Semantic Network Analysis in Social Sciences
Semantic Network Analysis in Social Sciences

Author: Elad Segev

Publisher: Routledge

Published: 2021-11-29

Total Pages: 223

ISBN-13: 1000471918

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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.

Semantic Network Analysis
Semantic Network Analysis

Author: Wouter van Atteveldt

Publisher:

Published: 2008

Total Pages: 256

ISBN-13:

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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.

Principles of Semantic Networks
Principles of Semantic Networks

Author: John F. Sowa

Publisher: Morgan Kaufmann

Published: 2014-07-10

Total Pages: 595

ISBN-13: 1483221148

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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.

Complex Network Analysis in Python
Complex Network Analysis in Python

Author: Dmitry Zinoviev

Publisher: Pragmatic Bookshelf

Published: 2018-01-19

Total Pages: 330

ISBN-13: 1680505408

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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
Social Networks and the Semantic Web

Author: Peter Mika

Publisher: Springer Science & Business Media

Published: 2007-10-23

Total Pages: 237

ISBN-13: 0387710019

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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.

Advances in Quantitative Ethnography
Advances in Quantitative Ethnography

Author: Brendan Eagan

Publisher: Springer Nature

Published: 2019-10-12

Total Pages: 368

ISBN-13: 3030332322

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This book constitutes the refereed proceedings of the First International Conference on Quantitative Ethnography, ICQE 2019, held in Madison, Wisconsin, USA, in October 2019. It consists of 23 full and 9 short carefully reviewed papers selected from 52 submissions. The contributions come from a diverse range of fields and perspectives, including learning analytics, history, and systems engineering, all attempting to understand the breadth of human behavior using quantitative ethnographic approaches.

Semantic Networks in Artificial Intelligence
Semantic Networks in Artificial Intelligence

Author: Fritz W. Lehmann

Publisher: Pergamon

Published: 1992

Total Pages: 776

ISBN-13:

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Hardbound. Semantic Networks are graphic structures used to represent concepts and knowledge in computers. Key uses include natural language understanding, information retrieval, machine vision, object-oriented analysis and dynamic control of combat aircraft. This major collection addresses every level of reader interested in the field of knowledge representation. Easy to read surveys of the main research families, most written by the founders, are followed by 25 widely varied articles on semantic networks and the conceptual structure of the world. Some extend ideas of philosopher Charles S Peirce 100 years ahead of his time. Others show connections to databases, lattice theory, semiotics, real-world ontology, graph-grammers, lexicography, relational algebras, property inheritance and semantic primitives. Hundreds of pictures show semantic networks as a visual language of thought.

Semantic Systems. The Power of AI and Knowledge Graphs
Semantic Systems. The Power of AI and Knowledge Graphs

Author: Maribel Acosta

Publisher: Springer Nature

Published: 2019-11-04

Total Pages: 400

ISBN-13: 3030332209

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This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies.

The Oxford Handbook of Political Networks
The Oxford Handbook of Political Networks

Author: Jennifer Nicoll Victor

Publisher: Oxford University Press

Published: 2018

Total Pages: 1011

ISBN-13: 0190228210

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Politics is intuitively about relationships, but until recently the network perspective has not been a dominant part of the methodological paradigm that political scientists use to study politics. This volume is a foundational statement about networks in the study of politics.

Semantic Networks for Understanding Scenes
Semantic Networks for Understanding Scenes

Author: Gerhard Sagerer

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 507

ISBN-13: 1489919139

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Figure 1.1. An outdoor scene "A bus is passing three cars which are parking between trees at the side of the road. Houses having two storeys are lined up at the street. 3 4 Introduction Figure 1.2. An assembly scene There seems to be a small open place between the group of houses in the foreground and the store in the background". In such or a similar way the content of the natural scene shown above can be described. It is quite easy to give such a short description. The problem is somewhat more complex for the second image. First of all, it can be stated that the image does not show an everyday scene. It appears as a kind of man made surrounding. But everyone can accept the following statements about this image: 1. The image shows a snapshot of an assembly line. 2. The robot in front is screwing. 3. There is no person in the working area of the robots. 4. All objects on the conveyor belt are worked on by robots. There are no free objects on the belt.