The Semantic Web

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Literature review

Semantic web

The Semantic Web is a new approach to using information online. The Semantic Web facilitates the storage of information on the Internet and on intranets in machine readable form that enables software agents to understand and process the information in very powerful ways. When the web was originally created, it was designed as a place where users could store their documents, link them to other documents, and ultimately present them so other web users could read them. The HyperText Markup Language (HTML) designed to create these web pages is infused down to the very name of the tags to present information in a way that is useful for the human reader. As HTML evolved, the added features all helped enhance the web author's ability to create better layouts and present pages that were even more comprehensible by human readers.

Since everything on the Semantic Web is identified by a URI, the notion of linking to files as it is done in hypertext does not translate. Instead, Semantic Web portals collect URIs of files on the Semantic Web, and allow users to interact with the RDF graph of the statements.

In the context of creating a Semantic Web Portal for terrorism, any user would have the ability to submit RDF or the URIs of documents with data.

The Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources; using a variety of syntax formats.

Semantic Web is an initiative sponsored by World Wide Web Consortium. A vision of Tim- Berners-Lee and supported by Defense Advanced Research Projects Agency (DARPA) through DARPA Agent Markup Language (DAML) and European Community through the Ontology Interferencing Language (OIL).

OWL is the official web-based language and built on Resource Description Framework Schema (RDF-S) and in turn built on XML and XML-schema

The Semantic Web is envisioned as the next generation of the web that will help address these problems. New languages - the Resource Description Framework (RDF) and Web Ontology Language (OWL) - support the creation of data models that reflect the knowledge contained in any web resource, be it a text page, media document, or a database. Unlike HTML, this knowledge is represented in a way that makes it easy for computers to understand and work with the data. It also allows for the integration of data from across the web into a single model.

How Semantic Web is created

Once the concept of "IntelligenceReport" has been created, we may want to say something about the report. For example, we may want to add a date on which the report was created. This leads into the next Semantic Web fundamental: the triple. A triple is how statements are made on the Semantic Web. As one would expect, triples have three parts: the subject, the predicate, and the object. The subject of the triple is the thing being described - the "IntelligenceReport" resource, in this case. The predicate is a descriptor of what is being described about the subject. In this example, the predicate will be the creation date. The predicate itself is named with a URI that indicates where that resource could be defined. The object of the triple is the value given to the predicate. The object can be a literal (like a string of text), or another resource. The triple is often represented as two nodes (representing the subject and the object) connected by an edge representing the predicate. Figure 1 shows the triple for stating that the "IntelligenceReport" has a creation date of January 1, 2006.

Transformation of Semantic Web to Ontology

Using ontologies, the portal can combine statements from multiple files into a single model. Among the implications, this means that users can select sets of statements that reflect their personal interests, even if no one else has had that specific focus. Mapping between concepts to connect items as equivalent also allows statements to be merged into a single model.

How Semantic Web Helps Terrorism

In the ear of WWW, The speed, ubiquity, and potential anonymity of Internet based media like e-mail, Web sites, and Internet forums, make them ideal communication channels for militant groups and terrorist organizations. As a result, terrorists groups and their followers have created a vast presence on the Internet. A recent report estimates that there are more than 50000 Web sites created and maintained by known international terrorist groups, including Al-Qaeda, the Iraqi insurgencies, and many homegrown terrorist cells in Europe. Many of these sites are produced in multiple languages and can be hidden within innocuous looking Web sites.

On a related issue, the Semantic Web could also be used to track terrorist codes. In telephone and email conversations terrorists frequently use simple code words to mask their plans. In one case a terror attack was called a wedding and when one of the speakers asked if the bride was ready he was actually asking about the status of the bomb the terrorists were building. On a Semantic Web portal analysts would could mark up suspicious statements and link them into the context in which they were used - time, place, and the identity and activities of the participants in the conversation.

The Semantic Web could be similarly helpful in tracking and analyzing financial transactions. For example, all the users of a suspect bank account could be noted and then compared for other connections. The Semantic Web can also be used to study patterns of use of stolen credit and ATM cards. A series of purchases of potential explosive components with stolen credit cards, for example, could indicate that an operation was being planned. Because the Semantic Web encodes data it can be an effective tool for sorting through masses of details.

With the semantic web serve as the starting point of information search, various analysis tools would have to be used to analyze the network.

Techniques of data collection

Web Mining

Web mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the World Wide Web. Web mining is further divided into Web Content Mining, Web Structure Mining, and Web Usage Mining. Below are the steps of web mining

Steps: 1. Collection

Eg.spiders/crawlers programs

2. Analysis and Visualization.

  1. SNA -a mathematical method for tracking terrorists to be explained further
  2. Content analysis- develop several detailed (terrorism-specific) coding schemes

Content categories include: recruiting, training, sharing ideology, communication, propaganda etc

Here are the links of some relevant software and programs:

  1. Web metrics analysis examines the technical sophistication, media richness, and web interactivity of extremist and terrorist web sites
  2. Sentiment and affect analysis - Sentiment (polarity: positive/ negative) affect (emotion: violence, racism, anger, etc.) allows us to: identify radical and violent sites; and examine how radical ideas become "infectious" based on their contents, and senders and their interactions

Advantages and limitations:

Advantages of web mining: sheer amount of material to be analyzed is so great that it can quickly overwhelm traditional methods of monitoring and surveillance. In comparison, web mining is more effective and efficient.


  1. Terrorism is not confined to one country and it has no borders; different languages may be used. So this is where multilingual web mining comes in.
  2. we also have to be sensitive to the privacy of individuals, so we need to develop techniques for privacy sensitive data sharing data mining and web mining.
  3. Such techniques are not advanced enough, we need talents and skills to further develop on these software and techniques.

Social network analysis

Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models, and applications that are expressed in terms of relational concepts or processes. Along with growing interest and increased use of network analysis has come a consensus about the central principles underlying the network perspective. In addition to the use of relational concepts, we note the following as being important:

  • Actors and their actions are viewed as interdependent rather than independent, autonomous units
  • Relational ties (linkages) between actors are channels for transfer or "flow" of resources (either material or nonmaterial)
  • Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action
  • Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors
  • The unit of analysis in network analysis is not the individual, but an entity consisting of a collection of individuals and the linkages among them. Network methods focus on dyads (two actors and their ties), triads (three actors and their ties) or larger systems (subgroups of individuals, or entire networks.
  • Social networks can be represented as GRAPHS or MATRICES.

    "Conventional" social science data consist of a rectangular array of measurements. The rows of the array are the cases, or subjects, or observations. The columns consist of scores (quantitative or qualitative) on attributes, or variables, or measures.

    We could look at this data structure the same way as with attribute data. By comparing rows of the array, we can see which actors are similar to which other actors in whom they choose. By looking at the columns, we can see who is similar to whom in terms of being chosen by others. These are useful ways to look at the data, because they help us to see which actors have similar positions in the network. This is the first major emphasis of network analysis: seeing how actors are located or "embedded" in the overall network.

    The major difference between conventional and network data is that conventional data focuses on actors and attributes; network data focus on actors and relations. The difference in emphasis is consequential for the choices that a researcher must make in deciding on research design, in conducting sampling, developing measurement, and handling the resulting data. It is not that the research tools used by network analysts are different from those of other social scientists (they mostly are not). But the special purposes and emphases of network research do call for some different considerations.