Data is highly valued. It comes in many formats that have complex structures and relationships. Specifically examining social network data and personal identity it is apparent that data connected to the user is frequently produced, describes an instance, and is a volatile representation of a user’s values, believes, and norms.
The semantic web and metadata are two methods of structuring and describing objects on the web. The objective of using these methods is to allow computers to interpret these objects more effectively by defining the relationship between one another. Exploring the semantic web and metadata association with a person’s profile on a social network, a user can have semantic description related to a resource in three ways; as the author or creator of the resource; as the user or consumer of a resource; and as a commentator or evaluator of a resource. Exercising any of these relationships, a person may create metadata about that resource. An author might give the resource a category, for example. The consumer’s viewership is recorded metadata, adding to the “hit count” for the resource, and/or an evaluator might click the “like” button and link it to other people in the network.
The possibilities to map out and provide structure to online objects and activities can define the relevance and measure a users association to that resource. David Ellis et al (2002) devise a model to map user’s information seeking behaviours. The study considered information retrieval using new technologies and the effects on cognitive behaviour, problem solving, user situational context, and uncertainty reduction. The results found that the nature of the search is driven by process and context. Successive searchers are one particular element of the study whereby a user performs different search sessions over time, evolving the search each time to narrow the results (p.698). The ability to create a relational network of data is an evolution of this model concerning information retrieval as it adds context through personal profiling and network ties. Complex networked relational data systems can filter information to be more accurate to the individual profile, suppressing the need for successive searching behaviour. Ching (1979) describes the dictionary method of searching as searchers that compare text with those stored in memory through string-to-string match. To create contextual information machines require the ability to understand syntax, grammar, vocabulary, concordance, pronunciation and other properties (p.164). Contextual searchers are more apparent than ever, through the use of social network. User described resources attach meaning to data, and through complex network algorithms can provide greater content retrieval on a relational and contextual model rather than the traditional dictionary method.