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This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used.

It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques — based on statistics, graph analytics, machine learning, etc.

It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve.

This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout.

Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics. The origins of this book can be traced back to a Dagstuhl Seminar, held in , on the topic of Knowledge Graphs.

Much of the discussion of the seminar centred on this question, and there were divergent opinions as to how knowledge graphs could or should be defined; how they relate to previous concepts such as graph databases, knowledge bases, ontologies, RDF graphs, property graphs, semantic networks, etc.

As the discussion continued, a consensus began to emerge: Knowledge Graphs, as a topic, involves a novel confluence of techniques stemming from previously disparate scientific communities, with the unifying goal of developing novel graph-based techniques for better integrating and extracting value from diverse knowledge sources at large scale.

As a follow-up to the seminar, the attendees agreed that in order to foster this unifying view of Knowledge Graphs, there was a need for a manuscript that would serve as a general introduction to the area.

This manuscript would:. The manuscript would then serve as an introductory text for students, practitioners and researchers new to the area, helping to form a consensus in terms of what is a knowledge graph, laying the foundations for future developments. The goal of preparing this manuscript was an ambitious one, and involved drawing together and distilling down a vast amount of literature on a diverse range of topics into a set of key concepts described in an accessible way.

For this reason, the manuscript has been prepared by many authors, who have lent their knowledge and expertise to the preparation of specific sections. A short version of the manuscript was first published as a tutorial paper [ Hogan et al.

However, there was not enough space to describe all of the important developments in the area. This led us to publish this book, which further includes topics relating to the creation, enrichment, quality assessment, refinement and publication of knowledge graphs, as well as formal definitions, a historical perspective, and extended discussion throughout.

The book is divided into ten chapters. The second chapter presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried. The third chapter describes how the resulting data graph can be enhanced with notions of schema, identity and context.

The fourth chapter discusses how ontologies and rules can be used to encode knowledge, and how they enable deductive forms of reasoning.

The fifth chapter delves into how inductive techniques — based on statistics, graph analytics, machine learning, etc. The sixth chapter is dedicated to techniques for the creation and enrichment of knowledge graphs from legacy sources of data. The seventh chapter enumerates a variety of quality measures that can be used to assess a knowledge graph in terms of its fitness for use in a variety of applications. The eighth chapter presents key methods for the refinement of knowledge graphs, with the goal of improving their completeness and correctness.

The ninth chapter provides a survey of the open and enterprise knowledge graphs that have emerged in recent years, along with the industries within which, and the applications for which, they have been most widely adopted. The tenth chapter wraps up the book with discussion of the current limitations and future directions along which knowledge graphs are likely to evolve.

A key aim of this book is to be accessible to a broader audience. The book aims to motivate and illustrate the various concepts it introduces from a practical perspective, and in order to be as accessible as possible, relies heavily on an example-driven presentation using a graphical notation. For the reader wishing to dig more into the technical minutiae, we complement this discussion with formal definitions throughout; however, the reader more interested in understanding the general concepts and their rationale will find the discussion to be self-contained if they choose to skip the definitions presented in visually distinctive boxes.

The book serves as an entry point for those new to the topic, and may thus serve as a useful textbook for university courses, for researchers who are venturing into the topic for the first time, and for practitioners who wish to understand more about how knowledge graphs might be of use within their company or organisation, or indeed, how to maximise the value of the knowledge graphs that they are currently developing.

Readers who are already active within specific sub-areas of Knowledge Graphs may further appreciate the technical definitions included, the references to other literature provided, and the broader perspective that this book offers in terms of the other related sub-areas and how they complement each other. By drawing together diverse techniques from disparate areas, Knowledge Graphs has become an exciting topic in terms of both research and applications.

We expect to see growing interest on this topic as the years advance, and indeed hope that this book will help to more firmly establish the foundations of this topic, and to foster future developments upon these foundations, potentially by its readers. We also thank those who provided feedback on this content. Hogan was funded by Fondecyt Grant No. The growing industrial uptake of the concept proved difficult for academia to ignore: more and more scientific literature is being published on knowledge graphs, which includes books e.

Underlying all such developments is the core idea of using graphs to represent data, often enhanced with some way to explicitly represent knowledge [ Noy et al. The result is most often used in application scenarios that involve integrating, managing and extracting value from diverse sources of data at large scale [ Noy et al.

Employing a graph-based abstraction of knowledge has numerous benefits in such settings when compared with, for example, a relational model or NoSQL alternatives. Graphs provide a concise and intuitive abstraction for a variety of domains, where edges capture the potentially cyclical relations between the entities inherent in social data, biological interactions, bibliographical citations and co-authorships, transport networks, and so forth [ Angles and Gutierrez, ].

Graphs allow maintainers to postpone the definition of a schema, allowing the data — and its scope — to evolve in a more flexible manner than typically possible in a relational setting, particularly for capturing incomplete knowledge [ Abiteboul, ].

Unlike other NoSQL models, specialised graph query languages support not only standard relational operators joins, unions, projections, etc. Standard knowledge representation formalisms — such as ontologies [ Hitzler et al. Scalable frameworks for graph analytics [ Malewicz et al. Various representations have also been developed that support applying machine learning techniques both directly and indirectly over graphs [ Wang et al.

In summary, the decision to build and use a knowledge graph opens up a range of techniques that can be brought to bear for integrating and extracting value from diverse sources of data at large scale. The goal of this book is to motivate and give a comprehensive introduction to knowledge graphs: to describe their foundational data models and how they can be queried; to discuss representations relating to schema, identity, and context; to discuss deductive and inductive ways to make knowledge explicit; to present a variety of techniques that can be used for the creation and enrichment of graph-structured data; to describe how the quality of knowledge graphs can be discerned and how they can be refined; to discuss standards and best practices by which knowledge graphs can be published; and to provide an overview of existing knowledge graphs found in practice.

Our intended audience includes researchers and practitioners who are new to knowledge graphs. As such, we do not assume that readers have specific expertise on knowledge graphs. Knowledge graph. Herein we adopt an inclusive definition, where we view a knowledge graph as a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities.

The graph of data aka data graph conforms to a graph-based data model, which may be a directed edge-labelled graph , a property graph , etc.

By knowledge , we refer to something that is known. Such knowledge may be accumulated from external sources, or extracted from the knowledge graph itself. Simple statements can be accumulated as edges in the data graph.

If the knowledge graph intends to accumulate quantified statements, a more expressive way to represent knowledge — such as ontologies or rules — is required.

Deductive methods can then be used to entail and accumulate further knowledge e. Additional knowledge — based on simple or quantified statements — can also be extracted from and accumulated by the knowledge graph using inductive methods. Knowledge graphs are often assembled from numerous sources, and as a result, can be highly diverse in terms of structure and granularity. To address this diversity, representations of schema , identity , and context often play a key role, where a schema defines a high-level structure for the knowledge graph, identity denotes which nodes in the graph or in external sources refer to the same real-world entity, while context may indicate a specific setting in which some unit of knowledge is held true.

As aforementioned, effective methods for extraction , enrichment , quality assessment , and refinement are required for a knowledge graph to grow and improve over time. In practice. Knowledge graphs aim to serve as an ever-evolving shared substrate of knowledge within an organisation or community [ Noy et al.

We distinguish two types of knowledge graphs in practice: open knowledge graphs and enterprise knowledge graphs. Open knowledge graphs are published online, making their content accessible for the public good.

The most prominent examples — DBpedia [ Lehmann et al. Open knowledge graphs have also been published within specific domains, such as media [ Raimond et al. Enterprise knowledge graphs are typically internal to a company and applied for commercial use-cases [ Noy et al. Prominent industries using enterprise knowledge graphs include Web search e. Applications include search [ Shrivastava, , Singhal, ], recommendations [ Chang, , Hamad et al.

We will provide more details on the use of knowledge graphs in practice in Chapter Running example. The knowledge graph is managed by a tourism board that aims to increase tourism in the country and promote new attractions in strategic areas.

The knowledge graph itself will eventually describe tourist attractions, cultural events, services, businesses, travel routes, etc. Some applications the organisation envisages are to:. At the foundation of any knowledge graph is the principle of first applying a graph abstraction to data, resulting in an initial data graph.

We now discuss a selection of graph-structured data models that are commonly used in practice to represent data graphs. We then discuss the primitives that form the basis of graph query languages used to interrogate such data graphs. Leaving aside graphs, let us assume that the tourism board from our running example has not yet decided how to model relevant data about attractions, events, services, etc.

The board first considers using a tabular structure — in particular, relational databases — to represent the required data, and though they do not know precisely what data they will need to capture, they begin to design an initial relational schema. They begin with an Event table with five columns:. Event name , venue , type , start , end. But as they start to populate the data, they encounter various issues: events may have multiple names e.

Incrementally addressing these modelling issues as the data become more diverse, they generate internal identifiers for events and adapt their relational schema until they have:.

Along the way, the board has to incrementally change the schema several times in order to support new sources of data. Each such change requires a costly remodelling, reloading, and reindexing of data; here we only considered one table.

The tourism board struggles with the relational model because they do not know, a priori , what data will need to be modelled or what sources they will use. In fact, the refined, flexible schema that the board ends up with — as shown in 2. By instead adopting a graph data model from the outset, the board could forgo the need for an upfront schema, and could define any binary relation between any pair of entities at any time.

We now introduce graph data models popular in practice [ Angles et al. A directed edge-labelled graph sometimes known as a multi-relational graph [ Nickel and Tresp, , Bordes et al.

In the case of knowledge graphs, nodes are used to represent entities and edges are used to represent binary relations between those entities. Figure 2. The graph includes data about the names, types, start and end date-times, and venues for events.



New perspectives on microsoft access 2013 introductory pdf free


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This text provides students with excellent examples and screenshots of fundamental database concepts and skills. The key features of this book are the detailed explanations and the variety of exercises in which students can apply what they new perspectives on microsoft access 2013 introductory pdf free learned.

Matthew Alimagham Spartanburg Community College About This Book This book provides complete coverage of Microsoft Accessand includes the following: Detailed, hands-on instruction of Accessincluding creating and maintaining a database, querying a database, creating forms and reports, integrating Access with other programs, creating macros, writing VBA code, and securing a database Coverage of important database перейти, including guidelines for designing databases and setting field properties, defining table relationships, object dependencies, normalization, and Access naming conventions Exploration of new Access features, including working in Backstage view, using the Data Type gallery to add new fields, applying themes to database objects, creating navigation forms, and using the Macro Designer New for this edition!

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Following guidelines recommended by NAIC, the club members chose Nest Egg as their club name, prepared and approved new perspectives on microsoft access 2013 introductory pdf free partnership agreement and a set of bylaws, registered their club, obtained the club s tax ID, and established an online brokerage account to handle the club s investments.

The 15 permanent club members, each having one vote for all club matters, include 13 individuals and two couples for a total of 17 individuals. The online brokerage account will track the club s investments, but Felicia Rodriquez, the club treasurer, has created a database to handle club accounting for monthly dues and for any future withdrawals.

Felicia s database consists of two tables, tblmember and tblcontribution. Figure 1 shows the structure of the tblmember table, which stores data about each club member.

Each tblmember table record contains a member ID number and each member s first name, last name, address, phone, join date, and participation level the Level field. PaymentID is the table s primary key. MemberID adobe acrobat pro x offline installer free штука a foreign new perspectives on microsoft access 2013 introductory pdf free in the tblcontribution table, and the tblmember table will have a one-tomany relationship with the tblcontribution table.

The other fields in the tblcontribution table are PaidDate and InvestmentAmt. To help Felicia finish her work with the database, complete the following steps: 1. Make sure you have created your copy of the Access New perspectives on microsoft access 2013 introductory pdf free Files and stored them in a trusted folder, and that your computer can access them. Review the tblmember and tblcontribution tables to become familiar with their structures and data. If you are unfamiliar with any property setting, use the Access Help system for an explanation of that property.

For the tblcontribution table, specify PaymentID as the primary key and resize all datasheet columns to their best fit. Modify the first record cardfight vanguard online game for pc the tblmember table datasheet by entering your name, city, state or province two-character postal abbreviationpostal code zip codeand phone number; enter phone numbers in format.

For the last four records, enter the same city, state or province, postal code, and phone area code but enter different phone numbers. Select a second city, state or province, postal code, and phone area code, and then enter these values in recordsusing five different phone numbers. Finally, select a third city, state or province, postal code, and phone area code, and then enter these values in recordsusing five different phone numbers. Resize all datasheet columns to their best fit. Define a one-to-many relationship between the primary tblmember table and the related tblcontribution table, using MemberID as the common field, enforcing referential integrity, and selecting the Cascade Update Related Fields option.

Resize the tblmember field list so that all fields are visible, and then create and print the Relationships for NestEgg report but do not save it. Save the export steps.


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