 Vincci Kwong, -
Falcon 2.0: an Entity and Relation Linking Tool Over Wikidata
Falcon 2.0: An Entity and Relation Linking Tool over Wikidata Ahmad Sakor Kuldeep Singh [email protected] [email protected] L3S Research Center and TIB, University of Hannover Cerence GmbH and Zerotha Research Hannover, Germany Aachen, Germany Anery Patel Maria-Esther Vidal [email protected] [email protected] TIB, University of Hannover L3S Research Center and TIB, University of Hannover Hannover, Germany Hannover, Germany ABSTRACT Management (CIKM ’20), October 19–23, 2020, Virtual Event, Ireland. ACM, The Natural Language Processing (NLP) community has signifi- New York, NY, USA, 8 pages. https://doi.org/10.1145/3340531.3412777 cantly contributed to the solutions for entity and relation recog- nition from a natural language text, and possibly linking them to 1 INTRODUCTION proper matches in Knowledge Graphs (KGs). Considering Wikidata Entity Linking (EL)- also known as Named Entity Disambiguation as the background KG, there are still limited tools to link knowl- (NED)- is a well-studied research domain for aligning unstructured edge within the text to Wikidata. In this paper, we present Falcon text to its structured mentions in various knowledge repositories 2.0, the first joint entity and relation linking tool over Wikidata. It (e.g., Wikipedia, DBpedia [1], Freebase [4] or Wikidata [28]). Entity receives a short natural language text in the English language and linking comprises two sub-tasks. The first task is Named Entity outputs a ranked list of entities and relations annotated with the Recognition (NER), in which an approach aims to identify entity proper candidates in Wikidata. The candidates are represented by labels (or surface forms) in an input sentence. -
What Are Controlled Vocabularies?
2. What Are Controlled Vocabularies? A controlled vocabulary is an organized arrangement of words and phrases used to index content and/or to retrieve content through browsing or searching. It typically includes preferred and variant terms and has a defined scope or describes a specific domain. 2.1. Purpose of Controlled Vocabularies The purpose of controlled vocabularies is to organize information and to provide terminology to catalog and retrieve information. While capturing the richness of variant terms, controlled vocabularies also promote consistency in preferred terms and the assignment of the same terms to similar content. Given that a shared goal of the cultural heritage community is to improve access to visual arts and material culture information, controlled vocabularies are essential. They are necessary at the indexing phase because without them catalogers will not consistently use the same term to refer to the same person, place, or thing. In the retrieval process, various end users may use different synonyms or more generic terms to refer to a given concept. End users are often not specialists and thus need to be guided because they may not know the correct term. The most important functions of a controlled vocabulary are to gather together variant terms and synonyms for concepts and to link concepts in a logical order or sort them into categories. Are a rose window and a Catherine wheel the same thing? How is pot-metal glass related to the more general term stained glass? The links and relationships in a controlled vocabulary ensure that these connections are defined and maintained, for both cataloging and retrieval. -
Useful Links
Useful links Authority control: a basic glossary of terms http://ublib.buffalo.edu/libraries/units/cts/ac/def.html A basic glossary of terms on authority control compiled from various sources AUTOCAT http://www.cwu.edu/~dcc/Autocat/subscribe.html AUTOCAT is a semi-moderated international electronic discussion list. It serves as an electronic forum for the discussion of all questions relating to cataloguing and authority control in libraries. Data Dictionary for Preservation Metadata: Final Report of the PREMIS (PREservation Metadata: Implementation Strategies) Working Group http://www.oclc.org/research/projects/pmwg/premis-final.pdf This publication includes the PREMIS Working Group Final Report, the Data Dictionary, and Examples. The report and the PREMIS Data Dictionary version 1.0 are the culmination of nearly two years of effort by PREMIS members. This Data Dictionary defines and describes an implementable set of core preservation metadata with broad applicability to digital preservation repositories. This report is intended to put the Data Dictionary into context, explain the underlying assumptions and data model, and provide additional information about the meaning and use of semantic units defined in the Data Dictionary. Dewey Decimal Classification (DDC) http://www.oclc.org/dewey/ The Dewey Decimal Classification (DDC) system, devised by library pioneer Melvil Dewey in the 1870s and owned by OCLC since 1988, provides a dynamic structure for the organization of library collections. Now in its 22nd edition, and available in print and Web versions, the DDC is the world’s most widely used library classification system. The DDC provides a logical system for organizing every item in your library’s unique collection, offer library users familiarity and consistency of a time-honoured classification system used in 200,000 libraries worldwide. -
Wembedder: Wikidata Entity Embedding Web Service
Wembedder: Wikidata entity embedding web service Finn Årup Nielsen Cognitive Systems, DTU Compute Technical University of Denmark Kongens Lyngby, Denmark [email protected] ABSTRACT as the SPARQL-based Wikidata Query Service (WDQS). General I present a web service for querying an embedding of entities in functions for finding related items or generating fixed-length fea- the Wikidata knowledge graph. The embedding is trained on the tures for machine learning are to my knowledge not available. Wikidata dump using Gensim’s Word2Vec implementation and a simple graph walk. A REST API is implemented. Together with There is some research that combines machine learning and Wiki- the Wikidata API the web service exposes a multilingual resource data [9, 14], e.g., Mousselly-Sergieh and Gurevych have presented for over 600’000 Wikidata items and properties. a method for aligning Wikidata items with FrameNet based on Wikidata labels and aliases [9]. Keywords Wikidata, embedding, RDF, web service. Property suggestion is running live in the Wikidata editing inter- face, where it helps editors recall appropriate properties for items during manual editing, and as such a form of recommender sys- 1. INTRODUCTION tem. Researchers have investigated various methods for this pro- The Word2Vec model [7] spawned an interest in dense word repre- cess [17]. sentation in a low-dimensional space, and there are now a consider- 1 able number of “2vec” models beyond the word level. One recent Scholia at https://tools.wmflabs.org/scholia/ is avenue of research in the “2vec” domain uses knowledge graphs our web service that presents scholarly profiles based on data in [13].