Baby steps towards the semantic network

In 2001, Sir Tim Berners-lee (inventor of the World Wide Web) presented his vision regarding the Semantic network. His objective was a 'net' which can analyze this information in a nearly-human manner by creating ontologisms that will use contexts and connections between all content existing throughout the web.

 

We, as human beings, are able to comprehend concepts/terms which bear various meanings which depend on their context. For example, referring to a large military arsenal or mentioning Arsenal winning the champions' league is an example two distinctly different representations of the word 'arsenal'. According to context, human beings are able to usually guess the meaning of a word presented in a sentence. This is true even in cases in which the word itself isn't recognized by the reader/listener.

 

These ontologisms can lead to a web that can learn meanings through many sources. For example "Britain's leading football teams are: Chelsea, Man. United and Arsenal".  By using automatic ontological tools that can decipher the sentence's context, the semantic web will realize that the word 'Arsenal' represents. Furthermore, terms of ‘victory’ and ‘score’ are linked to the world of soccer according to other sources and ontologisms. The net may now ‘comprehend’ the context of the sentence "I just saw Arsenal win 5:0".

 

 Are semantics already here?

Berners-lee, in an attempt to reach this goal, founded W3C: a body meant to syndicate the various technologies in the field in order to generate semantic connections for as much information on the web as possible. Furthermore, these connections are represented in languages optimized to the data (such as OWL, RDF, and XML).

However, the road to the semantic web is a long and winding one. The difficulties in actualizing this dream stem from the vast uncharted information on the web and different standardization methods for each knowledge platform. That said, even in our day and age we can view many developing semantic abilities:

For example, if I feed a time or date into my Smartphone, the device will suggest I save said date for an upcoming event. If I receive a message with the words Herzl 6 in it, my Smartphone will guess that this is an address and will suggest a suitable route to this location. Soon enough, based on context, Smartphones will be able to "recognize" whether this address is a restaurant and display the menu or suggest I book a table, or even suggest I listen to a song whose lyrics include these words.

 

Semantics in organizations

 

Challenges which act as an Achilles heel in the World Wide Web are reduced to a minimum when dealing with an organizational network. The ontologisms are based on a uniform technological and logical standard, are adapted to the various changes in the organization and are based on organizational language and contexts.

So, you might ask, how can I benefit from this as an organization?

The possibilities are endless. For example, you can increase and optimize collaborations; by linking different parties in your organization, the system will be able to display in which fields each individual can be of assistance. Furthermore, I will be able to view the level of connection I maintain with organization workers.

 

This connection will also be defined based on rank and position, so that when a functionary leaves or retires I am able to reach the relevant contact. A new worker will be able to view a map of connections relevant to the fields he/she requires and the level of relation necessary when contacting each functionary.

 

Another benefit of the semantic web is advanced learning and instruction abilities. As a new worker, I can receive any detail relevant to my job based on my predecessors and colleagues' data analysis. I can view a list of tasks required from a new worker on this job as well as a list of tools and authorizations required in order to fulfill said tasks.

 

 

 

How can one generate semantics?

  1. By using ontologisms for fields relevant to the organization. These can be found in several databases, such as:

Protégé Ontology – Library http://protegewiki.stanford.edu/wiki/Protege_Ontology_Library

FOAF- http://xmlns.com/foaf/0.1 /

SIOC - http://www.w3.org/Submission/sioc-spec/

  1. Automatic setup via software which can 'recognize' various elements such as:
    1. Context and connection: "Britain's leading football teams are…" "Social networks such as Facebook, Snapchat, and WhatsApp" etc.
  • Analyzing related words and synonyms "a white car drove down the street", "a white Mitsubishi drove down the street", "a white vehicle drove down the street".
  1. Utilizing social organizational social networks- how can we succeed without turning to social networks? We can, for example, encourage the use of tags (such as hashtags) and analyze common expressions and links uploaded by workers.
  2. Manually: the organization's content experts can define the required relations or (in more advanced scenarios) complete and audit the aforementioned tools.

 

In conclusion, the use of semantics is slowly but surely becoming an integral part of our routine and as such, we as organizations are gradually learning how to exploit it. We are on the path to an age in which machines understand each other and in turn human beings as well substantially better.

 

 

 
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