Semantic Web and Social Web: Similarities and Differences !

Though semantic web and relational ontology has become a theoretical discussion among many pioneers of world wide web, it is yet to receive wider traction among the industrial use cases of internet. Yet the concept remains widely discussed in the academic circles and the research groups of enterprises focusing on online data distribution and search engines. However, semantic web as a paradigm has some differences from a social web where we can collaborate better and more meaningful.

Semantic web need to go way beyond the constructs and ambitions of autonomic computing if it should become a gateway to web socialization. Internet  should evolve to be more open web where we can create meanings and relationships within first degree of separation itself. I believe it is still very much skewed by the way search engines, resource identifiers, memory allocation architectures, data storage, binary approximation algorithms etc.

Social web is a world where society weaves a web of information around their social relationships and social use cases. It stems from the real web of information. Semantic web will be a subset of social web in this sense. If semantic web is dealing with information enterprise, social web is dealing with collaboration circles.Machines can become self aware and understand what is the cognizance of information that they process. But in social web, socialization will be the first phase and then semantic relationships will emerge. web sociology will depend on the the social coefficient that exist in various degrees of separation and the order of 'connectedness' in each collaboration circles.

The business dynamics of web sociology will depend on the market dynamics of software engineering economy as well. We cannot think of semantic or social web isolating it from the market ecosystem or software economic premises. In terms of social activities, what all are happening in web?

• Collaboration
• Conversation
• Construction
• Deconstruction
• Inspired connectivity
• Virtual sensitivity
• Cognitive mind mapping
• Symbolic learning
• Forgetting and temporary memory
• Recognition
• De-cognition
• Identification
• Crowd behavior
• Media affinity
• Informed passivity
• Social solitude
• Virtual sentiments 

This list goes on. Web sociology will need to come up with new frameworks where all these activities need to be aligned in the sociological perspective. Thus semantic web can generated meaningful association between machines and men. Semantic web can definitely write and read relationships with human beings. Socialization is something beyond communication. It is always an attempt to represent the social self and personal self to a wider audience. Only social web can aggregate and spread more meaningfulness, relationships and cognition in the collaboration circles. 

Unwind the Mind : Data Dynamics of Natural Computers

Data Diary (5): Natural Computers

Information space continues to enrich my imagination to new manifolds. The recent news that a new start up named Aysadi has come up with a Topological approach to machine learning and Big Data analytics, is really really an interesting conjecture. It is said that DARPA, NSF and Stanford were involved in this research for a long time. I hope this will be a good trend where we will have an open approach to data science. This should be beyond the dependency on specific tools. 

This inspired me to go through some nuances of topological learning and created lots and lots of questions in my 'unstructured' mathematical understanding. The stress on various 'in variance' conditions in topological analysis makes me believe that we are far from the best approach. A comprehensive approach to a data problem should not be defining a boundary to its explorations and insights. Yet my comment remains largely naive as I am not an authority or trained in topology. 

Continuing from our previous post on information - cognition   conjecture, I have landed on a cyclical condition. With the advent and advance of cognitive computing and neuroscience, we are creating anew computing machines driven by human cognition. So we can state that cognition can control computation and therefore information too. On the other side of the coin, can information control cognition. In simple terms the answer is yes, a plain yes. If so, can we create a cyclical information - cognition cyclical machine ? This should be a machine where cognition initiates information processing and then information processing generates new re-cognition. 

When I try to rationalize this order, I believe this is happening in all our day to day lively transactions. Going on the same lines, how many machines can claim to do this natural computing cycle to maximum approximation to the real world. And what is the most effective model to observe the data flow in this cognition - information - re-cognition cycle. Knowledge ( Neural Signals, Thought Processes ) in ( Cognition ) - (Language, Semantics, Syntax) in ( Information ) - ( Semiotics, Visuals, Shapes, Numbers, Senses, Emotions ) in Re-cognition seems to be data dynamics. Natural computing demands more rigorous modelling for data dynamics. In pursuit of more natural thoughts ... 

+Gokul Alex 

PS: Content is Social. Social is Me

Data Diary #4

Information Machines and Metadata Strategy: Some early thoughts

How much of engineering is required for designing / evolving +information machines so that they can always differentiate between data and +metadata and further go beyond to create knowledge and insights out of it. While thinking of this question. I cam across the role played by Search Engines in this. No doubt, search engines are information machines. They learn the #semantic graphs of information relationships in a sea of indexes. Let me bring some metrics here. How will we measure the effectiveness of search engines as information machines. Do we need some axis to plot / visualize their effectiveness.

One of my favorite questions will be how much of data / information / relationships / indexes / semantic webs can be converted to working knowledge by Search engines? Rather can they do this task at all without the intervention of human interactions, at all ! 

Going by the same lines, my categorization / differential positioning of +metadata with respect to data will not be merely based on the relationship between the meaning and associative positioning of data nodes. It will be rather based on how one node of data connects the other node of data to a third node of data. So data and +metadata should always have more than one connecting dot between them. Thus when we use a search engine to find all the +metadata about a particular data node, it should find the meta data based on this semantic graph. 

+Gokul Alex 

@gokulgaze, PS: Views are my own and do not subscribe to any organization or institution