UNIT-1

Semantic Web

 The Semantic Web is the application of

advanced knowledge technologies to
the Web and distributed systems in
general.
 Information that is missing or hard to
access for our machines can be made
accessible using ontologies.
 Ontologies are formal, which allows a
computer to emulate human ways of
reasoning with knowledge.
 Ontologies carry a social commitment
toward using a set of concepts and
relationships in an agreed way.
 The Semantic Web adds another layer on
the Web architecture that requires
agreements to ensure interoperability.

LIMITATIONS OF THE CURRENT WEB

 finding relevant information

 extracting relevant information
 combining and reusing information

aptation to our primary

interface to the vast information that constitutes
the Web: the search engine.

satisfaction or not at all.

 What’s wrong with the Web?
The questions below are specific. They represent
very general categories of search tasks. In each of
these cases semantic technology would
drastically improve the computer’s ability to
give more appropriate answers.

semantic solutions.

Semantic solutions, often associated with the semantic web, refer

to a set of technologies and approaches designed to make data and
information on the internet more understandable and machine-
readable. These solutions aim to improve the way computers and
software systems interpret, process, and use data by adding context
and meaning to it. Here are the key components and aspects of
semantic solutions:

Semantic Web Technologies:

Semantic solutions leverage a variety of technologies to achieve

their goals. Some of the core technologies include:

Resource Description Framework (RDF):

RDF is a standardized way to describe resources on the web and the

relationships between them. It uses triples (subject-predicate-object) to
represent data and metadata, making it easier to link and relate
information.

Ontologies and Taxonomies:

Ontologies define the relationships and properties of concepts

within a specific domain. They provide a structured framework for
understanding and organizing data. Taxonomies are hierarchical
structures that categorize data into a controlled vocabulary.

SPARQL (SPARQL Protocol and RDF Query Language):

SPARQL is a query language used to retrieve and manipulate data

stored in RDF format. It allows for powerful querying and
reasoning over semantic data.

Linked Data:

Semantic solutions encourage the practice of linking and

interconnecting data from various sources on the web. This
interconnectedness enables computers to traverse links, gather
context, and extract meaningful information.
Data Integration:

Semantic solutions facilitate the integration of heterogeneous data

sources, making it possible to combine and query data from
different formats and domains coherently.

Machine Understanding:

By using ontologies and RDF, semantic solutions provide

machines with a way to understand the semantics, or meaning, of
data. This enables better data processing, reasoning, and inference.

Improved Search and Discovery:

Semantic technologies enhance search engines' ability to

understand user queries and provide more accurate and context-
aware search results. Users can find information more efficiently.

Personalization and Recommendation:

Semantic solutions can be used to create personalized experiences

for users by analyzing their preferences and behaviors to make
tailored content recommendations.

Knowledge Graphs:

Semantic technologies are often used to create knowledge graphs,

which represent knowledge as a network of interconnected concepts
and relationships. Knowledge graphs are used by search engines and
recommendation systems to enhance user experiences.

Applications:

Semantic solutions find applications in various domains, including

healthcare (for semantic interoperability of electronic health records),
e-commerce (for better product recommendations), content
management (for improved categorization and retrieval), and more.

Data Standards:

Standardization is a crucial aspect of semantic solutions. Common

data standards and ontologies ensure that data is structured and
described consistently, allowing for greater interoperability.
Semantic solutions have the potential to significantly improve how
we organize, search, and interact with data on the web and in
various information systems. They enable machines to understand
the meaning
behind data, leading to more intelligent and context-aware
applications and services.

Development of the Semantic Web.

The Semantic Web is an extension of the current web, aiming to make data more
understandable and machine-readable. Here’s a brief overview of its
development:
Origins
 Tim Berners-Lee: The concept was proposed by Tim Berners-Lee in the
late 1990s. He envisioned a web where data could be linked and
understood by machines, facilitating better data integration and
interoperability.
Key Technologies
1. RDF (Resource Description Framework): Introduced to represent
information about resources in a graph format, allowing data to be
connected in a meaningful way.
2. OWL (Web Ontology Language): A more expressive language for
defining complex relationships between concepts and data, enabling
richer semantic relationships.
3. SPARQL: A query language specifically designed for retrieving and
manipulating data stored in RDF format.
Standards and Protocols
 The W3C (World Wide Web Consortium) plays a crucial role in
developing standards for the Semantic Web, promoting interoperability
and the use of linked data.
Linked Data
 Promoted by Berners-Lee, this concept encourages publishing structured
data on the web so it can be easily interlinked and queried, creating a
more interconnected web of data.
Applications
 Knowledge Graphs: Used by search engines like Google to provide
more relevant search results.
 Data Integration: Enhances the ability to integrate data from different
sources, useful in areas like healthcare and scientific research.
 Smart Agents: AI systems that can understand and process information
in a human-like manner.
Current Trends
 Continued growth in AI and machine learning is influencing the Semantic
Web, as these technologies can leverage the structured data to provide
better insights and automation.
  Increasing focus on privacy and ethics in data usage, which will shape
future developments in the Semantic Web.
The Semantic Web aims to create a more intelligent web, where data is not just
presented but understood and utilized in a meaningful way.

Emergency of social web

The social web refers to the evolution of the internet and web
technologies to facilitate social interactions and user-generated
content sharing. This has led to the widespread adoption of social
media platforms and other social networking services. The social
web has significantly impacted the way people communicate, share
information, and connect with others online.

The emergence of the social web can be traced back to the early
2000s with the rise of platforms like Friendster, MySpace, and
Facebook. These websites allow users to create profiles, connect
with friends, and share updates and media. As internet
infrastructure improved, and mobile devices became more
prevalent, the social web's popularity skyrocketed, leading to the
creation of numerous other platforms like Twitter, Instagram,
LinkedIn, Snapchat, and others.

Some key aspects of the social web include:

Social Networking:
Social networking sites allow users to create personal
profiles, connect with friends, family, and colleagues, and interact with
them through messages, comments, and likes.

User-Generated Content:
The social web encourages users to create and share
their content, including photos, videos, articles, and opinions,
enabling a vast and diverse array of user-generated content.

Real-Time Communication:
Instant messaging and real-time updates have become
essential features of the social web, facilitating quick and direct
communication between individuals and groups.
Online Communities:
Social web platforms foster the creation of online
communities based on shared interests, activities, or goals, allowing
users to find like-minded individuals from around the world.

Influence and Virality:

The social web has enabled content to go viral, spreading
rapidly across networks and reaching a large audience in a short
period.

Privacy and Security Concerns:

The social web has also raised concerns about
privacy and data security as users share personal information
and content online.

Impact on Society:
The social web has had a profound impact on society,
politics, and culture, influencing everything from social movements
to marketing strategies.
In summary, the emergence of the social web has fundamentally changed how people interact and share information
on the internet. It has connected individuals globally, reshaped communication patterns, and provided a platform
for expression and engagement on a scale never seen before.

Social Network analysis

Ans: Social networks are platforms that enable individuals to connect, communicate, and share content with each other. They
have become a central part of modern life, influencing everything from personal relationships to business and politics. Here’s
an overview of key aspects:
Types of Social Networks
1. General Social Networks: Platforms like Facebook and Instagram allow users to create profiles, share updates, photos,
and videos, and interact with friends and followers.
2. Professional Networks: LinkedIn focuses on career development, networking, and job searching, enabling users to
connect with industry professionals.
3. Content Sharing Networks: Platforms like YouTube and TikTok center around sharing video content, while others
like Pinterest focus on images and ideas.
4. Microblogging Sites: Twitter allows users to post short updates, follow news, and engage in conversations in real-
time.
5. Discussion Forums: Sites like Reddit facilitate discussions on various topics, allowing users to share insights and
opinions.
Features
 User Profiles: Customizable profiles that showcase personal information, interests, and activities.
 News Feeds: A dynamic stream of updates from connections, pages, or groups that users follow.
 Messaging: Private messaging or commenting features that facilitate direct communication.
 Groups and Communities: Spaces for users to join like-minded individuals around specific interests or topics.
Impact
1. Communication: Social networks have revolutionized how people communicate, enabling instant interactions across
the globe.
2. Information Sharing: They serve as platforms for sharing news, opinions, and information, often influencing public
discourse.
3. Marketing and Business: Businesses leverage social networks for brand promotion, customer engagement, and
targeted advertising, reaching vast audiences.
4. Social Movements: Social media has played a crucial role in organizing and mobilizing social movements, allowing
for rapid dissemination of information and collective action.
Challenges
 Privacy Concerns: Users often share personal information, raising issues about data security and privacy.
 Misinformation: The spread of false information can have serious consequences, affecting public opinion and behavior.
 Mental Health: Studies suggest that excessive use of social media can lead to anxiety, depression, and social isolation.
 Addiction: The design of social networks can encourage compulsive use, impacting productivity and real-life interactions.
Future Trends
 Increased Regulation: Governments and organizations are considering regulations to address privacy and misinformation issues.
 Integration of AI: Enhanced algorithms for content curation, moderation, and user engagement are likely to shape the user
experience.
 Virtual and Augmented Reality: Emerging technologies could create more immersive social networking experiences.
Social networks continue to evolve, shaping how we connect, share, and engage with the world around us. Their influence is
profound, making them an essential aspect of contemporary society.
NETWROK ANALYSIS
Social Network Analysis (SNA) is the study of social relations among a set of actors. The key
difference between network analysis and other approaches to social science is the focus on
relationships between actors rather than the attributes of individual actors. Network analysis
takes a global view on social structures based on the belief that types and patterns of
relationships emerge from individual connectivity and that the presence (or absence) of such
types and patterns have substantial effects on the network and its constituents. In particular, the
network structure provides opportunities and imposes constraints on the individual actors by
determining the transfer or flow of resources (material or immaterial) across the network.
The focus on relationships as opposed to actors can be easily understood by an example. When trying to predict the
performance of individuals in a scientific community by
some measure (say, number of publications), a traditional social science approach would dictate to look at the attributes of the
researchers such as the amount of grants they
attract, their age, the size of the team they belong to etc. A statistical analysis would then proceed by trying to relate these
attributes to the outcome variable, i.e. the number
of publications. In the same context, a network analysis study would focus on the interdependencies within the
research community

DEVELOPMENT OF SOCIAL NETWORK ANALYSIS

The field of Social Network Analysis today is the result of the convergence of several streams of applied
research in sociology, social psychology and anthropology. Many of the concepts of network analysis
have been developed independently by various researchers often through empirical studies of various
social settings.

For example, many social psychologists of the 1940s found a formal description of social groups useful in
depicting communication channels in the group when trying to explain processes of group
communication. Already in the mid-1950s anthropologists have found network representations useful in
generalizing actual field observations, for example when comparing the level of reciprocity in marriage
and other social exchanges across different cultures.

Some of the concepts of network analysis have come naturally from social studies. In an influential early
study at the Hawthorne works in Chicago, researchers from Harvard looked at the workgroup behavior
(e.g. communication, friendships, helping, controversy) at a specific partof the factory, the bank wiring
room. The investigators noticed that workers themselves used specific terms to describe who is in “our
group”.

The researchers tried to understand how such terms arise by reproducing in a visual way the group
structure of the organization as it emerged from the individual relationships of the factory workers.

2. In another study of mixed-race city in the Southern US researchers looked at the network of
overlapping “cliques” defined by race and age.

3. They also went further than the Hawthorne study in generating hypotheses about the possible
connections between cliques.
Despite the various efforts, each of the early studies used a different set of concepts and different methods of
representation and analysis of social networks. However, from the 1950s network analysis began to
converge around the unique world view that distinguishes network analysis from other approaches to
sociological research.

This convergence was facilitated by the adoption of a graph representation of social networks usually credited
to Moreno. What Moreno called a sociogram was a visual representation of social networks as a set of
nodes connected by directed links. The nodes represented individuals in Moreno’s work, while the edges
stood for personal relations. However, similar representations can be used to depict a set of relationships
between any kind of social unit such as groups, organizations, nations etc. While 2D and 3D visual
modeling is still an important technique of network analysis, the sociogram is honored mostly for opening
the way to a formal treatment of network analysis based on graph theory.

The following decades have seen a tremendous increase in the capabilities of network analysis mostly through
new applications. SNA gains its relevance from applications and these settings in turn provide the theories
to be tested and greatly influence the development of the methods and the interpretation of the outcomes.
For example, one of the relatively new areas of network analysis is the analysis of networks in
entrepreneurship, an active area of research that builds and contributes to organization and management
science.

The vocabulary, models and methods of network analysis also expand continuously through applications that
require to handle ever more complex data sets. An example of this process is the advances in dealing
with longitudinal data. New probabilistic models are capable of modeling the evolution of social
networks and answering questions regarding the dynamics of communities. Formalizing an increasing set
of concepts in terms of networks also contributes to both developing and testing theories in more
theoretical branches of sociology.

The increasing variety of applications and related advances in methodology can be best observed at the yearly
Sunbelt Social Networks Conference series, which started in 1980.

4. The field of Social Network Analysis also has a journal of the same name since 1978,
dedicated largely to methodological issues.

5. However, articles describing various applications of social network analysis can be found in almost
any field where networks and relational data play an important role.

While the field of network analysis has been growing steadily from the beginning, there have been two
developments in the last two decades that led to an explosion in network literature. First, advances in
information technology brought a wealth of electronic data and significantly increased analytical power.

Second, the methods of SNA are increasingly applied to networks other than social networks such as the
hyperlink structure on the Web or the electric grid. This advancement —brought forward primarily by
physicists and other natural scientists— is based on the discovery that many networks in nature share a
number of commonalities with social networks.
SIT1610 – Social Network Analysis – Unit I

In the following, we will also talk about networks in general, but it should be clear from the text that
many of the measures in network analysis can only be strictly interpreted in the context of social networks
or have very different interpretation in networks of other kinds.

Fig.6 The upper part shows the location of the workers in the wiring room, while the lower part is a
network image of fights about the windows between workers (W), solderers (S) and inspectors (I).

The term social network “” has been introduced by Barnes in 1954. This convergence was facilitated by
the adoption of a graph representation of social networks called as
Sociogram usually credited to Moreno.
Sociogram was a visual representation of social networks as a set of nodes connected by directed links.
The nodes represented individuals while the edges stood for personal relations. The sociogram is
honored mostly for opening the way to a formal treatment of network analysis based on graph theory.
 The vocabulary, models and methods of network analysis also expand continuously through applications
that require to handle ever more complex data sets.

An example of this process are the advances in dealing with longitudinal data. New probabilistic models
are capable of modeling the evolution of social networks and answering questions regarding the dynamics
of communities.

Formalizing an increasing set of concepts in terms of networks also contributes to both developing and
testing theories in more theoretical branches of sociology.

While the field of network analysis has been growing steadily from the beginning, there have been two
developments in the last two decades that led to an explosion in network literature

First, advances in information technology brought a wealth of electronic data and significantly increased
analytical power.

Second, the methods of SNA are increasingly applied to networks other than social networks such as the
hyperlink structure on the Web or the electric grid

This advancement is based on the discovery that many networks in nature share a number of
commonalities with social networks.

KEY CONCEPTS AND MEASURES IN NETWORK ANALYSIS

Social Network Analysis has developed a set of concepts and methods specific to the analysis of social
networks.

The global structure of networks

A Social network can be represented as a Graph G = (V,E) where V denotes finite set of vertices
and E denoted finite set of Edges.
Each graph can be associated with its characteristic matrix M: =(mi,j)n*n where n =|V|

A component is a maximal connected subgraph. Two vertices are in the same (strong)
component if and only if there exists a (directed) path between them.
American psychologist Stanley Milgram experiment about the structure of social networks.
Milgram calculated the average of the length of the chains and concluded that the experiment
showed that on average Americans are no more than six steps apart from each other. While this
is also the source of the expression six degrees of separation the actual number is rather dubious:
 Fig. 7 Most network analysis methods work on an abstract,
graph based representation of real world networks.
Formally, what Milgram estimated is the size of the average shortest path of the network, which
is also called characteristic path length. The shortest path between two vertices vs and vt is a path
that begins at the vertex vs and ends in the vertex vt and contains the least possible number of
vertices. The shortest path between two vertices is also called a geodesic. The longest geodesic
in the graph is called the diameter of the graph: this is the maximum number of steps that is
required between any two nodes. The average shortest path is the average of the length of the
geodesics between all pairs of vertices in the graph.

A practical impact of Milgram’s finding structures is as that possible models for social networks.
The two dimensional lattice model shown in Figure.

Fig.8 The 2D lattice model of networks (left). By connecting the nodes on the opposite
borders of the lattice we get a toroidal lattice (right).
Clustering for a single vertex can be measured by the actual number of the edges between
the neighbors of a vertex divided by the possible number of edges between the neighbors. When
taken the average over all vertices we get to the measure known as clustering coefficient. The
clustering coefficient of tree is zero, which is easy to see if we consider that there are no triangles
of edges (triads) in the graph. In a tree, it would never be the case that our friends are friends
with each other.
Fig.9 A tree is a connected graph where there are no loops and paths leading from a
vertex to itself.
The macro-structure of social networks
The image that emerges is one of dense clusters or social groups sparsely connected to each other
by a few ties as shown in Figure 1.7.d. For example, this is the image that appears if we
investigate the co-authorship networks of a scientific community. Bounded by limitations of
space and resources, scientists mostly co-operate with colleagues from the same institute.
Occasional exchanges and projects with researchers from abroad, however, create the kind of
shortcut ties that Watts explicitly incorporated within his model. These shortcuts make it possible
for scientists to reach each other in a relatively short number of steps.

Fig.10 Most real world networks show a structure where densely connected
subgroups are linked together by relatively few bridges

Clustering a graph into subgroups allows us to visualize the connectivity at a group level.
Core-Periphery (C/P) structure is one where nodes can be divided in two distinct subgroups:
nodes in the core are densely connected with each other and the nodes on the periphery, while
peripheral nodes are not connected with each other, only nodes
in the core (see Figure 1.7. e). The matrix form of a core
periphery structure is a

matrix

The result of the optimization is a classification of the

nodes as core or periphery and a measure of the error of
the solution.

Fig.11

The structural dimension of social capital refers to patterns of

relationships or positions that provide benefits in terms of
accessing large, important parts of the network.
Degree centrality equals the graph theoretic measure of
degree, i.e. the number of (incoming, outgoing or all) links of a
node.
Closeness centrality, which is obtained by calculating
the average (geodesic) distance of a node to all other nodes in
the network. In larger networks it makes sense to constrain the
size of the neighborhood in which to measure closeness
centrality. It makes little sense, for example, to talk about the
most central node on the level of a society. The resulting
measure is called local closeness centrality.
Two other measures of power and influence through networks are broker
positions and weak
ties.
Betweenness is defined as the proportion of paths — among the
geodesics between all pairs of nodes—that pass through a given
actor.
A structural hole occurs in the space that exists between closely clustered
communities.
Lastly, he proves that the structural holes measure correlates
with creativity by establishing a linear equation between the
network measure and the individual characteristics on one side
of the equation and creativity on the other side.