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Semantic Network Analysis in Social Sciences

Author : Elad Segev

Publisher : Routledge

Page : 223 pages

File Size : 26,8 MB

Release : 2021-11-29

Category : Psychology

ISBN : 1000471918

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Book Description

Semantic Network Analysis in Social Sciences introduces the fundamentals of semantic network analysis and its applications in the social sciences. Readers learn how to easily transform any given text into a visual network of words co-occurring together, a process that allows mapping the main themes appearing in the text and revealing its main narratives and biases. Semantic network analysis is particularly useful today with the increasing volumes of text-based information available. It is one of the developing, cutting-edge methods to organize, identify patterns and structures, and understand the meanings of our information society. The first chapters in this book offer step-by-step guidelines for conducting semantic network analysis, including choosing and preparing the text, selecting desired words, constructing the networks, and interpreting their meanings. Free software tools and code are also presented. The rest of the book displays state-of-the-art studies from around the world that apply this method to explore news, political speeches, social media content, and even to organize interview transcripts and literature reviews. Aimed at scholars with no previous knowledge in the field, this book can be used as a main or a supplementary textbook for general courses on research methods or network analysis courses, as well as a starting point to conduct your own content analysis of large texts.



Principles of Semantic Networks

Author : John F. Sowa

Publisher : Morgan Kaufmann

Page : 595 pages

File Size : 32,99 MB

Release : 2014-07-10

Category : Computers

ISBN : 1483221148

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Book Description

Principles of Semantic Networks: Explorations in the Representation of Knowledge provides information pertinent to the theory and applications of semantic networks. This book deals with issues in knowledge representation, which discusses theoretical topics independent of particular implementations. Organized into three parts encompassing 19 chapters, this book begins with an overview of semantic network structure for representing knowledge as a pattern of interconnected nodes and arcs. This text then analyzes the concepts of subsumption and taxonomy and synthesizes a framework that integrates many previous approaches and goes beyond them to provide an account of abstract and partially defines concepts. Other chapters consider formal analyses, which treat the methods of reasoning with semantic networks and their computational complexity. This book discusses as well encoding linguistic knowledge. The final chapter deals with a formal approach to knowledge representation that builds on ideas originating outside the artificial intelligence literature in research on foundations for programming languages. This book is a valuable resource for mathematicians.



Semantic Network

Author : Fouad Sabry

Publisher : One Billion Knowledgeable

Page : 121 pages

File Size : 25,88 MB

Release : 2023-06-26

Category : Computers

ISBN :

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Book Description

What Is Semantic Network A knowledge base that depicts the semantic relations that exist between concepts in a network is known as a semantic network, also known as a frame network. This is a form of knowledge representation that is frequently put to use. It can be either directed or undirected and consists of vertices, which represent concepts, and edges, which reflect semantic relations between concepts, mapping or linking semantic fields. Vertices are used to represent concepts. Edges represent semantic interactions. A semantic network can be "instantiated" in a variety of different ways, such as a concept map or a graph database. Semantic triples are the typical way that typical standardized semantic networks are expressed. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Semantic Network Chapter 2: Knowledge Representation and Reasoning Chapter 3: Semantic Web Chapter 4: Ontology (Computer Science) Chapter 5: John F. Sowa Chapter 6: Conceptual Graph Chapter 7: Semantic Similarity Chapter 8: Semantic Research Chapter 9: Semantic Data Model Chapter 10: Knowledge Graph (II) Answering the public top questions about semantic network. (III) Real world examples for the usage of semantic network in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of semantic network' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of semantic network.



Complex Network Analysis in Python

Author : Dmitry Zinoviev

Publisher : Pragmatic Bookshelf

Page : 330 pages

File Size : 19,29 MB

Release : 2018-01-19

Category : Computers

ISBN : 1680505408

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Book Description

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.



Social Networks and the Semantic Web

Author : Peter Mika

Publisher : Springer Science & Business Media

Page : 237 pages

File Size : 17,24 MB

Release : 2007-10-23

Category : Computers

ISBN : 0387710019

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Book Description

Social Networks and the Semantic Web offers valuable information to practitioners developing social-semantic software for the Web. It provides two major case studies. The first case study shows the possibilities of tracking a research community over the Web. It reveals how social network mining from the web plays an important role for obtaining large scale, dynamic network data beyond the possibilities of survey methods. The second case study highlights the role of the social context in user-generated classifications in content, such as the tagging systems known as folksonomies.



A Developer’s Guide to the Semantic Web

Author : Liyang Yu

Publisher : Springer

Page : 841 pages

File Size : 26,82 MB

Release : 2014-12-02

Category : Computers

ISBN : 3662437961

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Book Description

The Semantic Web represents a vision for how to make the huge amount of information on the Web automatically processable by machines on a large scale. For this purpose, a whole suite of standards, technologies and related tools have been specified and developed over the last couple of years and they have now become the foundation for numerous new applications. A Developer’s Guide to the Semantic Web helps the reader to learn the core standards, key components and underlying concepts. It provides in-depth coverage of both the what-is and how-to aspects of the Semantic Web. From Yu’s presentation, the reader will obtain not only a solid understanding about the Semantic Web, but also learn how to combine all the pieces to build new applications on the Semantic Web. The second edition of this book not only adds detailed coverage of the latest W3C standards such as SPARQL 1.1 and RDB2RDF, it also updates the readers by following recent developments. More specifically, it includes five new chapters on schema.org and semantic markup, on Semantic Web technologies used in social networks and on new applications and projects such as data.gov and Wikidata and it also provides a complete coding example of building a search engine that supports Rich Snippets. Software developers in industry and students specializing in Web development or Semantic Web technologies will find in this book the most complete guide to this exciting field available today. Based on the step-by-step presentation of real-world projects, where the technologies and standards are applied, they will acquire the knowledge needed to design and implement state-of-the-art applications.



EuroWordNet: A multilingual database with lexical semantic networks

Author : Piek Vossen

Publisher : Springer Science & Business Media

Page : 180 pages

File Size : 10,25 MB

Release : 2013-11-11

Category : Computers

ISBN : 9401714916

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Book Description

This book describes the main objective of EuroWordNet, which is the building of a multilingual database with lexical semantic networks or wordnets for several European languages. Each wordnet in the database represents a language-specific structure due to the unique lexicalization of concepts in languages. The concepts are inter-linked via a separate Inter-Lingual-Index, where equivalent concepts across languages should share the same index item. The flexible multilingual design of the database makes it possible to compare the lexicalizations and semantic structures, revealing answers to fundamental linguistic and philosophical questions which could never be answered before. How consistent are lexical semantic networks across languages, what are the language-specific differences of these networks, is there a language-universal ontology, how much information can be shared across languages? First attempts to answer these questions are given in the form of a set of shared or common Base Concepts that has been derived from the separate wordnets and their classification by a language-neutral top-ontology. These Base Concepts play a fundamental role in several wordnets. Nevertheless, the database may also serve many practical needs with respect to (cross-language) information retrieval, machine translation tools, language generation tools and language learning tools, which are discussed in the final chapter. The book offers an excellent introduction to the EuroWordNet project for scholars in the field and raises many issues that set the directions for further research in semantics and knowledge engineering.



Semantic Network Analysis

Author : Wouter van Atteveldt

Publisher :

Page : 256 pages

File Size : 32,71 MB

Release : 2008

Category : Social Science

ISBN :

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Book Description

This books describes a number of techniques that have been developed to facilitate Semantic Network Analysis. It describes techniques to automatically extract networks using co-occurrence, grammatical analysis, and sentiment analysis using machine learning. Additionally, it describes techniques to represent the extracted semantic networks and background knowledge about the actors and issues in the network, using Semantic Web techniques to deal with multiple issue categorisations and political roles and functions that shift over time. It shows how this combined network of message content and background knowledge can be queried and visualized to make it easy to answer a variety of research questions. Finally, this book describes the AmCAT infrastructure and iNet coding program for that have been developed to facilitate managing large automatic and manual content analysis projects.



Semantic Web for the Working Ontologist

Author : Dean Allemang

Publisher : Elsevier

Page : 369 pages

File Size : 47,98 MB

Release : 2011-07-05

Category : Computers

ISBN : 0123859662

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Book Description

Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL, Second Edition, discusses the capabilities of Semantic Web modeling languages, such as RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language). Organized into 16 chapters, the book provides examples to illustrate the use of Semantic Web technologies in solving common modeling problems. It uses the life and works of William Shakespeare to demonstrate some of the most basic capabilities of the Semantic Web. The book first provides an overview of the Semantic Web and aspects of the Web. It then discusses semantic modeling and how it can support the development from chaotic information gathering to one characterized by information sharing, cooperation, and collaboration. It also explains the use of RDF to implement the Semantic Web by allowing information to be distributed over the Web, along with the use of SPARQL to access RDF data. Moreover, the reader is introduced to components that make up a Semantic Web deployment and how they fit together, the concept of inferencing in the Semantic Web, and how RDFS differs from other schema languages. Finally, the book considers the use of SKOS (Simple Knowledge Organization System) to manage vocabularies by taking advantage of the inferencing structure of RDFS-Plus. This book is intended for the working ontologist who is trying to create a domain model on the Semantic Web. - Updated with the latest developments and advances in Semantic Web technologies for organizing, querying, and processing information, including SPARQL, RDF and RDFS, OWL 2.0, and SKOS - Detailed information on the ontologies used in today's key web applications, including ecommerce, social networking, data mining, using government data, and more - Even more illustrative examples and case studies that demonstrate what semantic technologies are and how they work together to solve real-world problems



Associative Networks

Author : Nicholas V. Findler

Publisher : Academic Press

Page : 481 pages

File Size : 21,43 MB

Release : 2014-05-10

Category : Reference

ISBN : 1483263010

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Book Description

Associative Networks: Representation and Use of Knowledge by Computers is a collection of papers that deals with knowledge base of programs exhibiting some operational aspects of understanding. One paper reviews network formalism that utilizes unobstructed semantics, independent of the domain to which it is applied, that is also capable of handling significant epistemological relationships of concept structuring, attribute/value inheritance, multiple descriptions. Another paper explains network notations that encode taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, as well as the relationships between syntactic units and their interpretations. One paper shows that networks can be designed to be intuitively and formally interpretable. Network formalisms are computer-oriented logics which become distinctly significant when access paths from concepts to propositions are built into them. One feature of a topical network organization is its potential for learning. If one topic is too large, it could be broken down where groupings of propositions under the split topics are then based on "co-usage" statistics. As an example, one paper cites the University of Maryland artificial intelligence (AI) group which investigates the control and interaction of a meaning-based parser. The group also analyzes the inferences and predictions from a number of levels based on mundane inferences of actions and causes that can be used in AI. The collection can be useful for computer engineers, computer programmers, mathematicians, and researchers who are working on artificial intelligence.