Entity resolution and information quality

by John R. Talburt

Publisher: Morgan Kaufmann/Elsevier in San Francisco, Calif

Written in English
Published: Pages: 235 Downloads: 852
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Subjects:

  • Management,
  • Customer relations,
  • Data processing,
  • Data mining

Edition Notes

Includes bibliographic references (p. 191-201) and index.

Statementby John R. Talburt
Classifications
LC ClassificationsQA76.9.D343 T348 2011
The Physical Object
Paginationxviii, 235 p. :
Number of Pages235
ID Numbers
Open LibraryOL25332972M
ISBN 100123819725
ISBN 109780123819727
LC Control Number2012405311
OCLC/WorldCa660519081

Among the topics are subjective information quality in data integrations, trends and research of wikis' quality and governance based on bibliometric and content analysis, principles reference data management for business intelligence, whether agile information management governance can be scaled to the enterprise, strategies for large-scale entity resolution base on inverted index data. Professor Talburt holds several patents related to customer data integration and the author of numerous articles on information quality and entity resolution, and is the author of Entity Resolution and Information Quality (Morgan Kaufmann, ). He also holds the IAIDQ Information Quality Certified Professional (IQCP) credential. Filed under: Business Intelligence, Business Rules, Data Quality The other day I had a conversation about product master data, and one of the participants, almost as .   Entity Resolution is fundamental to intelligence — any form of intelligence, human intelligence, machine intelligence, or otherwise. I’ve recently brought my Senzing company out of : Jeff Jonas.

Entity resolution (ER), the problem of extracting, match-ing and resolving entity mentions in structured and unstruc-tured data, is a long-standing challenge in database man-agement, information retrieval, machine learning, natural language processing and statistics. Ironically, different sub-. News. Jan. Our paper on Pay-As-You-Go ER has been accepted to the IEEE Transactions on Knowledge and Data Engineering. Overview. The goal of the SERF project is to develop a generic infrastructure for Entity Resolution (ER). ER (also known as deduplication, or record linkage) is an important information integration problem: The same "real-world entities" (e.g., customers, or products. Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big datas impact on MDM and the critical role of entity information management system (EIMS) in successful MDM.

Download OYSTER Entity Resolution for free. OYSTER is an Entity Resolution engine. Entity Resolution is the process by which a dataset is processed and records are identified that represent the same real-world entity. OYSTER (Open sYSTem Entity Resolution) is an entity resolution system that supports probabilistic direct matching, transitive linking, and asserted linking.5/5. Entity resolution is a vital device in processing and analyzing data in order to draw actual conclusions from the information being launched. Further evaluation in entity resolution is necessary to help promote information high high quality and improved data reporting in . The term Entity Resolution (ER) has only been in use for a few years, but the concept has been around since information systems have been in use. Sometimes called record de-duplication, record matching, record linking, merge-purge, or the co-reference problem, ER is the process of determining if two references to real-world objects are.   A named entity is a real world object which can be denoted through a proper name. [1] Named entity can be persons, organisations, countries, currencies etc. When we look at text in the form of sentences or paragraphs, different entities may be men.

Entity resolution and information quality by John R. Talburt Download PDF EPUB FB2

In short, Entity Resolution and Information Quality gives you the applied level know-how you need to aggregate data from disparate sources and form accurate customer and product profiles that support effective marketing and sales. It is an invaluable guide for succeeding in today s info-centric by: Entity Resolution and Information Quality - Kindle edition by Talburt, John R.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Entity Resolution and Information Quality/5(4). The book includes chapters that cover the principles of entity resolution and the principles of Information Quality, in addition to their concepts and terminology.

It also discusses the Fellegi-Sunter theory of record linkage, the Stanford Entity Resolution Framework, and the. In short, Entity Resolution and Information Quality gives you the applied level know-how you need to aggregate data from disparate sources and form accurate customer and product profiles that support effective marketing and sales.

It is an invaluable guide for succeeding in today s info-centric environment. Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality.

It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for Information and Data Quality {IAIDQ).Pages: Terry Talley, in Entity Resolution and Information Quality, Entity resolution is the process of probabilistically identifying some real thing based upon a set of possibly ambiguous clues.

Humans have been performing entity resolution throughout history. Early humans looked at footprints and tried to match that clue to the animals that made Entity resolution and information quality book tracks. Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality.

It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for.

Entity Resolution and Information Quality - Ebook written by John R. Talburt. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Entity Resolution and Information Quality.5/5(1).

Principles of Entity Resolution --Principles of Information Quality --Entity Resolution Models --Entity-Based Data Integration --Entity Resolution Systems --The OYSTER Project --Trends in Entity Resolution Research and Applications.

Responsibility: by John R. Talburt. mation quality to this book. Information quality and entity resolution are closely related and John, along with Rich Wang from MIT, were the driving forces behind the creation of the Information Quality program at the University of Arkansas at Lit-tle Rock (UALR).

This was the first program of its kind in theFile Size: KB. This well-written book is a welcome guide to concepts, terminologies, methods, and algorithms used in the emerging information science disciplines of Entity Resolution and Information Quality (ERIQ). Although written in a textbook format, it's appropriate and accessible to anyone interested in the two disciplines who have some familiarity with /5(4).

Entity Resolution and Information Quality presents subjects and definitions, and clarifies complicated terminologies relating to entity resolution and info high quality.

It takes a really broad view of IQ, together with its six-area framework and the talents shaped by the Worldwide Affiliation for Information and Knowledge Quality IAIDQ). Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality.

It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for Information and Data Quality {IAIDQ).

The book includes chapters that cover the principles of entity. Entity resolution and information quality. [John R Talburt] -- Customers and products are the heart of any business, and corporations collect more data about them every year.

eliminating duplications - and making crucial business decisions based on the results. This book is an authoritative, vendor-independent technical reference for. "Talburt, the author of this book, is one of the organizers of the first graduate degree program in information quality, hosted by the University of Arkansas at Little Rock.

The book contains seven easy-to-read chapters. A chapter on trends and research topics. In short, Entity Resolution and Information Quality gives you the applied level know-how you need to aggregate data from disparate sources and form accurate customer and product profiles that Author: John Talburt.

• Book / Survey Articles – Data Quality and Record Linkage Techniques – Evaluation of Entity Resolution Approached on Real‐world Match Problems resolution relationships: entity relationships. PART 2 DATA PREPARATION& ‐a. The Center for Advanced Research in Entity Resolution and InformationQuality (ERIQ) was established to advance research and best practices in the areas of.

Entity Resolution and Information Quality: Talburt, John R.: Books - 5/5(1). The book includes chapters that cover the principles of entity resolution and the principles of Information Quality, in addition to their concepts and terminology.

It also discusses the Fellegi-Sunter theory of record linkage, the Stanford Entity Resolution Framework, and the Brand: Elsevier Science.

A Graduate-Level Course on Entity Resolution and Information Quality: A Step toward ER Education. The ACM Journal of Data and Information Quality (JDIQ), Vol 4, No. 2, pp. Osesina, I. & Talburt, J.R. () A data-intensive approach to named entity recognition combining contextual and intrinsic indicators, International Journal of.

Form -Corporate or Entity Resolution Published by Guset User, Description: DOC Page 2 of 5 M (04/16) 1 Is the authorized signer employed by a registered broker-dealer, a securities exchange, or the Financial Industry Regulatory. Record linkage (RL) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).

Record linkage is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in.

Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality.

It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for /5(4).

A Summary of the KDD Tutorial Taught by Dr. Lise Getoor and Dr. Ashwin Machanavajjhala. Entity Resolution is becoming an important discipline in Computer Science and in Big Data, especially with the recent release of Google’s Knowledge Graph and the open Freebase ore it is exceptionally timely that last week at KDDDr.

Lise Getoor of the University Author: Benjamin Bengfort. Adaptive Graphical Approach to Entity Resolution ∗ Zhaoqi Chen Dmitri V. Kalashnikov Sharad Mehrotra Computer Science Department University of California, Irvine ABSTRACT Entity resolution is a very common Information Quality (IQ) problem with many different applications.

In digital libraries, it is related to problems of citation matching. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single.

Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality. It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for Information and Data Quality {IAIDQ).

The book includes chapters that cover the principles of entity. Overview of Entity Resolution: /ch Entity resolution is one of many importation operations for data quality management, information retrieval, and data management.

It has wide applications in. Entity resolution and analysis (ER&A) is a process that helps administrators to gather together a complete body of data about one particular item or object.

It helps solve different problems resulting from data entry errors, aliases, information silos and other issues where redundant data may cause confusion. Entity Resolution and Information Quality [Talburt, ] has been organized as a textbook and includes review questions and exerc ises.

The ER challenge data and.The process includes a multi-phase approach consisting of data-quality analysis, selection of entity-identity attributes for entity resolution, development of a truth-set, and implementation and benchmarking of an entity-resolution rule set using the open source entity-resolution system named by: 1.Entity Resolution in the Web of Data Synthesis Lectures on the Semantic Web: Theory and Technology.

The objective of this book is to present the new entity resolution challenges stemming from the openness of the Web of data in describing entities by an unbounded number of knowledge bases, Journal of Data and Information QualityCited by: