The Role of Ontologies in Data Integration
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Metadata and GIS
Metadata and GIS ® An ESRI White Paper • October 2002 ESRI 380 New York St., Redlands, CA 92373-8100, USA • TEL 909-793-2853 • FAX 909-793-5953 • E-MAIL [email protected] • WEB www.esri.com Copyright © 2002 ESRI All rights reserved. Printed in the United States of America. The information contained in this document is the exclusive property of ESRI. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by ESRI. All requests should be sent to Attention: Contracts Manager, ESRI, 380 New York Street, Redlands, CA 92373-8100, USA. The information contained in this document is subject to change without notice. U.S. GOVERNMENT RESTRICTED/LIMITED RIGHTS Any software, documentation, and/or data delivered hereunder is subject to the terms of the License Agreement. In no event shall the U.S. Government acquire greater than RESTRICTED/LIMITED RIGHTS. At a minimum, use, duplication, or disclosure by the U.S. Government is subject to restrictions as set forth in FAR §52.227-14 Alternates I, II, and III (JUN 1987); FAR §52.227-19 (JUN 1987) and/or FAR §12.211/12.212 (Commercial Technical Data/Computer Software); and DFARS §252.227-7015 (NOV 1995) (Technical Data) and/or DFARS §227.7202 (Computer Software), as applicable. Contractor/Manufacturer is ESRI, 380 New York Street, Redlands, CA 92373- 8100, USA. -
Creating Permissionless Blockchains of Metadata Records Dejah Rubel
Articles No Need to Ask: Creating Permissionless Blockchains of Metadata Records Dejah Rubel ABSTRACT This article will describe how permissionless metadata blockchains could be created to overcome two significant limitations in current cataloging practices: centralization and a lack of traceability. The process would start by creating public and private keys, which could be managed using digital wallet software. After creating a genesis block, nodes would submit either a new record or modifications to a single record for validation. Validation would rely on a Federated Byzantine Agreement consensus algorithm because it offers the most flexibility for institutions to select authoritative peers. Only the top tier nodes would be required to store a copy of the entire blockchain thereby allowing other institutions to decide whether they prefer to use the abridged version or the full version. INTRODUCTION Several libraries and library vendors are investigating how blockchain could improve activities such as scholarly publishing, content dissemination, and copyright enforcement. A few organizations, such as Katalysis, are creating prototypes or alpha versions of blockchain platforms and products.1 Although there has been some discussion about using blockchains for metadata creation and management, only one company appears to be designing such a product. Therefore, this article will describe how permissionless blockchains of metadata records could be created, managed, and stored to overcome current challenges with metadata creation and management. LIMITATIONS OF CURRENT PRACTICES Metadata standards, processes, and systems are changing to meet twenty-first century information needs and expectations. There are two significant limitations, however, to our current metadata creation and modification practices that have not been addressed: centralization and traceability. -
A Tool for Identifying Attribute Correspondences in Heterogeneous Databases Using Neural Networks Q
Data & Knowledge Engineering 33 (2000) 49±84 www.elsevier.com/locate/datak SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks q Wen-Syan Li a,*,1, Chris Clifton b,2 a C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, MS SJ100 San, Jose, CA 95134, USA b The MITRE Corporation, M/S K308, 202 Burlington Road, Bedford, MA 01730-1420, USA Received 23 February 1999; received in revised form 23 September 1999; accepted 11 November 1999 Abstract One step in interoperating among heterogeneous databases is semantic integration: Identifying relationships between attributes or classes in dierent database schemas. SEMantic INTegrator (SEMINT) is a tool based on neural networks to assist in identifying attribute correspondences in heterogeneous databases. SEMINT supports access to a variety of database systems and utilizes both schema information and data contents to produce rules for matching corresponding attributes automatically. This paper provides theoretical background and implementation details of SEMINT. Ex- perimental results from large and complex real databases are presented. We discuss the eectiveness of SEMINT and our experiences with attribute correspondence identi®cation in various environments. Ó 2000 Elsevier Science B.V. All rights reserved. Keywords: Heterogeneous databases; Database integration; Attribute correspondence identi®cation; Neural networks 1. Introduction Advances in information technology have lead to an explosion in the number of databases accessible to a user. Although these databases are generally designed to be self-contained, serving a limited application domain, the ability to integrate data from multiple databases enables many new applications. For example, corporate decision-makers have realized that the huge amount of information stored in information systems is not only operational data; but an asset that can, when integrated, be used to support decision-making. -
Semantic Integration and Knowledge Discovery for Environmental Research
Journal of Database Management, 18(1), 43-67, January-March 2007 43 Semantic Integration and Knowledge Discovery for Environmental Research Zhiyuan Chen, University of Maryland, Baltimore County (UMBC), USA Aryya Gangopadhyay, University of Maryland, Baltimore County (UMBC), USA George Karabatis, University of Maryland, Baltimore County (UMBC), USA Michael McGuire, University of Maryland, Baltimore County (UMBC), USA Claire Welty, University of Maryland, Baltimore County (UMBC), USA ABSTRACT Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantics- based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution brows- ing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results. Keywords: environmental research, knowledge discovery and navigation, semantic integra- tion, semantic networks, -
Semantic Integration Across Heterogeneous Databases Finding Data Correspondences Using Agglomerative Hierarchical Clustering and Artificial Neural Networks
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO Master in Computer Science Date: April 11, 2018 Supervisor: John Folkesson Examiner: Hedvig Kjellström Swedish title: Semantisk integrering mellan heterogena databaser: Hitta datakopplingar med hjälp av hierarkisk klustring och artificiella neuronnät School of Computer Science and Communication iii Abstract The process of data integration is an important part of the database field when it comes to database migrations and the merging of data. The research in the area has grown with the addition of machine learn- ing approaches in the last 20 years. Due to the complexity of the re- search field, no go-to solutions have appeared. Instead, a wide variety of ways of enhancing database migrations have emerged. This thesis examines how well a learning-based solution performs for the seman- tic integration problem in database migrations. Two algorithms are implemented. One that is based on informa- tion retrieval theory, with the goal of yielding a matching result that can be used as a benchmark for measuring the performance of the machine learning algorithm. The machine learning approach is based on grouping data with agglomerative hierarchical clustering and then training a neural network to recognize patterns in the data. This al- lows making predictions about potential data correspondences across two databases. -
Studying Social Tagging and Folksonomy: a Review and Framework
Studying Social Tagging and Folksonomy: A Review and Framework Item Type Journal Article (On-line/Unpaginated) Authors Trant, Jennifer Citation Studying Social Tagging and Folksonomy: A Review and Framework 2009-01, 10(1) Journal of Digital Information Journal Journal of Digital Information Download date 02/10/2021 03:25:18 Link to Item http://hdl.handle.net/10150/105375 Trant, Jennifer (2009) Studying Social Tagging and Folksonomy: A Review and Framework. Journal of Digital Information 10(1). Studying Social Tagging and Folksonomy: A Review and Framework J. Trant, University of Toronto / Archives & Museum Informatics 158 Lee Ave, Toronto, ON Canada M4E 2P3 jtrant [at] archimuse.com Abstract This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical frameworks). Keywords: Social tagging, folksonomy, tagging, literature review, research review 1. Introduction User-generated keywords – tags – have been suggested as a lightweight way of enhancing descriptions of on-line information resources, and improving their access through broader indexing. “Social Tagging” refers to the practice of publicly labeling or categorizing resources in a shared, on-line environment. -
Metadata Creation Practices in Digital Repositories and Collections
Metadata Creation Practices in Digital Repositories and Collections: Schemata, Jung-ran Park and Selection Criteria, and Interoperability Yuji Tosaka This study explores the current state of metadata-creation development of such a mediation mechanism calls for practices across digital repositories and collections by an empirical assessment of various issues surrounding metadata-creation practices. using data collected from a nationwide survey of mostly The critical issues concerning metadata practices cataloging and metadata professionals. Results show across distributed digital collections have been rela- that MARC, AACR2, and LCSH are the most widely tively unexplored. While examining learning objects and used metadata schema, content standard, and subject- e-prints communities of practice, Barton, Currier, and Hey point out the lack of formal investigation of the metadata- controlled vocabulary, respectively. Dublin Core (DC) is creation process.2 As will be discussed in the following the second most widely used metadata schema, followed section, some researchers have begun to assess the current by EAD, MODS, VRA, and TEI. Qualified DC’s wider state of descriptive practices, metadata schemata, and use vis-à-vis Unqualified DC (40.6 percent versus 25.4 content standards. However, the literature has not yet developed to a point where it affords a comprehensive percent) is noteworthy. The leading criteria in selecting picture. Given the propagation of metadata projects, it is metadata and controlled-vocabulary schemata are collec- important to continue to track changes in metadata-cre- tion-specific considerations, such as the types of resources, ation practices while they are still in constant flux. Such efforts are essential for adding new perspectives to digital nature of the collection, and needs of primary users and library research and practices in an environment where communities. -
Geotagging Photos to Share Field Trips with the World During the Past Few
Geotagging photos to share field trips with the world During the past few years, numerous new online tools for collaboration and community building have emerged, providing web-users with a tremendous capability to connect with and share a variety of resources. Coupled with this new technology is the ability to ‘geo-tag’ photos, i.e. give a digital photo a unique spatial location anywhere on the surface of the earth. More precisely geo-tagging is the process of adding geo-spatial identification or ‘metadata’ to various media such as websites, RSS feeds, or images. This data usually consists of latitude and longitude coordinates, though it can also include altitude and place names as well. Therefore adding geo-tags to photographs means adding details as to where as well as when they were taken. Geo-tagging didn’t really used to be an easy thing to do, but now even adding GPS data to Google Earth is fairly straightforward. The basics Creating geo-tagged images is quite straightforward and there are various types of software or websites that will help you ‘tag’ the photos (this is discussed later in the article). In essence, all you need to do is select a photo or group of photos, choose the "Place on map" command (or similar). Most programs will then prompt for an address or postcode. Alternatively a GPS device can be used to store ‘way points’ which represent coordinates of where images were taken. Some of the newest phones (Nokia N96 and i- Phone for instance) have automatic geo-tagging capabilities. These devices automatically add latitude and longitude metadata to the existing EXIF file which is already holds information about the picture such as camera, date, aperture settings etc. -
L Dataspaces Make Data Ntegration Obsolete?
DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles DBKDA 2011 Panel Discussion: Will Dataspaces Make Data Integration Obsolete? Moderator: Fritz Laux, Reutlingen Univ., Germany Panelists: Kazuko Takahashi, Kwansei Gakuin Univ., Japan Lena Strömbäck, Linköping Univ., Sweden Nipun Agarwal, Oracle Corp., USA Christopher Ireland, The Open Univ., UK Fritz Laux, Reutlingen Univ., Germany DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles The Dataspace Idea Space of Data Management Scalable Functionality and Costs far Web Search Functionality virtual Organization pay-as-you-go, Enterprise Dataspaces Admin. Portal Schema Proximity Federated first, DBMS DBMS scient. Desktop Repository Search DBMS schemaless, near unstructured high Semantic Integration low Time and Cost adopted from [Franklin, Halvey, Maier, 2005] DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Inclusive ● Deal with all the data of interest, in whatever form => but semantics matters ● We need access to the metadata! ● derive schema from instances? ● Discovering new data sources => The Münchhausen bootstrap problem? Theodor Hosemann (1807-1875) DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Co-existence -
Folksonomies - Cooperative Classification and Communication Through Shared Metadata
Folksonomies - Cooperative Classification and Communication Through Shared Metadata Adam Mathes Computer Mediated Communication - LIS590CMC Graduate School of Library and Information Science University of Illinois Urbana-Champaign December 2004 Abstract This paper examines user-generated metadata as implemented and applied in two web services designed to share and organize digital me- dia to better understand grassroots classification. Metadata - data about data - allows systems to collocate related information, and helps users find relevant information. The creation of metadata has generally been approached in two ways: professional creation and author creation. In li- braries and other organizations, creating metadata, primarily in the form of catalog records, has traditionally been the domain of dedicated profes- sionals working with complex, detailed rule sets and vocabularies. The primary problem with this approach is scalability and its impracticality for the vast amounts of content being produced and used, especially on the World Wide Web. The apparatus and tools built around professional cataloging systems are generally too complicated for anyone without spe- cialized training and knowledge. A second approach is for metadata to be created by authors. The movement towards creator described docu- ments was heralded by SGML, the WWW, and the Dublin Core Metadata Initiative. There are problems with this approach as well - often due to inadequate or inaccurate description, or outright deception. This paper examines a third approach: user-created metadata, where users of the documents and media create metadata for their own individual use that is also shared throughout a community. 1 The Creation of Metadata: Professionals, Con- tent Creators, Users Metadata is often characterized as “data about data.” Metadata is information, often highly structured, about documents, books, articles, photographs, or other items that is designed to support specific functions. -
XML Metadata Standards and Topic Maps Outline
XML Metadata Standards and Topic Maps Erik Wilde 16.7.2001 XML Metadata Standards and Topic Maps 1 Outline what is XML? z a syntax (not a data model!) what is the data model behind XML? z XML Information Set (basically, trees...) what can be described with XML? z describing the content syntactically (schemas) z describing the content abstractly (metadata) XML metadata is outside of XML documents ISO Topic Maps z a "schema language" for meta data 16.7.2001 XML Metadata Standards and Topic Maps 2 1 Extensible Markup Language standardized by the W3C in February 1998 a subset (aka profile) of SGML (ISO 8879) coming from a document world z data are documents defined in syntax z no abstract data model problems in many real-world scenarios z how to compare XML documents z attribute order, white space, namespace prefixes, ... z how to search for data within documents z query languages operate on abstract data models z often data are not documents 16.7.2001 XML Metadata Standards and Topic Maps 3 Why XML at all? because it's simple z easily understandable, human-readable because of the available tools z it's easy to find (free) XML software because of improved interoperability z all others do it! z easy to interface with other XML applications because it's versatile z the data model behind XML is very versatile 16.7.2001 XML Metadata Standards and Topic Maps 4 2 XML Information Set several XML applications need a data model z style sheets for XML (CSS, XSL) z interfaces to programming languages (DOM) z XML transformation languages (XSLT) z XML -
Learning to Match Ontologies on the Semantic Web
The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologies on the Semantic Web AnHai Doan1, Jayant Madhavan2, Robin Dhamankar1, Pedro Domingos2, Alon Halevy2 1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA fanhai,[email protected] 2 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA fjayant,pedrod,[email protected] Received: date / Revised version: date Abstract On the Semantic Web, data will inevitably come and much of the potential of the Web has so far remained from many different ontologies, and information processing untapped. across ontologies is not possible without knowing the seman- In response, researchers have created the vision of the Se- tic mappings between them. Manually finding such mappings mantic Web [BLHL01], where data has structure and ontolo- is tedious, error-prone, and clearly not possible at the Web gies describe the semantics of the data. When data is marked scale. Hence, the development of tools to assist in the ontol- up using ontologies, softbots can better understand the se- ogy mapping process is crucial to the success of the Seman- mantics and therefore more intelligently locate and integrate tic Web. We describe GLUE, a system that employs machine data for a wide variety of tasks. The following example illus- learning techniques to find such mappings. Given two on- trates the vision of the Semantic Web. tologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well- founded probabilistic definitions to several practical similar- Example 1 Suppose you want to find out more about some- ity measures, and show that GLUE can work with all of them.