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Entity Resolution and Information Quality

Author : John R. Talburt

Publisher : Elsevier

Page : 254 pages

File Size : 33,15 MB

Release : 2011-01-14

Category : Computers

ISBN : 0123819733

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Book Description

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 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 Algebraic Model for Entity Resolution, which are the major theoretical models that support Entity Resolution. In relation to this, the book briefly discusses entity-based data integration (EBDI) and its model, which serve as an extension of the Algebraic Model for Entity Resolution. There is also an explanation of how the three commercial ER systems operate and a description of the non-commercial open-source system known as OYSTER. The book concludes by discussing trends in entity resolution research and practice. Students taking IT courses and IT professionals will find this book invaluable. - First authoritative reference explaining entity resolution and how to use it effectively - Provides practical system design advice to help you get a competitive advantage - Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.



Entity Resolution in the Web of Data

Author : Vassilis Christophides

Publisher : Springer Nature

Page : 106 pages

File Size : 13,40 MB

Release : 2022-05-31

Category : Mathematics

ISBN : 3031794680

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Book Description

In recent years, several knowledge bases have been built to enable large-scale knowledge sharing, but also an entity-centric Web search, mixing both structured data and text querying. These knowledge bases offer machine-readable descriptions of real-world entities, e.g., persons, places, published on the Web as Linked Data. However, due to the different information extraction tools and curation policies employed by knowledge bases, multiple, complementary and sometimes conflicting descriptions of the same real-world entities may be provided. Entity resolution aims to identify different descriptions that refer to the same entity appearing either within or across knowledge bases. 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, the semantic and structural diversity of the descriptions provided across domains even for the same real-world entities, as well as the autonomy of knowledge bases in terms of adopted processes for creating and curating entity descriptions. The scale, diversity, and graph structuring of entity descriptions in the Web of data essentially challenge how two descriptions can be effectively compared for similarity, but also how resolution algorithms can efficiently avoid examining pairwise all descriptions. The book covers a wide spectrum of entity resolution issues at the Web scale, including basic concepts and data structures, main resolution tasks and workflows, as well as state-of-the-art algorithmic techniques and experimental trade-offs.



Data Matching

Author : Peter Christen

Publisher : Springer Science & Business Media

Page : 279 pages

File Size : 13,96 MB

Release : 2012-07-04

Category : Computers

ISBN : 3642311644

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Book Description

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. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.



Entity Resolution in the Web of Data

Author : Vassilis Christophides

Publisher : Morgan & Claypool Publishers

Page : 124 pages

File Size : 32,89 MB

Release : 2015-08-01

Category : Computers

ISBN : 1627058044

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Book Description

In recent years, several knowledge bases have been built to enable large-scale knowledge sharing, but also an entity-centric Web search, mixing both structured data and text querying. These knowledge bases offer machine-readable descriptions of real-world entities, e.g., persons, places, published on the Web as Linked Data. However, due to the different information extraction tools and curation policies employed by knowledge bases, multiple, complementary and sometimes conflicting descriptions of the same real-world entities may be provided. Entity resolution aims to identify different descriptions that refer to the same entity appearing either within or across knowledge bases. 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, the semantic and structural diversity of the descriptions provided across domains even for the same real-world entities, as well as the autonomy of knowledge bases in terms of adopted processes for creating and curating entity descriptions. The scale, diversity, and graph structuring of entity descriptions in the Web of data essentially challenge how two descriptions can be effectively compared for similarity, but also how resolution algorithms can efficiently avoid examining pairwise all descriptions. The book covers a wide spectrum of entity resolution issues at the Web scale, including basic concepts and data structures, main resolution tasks and workflows, as well as state-of-the-art algorithmic techniques and experimental trade-offs.



Unstructured Data Analysis

Author : Matthew Windham

Publisher : SAS Institute

Page : 166 pages

File Size : 44,9 MB

Release : 2018-09-14

Category : Computers

ISBN : 1635267099

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Book Description

Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.



Knowledge Graphs and Big Data Processing

Author : Valentina Janev

Publisher : Springer Nature

Page : 212 pages

File Size : 12,85 MB

Release : 2020-07-15

Category : Computers

ISBN : 3030531996

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Book Description

This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.



Innovative Techniques and Applications of Entity Resolution

Author : Wang, Hongzhi

Publisher : IGI Global

Page : 433 pages

File Size : 42,28 MB

Release : 2014-02-28

Category : Computers

ISBN : 1466651997

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Book Description

Entity resolution is an essential tool in processing and analyzing data in order to draw precise conclusions from the information being presented. Further research in entity resolution is necessary to help promote information quality and improved data reporting in multidisciplinary fields requiring accurate data representation. Innovative Techniques and Applications of Entity Resolution draws upon interdisciplinary research on tools, techniques, and applications of entity resolution. This research work provides a detailed analysis of entity resolution applied to various types of data as well as appropriate techniques and applications and is appropriately designed for students, researchers, information professionals, and system developers.



Domain-Specific Knowledge Graph Construction

Author : Mayank Kejriwal

Publisher : Springer

Page : 115 pages

File Size : 22,24 MB

Release : 2019-03-04

Category : Computers

ISBN : 3030123758

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Book Description

The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as an accessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.



Web Data Mining

Author : Bing Liu

Publisher : Springer Science & Business Media

Page : 637 pages

File Size : 14,45 MB

Release : 2011-06-25

Category : Computers

ISBN : 3642194605

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Book Description

Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.



The Four Generations of Entity Resolution

Author : George Papadakis

Publisher : Springer Nature

Page : 152 pages

File Size : 49,17 MB

Release : 2022-06-01

Category : Computers

ISBN : 3031018788

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Book Description

Entity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of the research examines ways for improving its effectiveness and time efficiency. The initial ER methods primarily target Veracity in the context of structured (relational) data that are described by a schema of well-known quality and meaning. To achieve high effectiveness, they leverage schema, expert, and/or external knowledge. Part of these methods are extended to address Volume, processing large datasets through multi-core or massive parallelization approaches, such as the MapReduce paradigm. However, these early schema-based approaches are inapplicable to Web Data, which abound in voluminous, noisy, semi-structured, and highly heterogeneous information. To address the additional challenge of Variety, recent works on ER adopt a novel, loosely schema-aware functionality that emphasizes scalability and robustness to noise. Another line of present research focuses on the additional challenge of Velocity, aiming to process data collections of a continuously increasing volume. The latest works, though, take advantage of the significant breakthroughs in Deep Learning and Crowdsourcing, incorporating external knowledge to enhance the existing words to a significant extent. This synthesis lecture organizes ER methods into four generations based on the challenges posed by these four Vs. For each generation, we outline the corresponding ER workflow, discuss the state-of-the-art methods per workflow step, and present current research directions. The discussion of these methods takes into account a historical perspective, explaining the evolution of the methods over time along with their similarities and differences. The lecture also discusses the available ER tools and benchmark datasets that allow expert as well as novice users to make use of the available solutions.